Innovative methodology for through process inclusion level forecasting of engineering steel (INCLUSION).
Date: 2013-12-03
Institutions: Directorate-General for Research and Innovation
Subjects: data collection, industrial project, industrial research, information analysis, iron and steel industry, iron and steel product, research and development, research project, research report, resistance of materials, steel

European Commission

Research Fund for Coal and Steel

Innovative methodology  
for through process inclusion level  
forecasting of engineering steel

(INCLUSION)

E. Balducci, M. Massazza
Cogne Acciai Speciali (CAS)

Via Paravera, 16, 11100 Aosta, ITALY

F. Cirilli
Centro Sviluppo Materiali (CSM)

Via di Castel Romano, 100/102, 00128 Roma, ITALY

R. Pizarro
Gerdau Sidenor

Barrio Ugarte, S/N, 48970 Basauri, SPAIN

N. Link
Betriebsforschungsinstitut VDEh-Institut für angewandte Forschung GmbH (BFI)

Sohnstrasse 65, 40237 Düsseldorf, GERMANY

Grant Agreement RFSR-CT-2008-00002 
1 July 2008 to 30 June 2011

Final report

Directorate-General for Research and Innovation

2013 

EUR 26175 EN

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Luxembourg: Publications Office of the European Union, 2013 

ISBN 978-92-79-33088-9 
doi:10.2777/41933 

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TABLE OF CONTENTS 

EXECUTIVE  summary ............................................................................................................5 

Scientific and technical description of the results....................................................................13 

Objectives of the project ..........................................................................................................13 

Comparison of initially planned activities and work accomplished ........................................13 

Description of activities and discussion...................................................................................15 

WP1:  Steel  sampling  activity  during  process  and  on  final  product  for  identification  of 
detrimental inclusions ..............................................................................................................15 

Task 1.1 Steel grades selection (Sidenor, CAS)...................................................................15 

Task 1.2 Steel sampling activity (Sidenor, CAS).................................................................16 

Task 1.3 Analysis of steel samples (Sidenor, CSM, CAS)...................................................18 

Conclusions of WP1 .............................................................................................................26 

WP2.  Application  of  statistical  methods  for  inclusion  population  characterization  (CSM, 
BFI) 

27 

Task 2.1: Definition of the protocol for extension of the ESIS procedure (CSM, BFI).......29 

Task 2.2:  Application of the extreme values statistics for the quantitative determination of 
inclusion population. ............................................................................................................29 

WP3: evolution of the inclusion scenario along the production route.....................................40 

Task 3.1 Modelling (BFI, CSM) ..........................................................................................41 

Task 3.2 assessment of the method (Sidenor, CSM, CAS) ..................................................51 

Task 3.2 Definition of correlations between inclusion quantities and features at the different 
process stages (Sidenor, CSM, CAS) ...................................................................................55 

Conclusions of WP3 .............................................................................................................58 

WP4: From inclusion analysis to intelligent manufacturing....................................................59 

3 
 
 
 
 
 
 
 
Task  4.  1  Effect  of  inclusion  size  of  the  quality  of  the  product  after  rolling  (Sidenor, 
CAS)  59 

Task 4.2 Definition of the protocol  (Sidenor, CSM) .......................................................67 

Task 4.3 Application to a real case (Sidenor, CSM, CAS) ...............................................68 

Conclusions of WP4 .............................................................................................................76 

WP 5 Final report.....................................................................................................................77 

Task 5.1  Discussion of results (All).................................................................................78 

References................................................................................................................................80 

Exploitation and impact of the research results .......................................................................80 

List of Tables ...........................................................................................................................81 

List of Figures ..........................................................................................................................83 

Annex 1 – Liquid steel sampling .............................................................................................86 

ANNEX 2 - EDX ANALYSES ON 100Cr6  (Sidenor steel grade) ........................................88 

-SECOND SAMPLING CAMPAIGN: LOLLIPOP (Liquid) BILLET AND PRODUCT. ....88 

Annex 3 - EVA analysis of CAS samples ...............................................................................94 

Annex 4 – Sample preparation from LF and tundish sampling .............................................108 

ANNEX 5: FATIGUE TESTST ON 100Cr6 GRADE..........................................................111 

ANNEX 6: VALIDATION FATIGUE TESTS ON 100CR6 (HEAT 54062, TASK 4.3.2).130 

4 
 
 
 
 
 
 
EXECUTIVE  SUMMARY 

Background 
Despite an immense number of projects about, inclusions still represent a problem, causing defects 
in products. The work performed in the last forty years gave a deep knowledge on inclusion genesis 
during steelmaking routes and control techniques. But a number of uncontrolled events can cause the 
presence of large inclusions or clusters in the slab that can be transformed in not acceptable defects 
in the rolled product. 
The idea of this project is to develop a method allowing foreseeing – in a relatively quick and cheap 
way - the probability of occurrence of not acceptable defects produced in given rolling operations, 
starting from information retrieved from analysis on cast billets. On the basis of this information the 
steelmaker can decide a different rolling procedure, changing the final destination of the product, in 
order to reduce the probability of downgrading the product. 

Inclusions present in the slab in continuous casting can generate defects after rolling. The formation 
of a defect depends on:  
• 

size  of  the  starting  inclusion  and  the  subsequent  size  of  the  elongated  inclusions  as  a 
consequence of rolling operation  
reduction  ratio  applied  in  the  rolling:  the  larger  is  the  reduction  ration,  the  larger  will  be  the 
length of the resulting inclusion 

• 

Basically, the formation of a defect can be prevented acting on: 
1.  reduction  of  inclusion  size  and  amount  in  the  steelmaking  route,  in  order  to  obtain  an  as  cast 

product with an inclusion scenario formed by a small size inclusion 

2.  adapting the rolling program (the reduction ratio) to the inclusion scenario; in other words, if the 
final  product  is  a  wire  rod,  a  billet  production  having  a  small  size  inclusion  scenario  can  be 
rolled  up  to  thin  diameter,  while  a  billet  production  with  larger  inclusion  scenario  are  rolled 
following a different program, with smaller reduction ratio. 

The  first  action  to  improve  product  quality  is  currently  defined  as  clean  steel  production,  which 
means the production of steel with low level of impurities. Referring to inclusions, steel cleanliness 
is  controlled  by  a  wide  range  of  operating  practices  throughout  the  steelmaking  processes.  These 
include the time and location of deoxidant and alloy additions, the extent and sequence of secondary 
metallurgy  treatments,  optimization  of  stirring  and  transfer  operations,  shrouding  systems,  tundish 
geometry  and  practices,  the  absorption  capacity  of  the  various  metallurgical  fluxes,  and  casting 
practices. 

The  second  approach  is  a  new  one,  and  it  is  the  objective  of  this  work.  This  approach  is  not 
alternative, but complementary to clean steel production. In fact, clean steel production is aimed at 
reducing the impurities and inclusions content during steelmaking route; with the application of this 
approach the objective is to optimize the rolling program in order to limit the rejection of the final 
product from customers. 

Being this project dedicated to the relationships between inclusions in the as cast slab and defect in 
the products after rolling, for the sake of clearness the following convention has been adopted:  

inclusion(s) refers to non metallic particles present in the as cast billet; 

• 
•  defect(s) refers to the non metallic – elongated – structures present in the rolled products. 

5 
 
 
 
 
 
 
 
 
 
In  the  industrial  world  the  latter  are  commonly  called  inclusions,  while  defect  is  a  not  acceptable 
inclusions.  In  this  project  to  follow  this  common  nomenclature  would  have  generate  a  lot  of 
confusion. Therefore the particles in the products are always called defect, with a further distinction 
in acceptable defects and not acceptable defects. 

Objective of the project 
The  objective  of  the  project  is  to  develop  tools  enabling  to  foresee  the  occurrence  of  large  and 
harmful  inclusions  in  the  billet  before  rolling,  which  in  turn  can  cause  the  formation  of  not 
acceptable defects on rolled products, and give indication, through models of inclusions elongations, 
about rolling procedure, in order to reduce the probability of formation of not acceptable defect. The 
application of these tools can be used for decision supporting on rolling procedure.  

Logical steps of the project 

Step 1. Definition of criteria of acceptability of the defect 
The first problem in the project is how to establish a procedure to define if a defect is acceptable or 
not. Once identified the procedure, this should be transformed in an algorithm to be implemented in 
a calculation tool. 

The approach used in this project for the definition of criteria was to consider the possibility to adopt 
the  “commonly  accepted  criteria”  for  quality  product  evaluation,  since  product  quality  and 
acceptability of defects are strictly related. 

Inclusion  ranking  methods,  like  the  Standard  Regulation  E45  or  the  new  standard  EN10247,  were 
not  considered  as  suitable  reference  for  defining  criteria  that  could  be  transferred  in  algorithms. 
These  methods  are  rapid  but,  together  with  the  measurements  of  defects  size,  visual  aspects  of 
defects are taken into account so that a certain arbitrariness is left to the operator.   
Moreover, in these methods the sizes of defects are not related to real performance of the material 
for  the  target  application.  Hence  in  many  cases,  when  a  specific  performance  is  required,  these 
methods have poor usefulness.  

The  attention  was  therefore  addressed  to  methods,  which  take  into  account  material  performance, 
based on the mechanical properties of the material in presence of a defect. 
In  particular  defect  size  affects  the  resistance  to  fatigue.  Hence  the  fatigue  limit  of  a  material, 
imposed by the presence of defects, can be used as criteria to establish the acceptability of a defect.  

In this approach the first problem is to define the fatigue limit for given products.  
The  fatigue  limit  can  be  a  requirement  imposed  by  the  customer  (in  general  it  is  different  from 
customer to customer).   
In case of 420C1 (CAS) fatigue limit does not enter directly into customer specifications of the steel; 
for  this  type  of  steel  the  acceptability  of  customers  is  based  on  the  defect  ranking  according  to 
International standard E45. To use in this case fatigue limit value it was decided to consider as limit 
of acceptability the fatigue limit measured on a heat which has a good ranking from E45 standard, 
and then to use this vale as reference.  
On the other hand, for the 100Cr6 product (Sidenor) the usual specification is set on the Fatigue Life 
at a fixed solicitation level through a minimum value of the  L10 index, that is, the minimum Fatigue 
Life for which it is expected that 10% of the specimens within a given population would fail. The L10 
index is calculated by means of the Weibull analysis of the fatigue life data of the population tested 
at a fixed set of conditions. Thus the comparison of the mechanical performance of the tested heats 
has been evaluated by comparison of such an index 

6 
 
 
 
   
 
 
 
 
 
 
In  order  to  correlate  the  results  of  fatigue  tests  with  defects  size  it  was  used  a  model  available  in 
literature:  the  Murakami-Endo  model.  This  model  calculates  the  fatigue  limit  of  the  material  from 
geometrical and physical data of defects, and from steel hardness (which represents the contribution 
of the steel microstructure).  
Comparison  with  measured  values  of  fatigue  limits  of  materials  and  related  analysis  of  defects 
demonstrated the reliability of the fatigue model. 

Step 2. Individuation of large – harmful – inclusions, precursor of not acceptable defects 
The maximum acceptable defect length, or calculation of fatigue limit as a function of defect size, 
can  be  used  to  decide  the  maximum  reduction  ratio  that  can  be  applied  without  producing  not 
acceptable defects, provided a method to foresee defect size in the rolled product from the inclusion 
size in the as cast steel is available.  

The first problem is to know the inclusion size before rolling. 

For  this  purpose,  a  first  issue  in  the  project  was:  at  which  point  of  the  steelmaking  cycle  have 
samples to be taken to analyze inclusions? 
In  the  original  plan  the  sampling  activity  had  a  first  section  devoted  to  sampling  steel  during 
steelmaking cycle (ladle and tundish). The objective of this activity was to individuate the genesis of 
macro 
inclusions  (precursor  of  macrodefects)  during  steelmaking  operations,  and  define 
countermeasures. After extensive sampling campaigns performed for two different steel grades (for 
the two industrial partners) followed by inclusions analysis, the results revealed that the production 
process was enough stable and reproducible (that is the inclusion scenario was always homogeneous 
from the chemistry point of view), and inclusion size was the only distinguishing factor.  

After  discussion  with  project  revisers  and  TGS  experts,  taking  into  account  their  suggestion,  the 
project was re-addressed to concentrate only on the inclusion size at the end of casting, to support 
the subsequent rolling operations. 
This choice implied advantages and drawbacks. 
On  one  side,  it  allows  to  capture  also  inclusions  formed  in  continuous  casting,  for  example 
inclusions formed from reoxidation phenomena in tundish, or cluster formed by oxide precipitation 
and  agglomeration  during  cooling;  moreover,  sampling  procedure  on  a  solid  product  makes  the 
analysis more reproducible and more robust.  
On  the  other  side,  the  sampling  of  a  billet  seems  difficult  to  apply  in  routine  operations,  although 
operations and logistic can be adapted to implement the practice as routine operation. This procedure 
should be limited to more critical cases, selected on the basis of an analysis cost/benefit. 

A major issue of the project concerned the significance of the inclusion analysis for the problem of 
occurrence of defects. 
Formation of a defect is related to the presence of large inclusions. Hence, it is necessary to know 
the distribution of dimensions of a statistically significant population. According to the conventional 
inclusion analysis techniques, this means that a large number of samples must be measured to have a 
representative picture of the inclusion population. 
This makes the analysis extremely time consuming and expensive, since the improved steel refining 
techniques  of  modern  steelmaking  plants,  led  to  a  drastic  reduction  of  the  amount  of  inclusions 
dispersed  in  metals.  Moreover,  conventional  approaches  of  detecting  average  dimensions  and 
number  of  particles  are  weak  for  an  accurate  classification  of  type  and  dimensions  of  inclusions 
population.  

7 
 
 
 
 
 
 
 
 
A  different  statistics  approach  has  been  used  in  this  project:  Extreme  Value  Analysis  (EVA) 
Statistic.  The  adopted  approach  allows  to  estimate  the  size  of  big  inclusions  using  a  limited, 
realistically practicable, number of analysis. 
EVA is based on statistical theories concerning distributions of data, specially dedicated to evaluate 
the  occurrence  of  rare  events.  The  technique  can  be  used    because  inclusions  typically  follow 
defined, not random, distribution.  
The  use  of  EVA  technique  permits  to  calculate  the  probability  of  occurrence  of  a  large  inclusion, 
even if this sized inclusion is not present in the analyzed sample.  
The following Figure reports (in the usual linearized form) the Extreme Values Distribution Function 
[3] calculated for inclusions measured in a sample taken from an as cast billet. Dot points represent 
the  measured  values.  In  the  Figure  are  graphically  indicated  the  average  value,  the  maximum 
measured size and the maximum expected size, calculated with EVA. As maximum expected size (in 
general of inclusion or defect) is intended the maximum size that would be found if the analysis had 
been carried out on a much larger steel volume. According to ASTM standard [2], which has been 
followed in this project, extrapolations are always referred to a volume 1000 times larger than the 
volume of the sample. 

In  order  to  have  a  linear  relationship  on  the  graph  it  is  reported  the  reduced  variate  “y”  of  the 
distribution function P [2,3,7,8]. 

estimated distribution

95% confidence lower bound

95% confidence upper bound

measured values

max measured

max expected

0

50

100

150

200

250

300

)
)

P

(
n
l
-
(
n
l
-
=
y

8

7

6

5

4

3

2

1

0

-1

-2

defect size, mm

Extreme  values  distribution  on  inclusion  on  as  cast  billet.  Average  value,  maximum  measured 
inclusion and maximum expected inclusion are also reported. 

The application of this statistical technique allows to derive, within a high probability, information 
that would need a much larger number of analyses with conventional technique. 

8 
 
 
 
 
 
 
 
 
 
Step 3. Identification of relationships between defect size, inclusion size and reduction ratio in 
rolling 
The  second  problem  of  the  project  is  how  to  calculate  defect  size,  once  established  a  method  to 
evaluate inclusion size. 

During  rolling,  inclusion  present  in  the  as  cast  billet  are  elongated  forming  the  defect.  A  major 
objective of the project is to correlate the size of the defects with the size of the inclusions, precursor 
of the defect.  
An  important  issue  is  that  different  types  of  inclusions  are  elongated  in  different  ways.  A  deep 
analysis  of  inclusion  population,  carried  out  in  the  project,  suggested  to  classify  the  inclusions  in 
three  types:  plastic  deformable  inclusions,  rigid  undeformable  inclusions  and  cluster  of  smaller 
particles. 
The analysis of the defects suggested that plastic deformable inclusions and cluster of small particles 
generate defects, while single particle can be neglected. 
Deterministic  models  of  the  deformation  of  plastic  inclusions  and  clusters  during  rolling  were 
developed. The models supply the evolution of defect size as a function of starting inclusion size and 
reduction ratio during rolling. 

A  second  approach  has  been  also  pursuit,  based  on  mathematical  correlations  between  inclusions 
size and defects size. The correlations have been defined using sophisticated techniques such as data 
mining techniques and neural network.  
The mathematical correlation constitutes, in turn, a model for predicting defect size from inclusion 
size.    Moreover,  being  based  on  a  large  number  of  inclusions  and  defects,  the  individuation  of 
mathematical correlation is a statistical validation of the deterministic model.   

Step 4. Validation of the methodology to individuate defects from inclusions 
The  methodology  to  evaluate  the  effect  of  rolling  on  defects  formation  and  consequent  effect  on 
material performance is a sequential application of the tools developed in the project steps 1-3: 
•  Analysis of inclusion on as cast billet according to extreme values statistic, in order to obtain 
the average inclusion size, measured maximum inclusion size and expected maximum inclusion 
size; 

•  Calculation of the final defect size according to rolling programs with the deformation model or 

the statistical model; 

•  Direct evaluation of the acceptability of the defect length produced by the expected inclusions 
and/or calculation of the fatigue limit for the calculated defect and comparison with the limits 
of acceptability of the product. 

A  first  assessment  of  the  methodology  has  been  performed  by  using  the  deterministic  model  to 
calculate  defects  lengths  from  inclusion  sizes  in  billets  samples  from  industrial  partners  and  by 
comparing the calculation results with defects actually found in the rolled products. 

The  following  Figures  report  examples  of  inclusion  elongation  calculated  for  CAS  and  Sidenor 
steels. The starting inclusion sizes have been evaluated with the Extreme Value statistics from data 
from billets. The deterministic model of inclusion elongations has been used to calculate the lengths 
of the defects generated from those inclusions with the adopted reduction ratio in rolling. The results 
of  these  calculations  have  been  compared  with  defects  lengths  measured  in  rolled  products  and 
resulted to be in agreement with experimental data. 

9 
 
 
 
 
 
 
 
   
 
CAS 

Sidenor 

Following  this  first  comparison,  the  validation  has  been  reinforced  by  applying  the  defined 
mathematical  correlations among the measured inclusion size, in as cast billet, and defect lengths in 
rolled products. 
Measurement of inclusions sizes in billet and bar (Sidenor) and defect lengths in wire rods  (CAS) 
permitted to define a correlation between those two parameters. 
This correlation is in agreement with the prediction of the deterministic model. 

Results of the industrial application 
Industrial  trials  carried  out  by  Sidenor  confirmed  the  expected  correlations  among  inclusions 
determined in as cast product and fatigue measurement on rolled products. 

From EVA statistics, inclusions of expected size of 611 (cid:31)m were determined on product bars having 
60 mm of diameter. Deterministic deformation model was used to calculate the expected defect size. 
Then,  the  fatigue  model  was  used  to  calculate  the  corresponding  fatigue  on  the  product.  The 
measured fatigue resulted in agreement with calculation.   

In industrial trials CAS, the EVA statistics was used the determine maximum inclusion size on billet 
slice and the deformation model was used to calculate defect length.  

As an example, the inclusion analysis on the billet gave the following results for plastic inclusions: 

Measured average size  
Maximum measured size 
Maximum expected size (EVA) 

17 
38 
66 

μm  
μm 
μm  

The  deformation  model  was  used  to  predict  the  defect  size  for  two  final  diameter  of  the  rolled 
product. The following defects size, as a function of wire rod diameter were calculated: 

20 mm wire row (μm) 
200 long.  

5.5 mm wire rod(cid:31)μm) 
940 long. 

The obtained analysis in rolled products confirmed the predictions. 
The  following  figure  shows  statistical  distribution  of  defects  in  the  two  rolling  products  and 
microscopic analysis of defect examples. 

10 
 
 
 
 
 
 
 
 
 
 
 
4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

0

50

100

150

200

4

3

2

1

0

)
)

P
(
n
l
-
(
n
l
-
=
y

-1

-2

0

200

400

600

800

1000

1200

defect size, μm

defect size, μm

20 mm wire rod.  
Measured  defect  length  up  to  150  μm  and 
maximum expected defect 210 μm (from EVA).  
Microscopic picture represent maximum measured 
defect 

5,5 mm wiew rod.  
Measured  defect  length  up  to  600  μm  and 
maximum expected defect 960 μm (from EVA).  
 Microscopic 
measured defect 

represent  maximum 

picture 

In a practical application, the above result would suggest to avoid rolling up to smaller diameters. 

The industrial trials in both Sidenor and CAS demonstrated that this tool is affidable to predict defect 
size and fatigue limit, hence it is useful to give indications about rolling operations. This tool can be 
used also to evaluate the advantages of improving the inclusion scenario on the billet on the as cast 
product. 

Conclusions 
A  method  to  predict  the  defects  size  from    inclusion  analysis  has  been  developed  and  tested  in 
industrial trials, based on the following procedure.  

1.  Sampling of steel from as cast billet (or from tundish) to define the starting inclusion size.  

Sampling from as cast billet implies, of course, that the time interval between casting and rolling 
is long enough (10 hours at the least) to follow the whole procedure. This is the case of CAS and 
Sidenor.  
Alternatively,  samples  may be  collected  from  tundish.  This  has  the  advantage  of  fast  sampling 
but in some cases, information may be lost about  inclusions formed during continuous casting. 
According  to  the  results  obtained  in  this  project  the  distribution  functions  of  inclusion  size  in 
tundish and as cast billet overlap.  
The procedure for sample cutting and preparation is described in Annex 3.  

2.  Inclusion analysis according to Extreme Values Statistic as reported by ASTM E 2283.  

The results of the project demonstrated the application of the technique is indispensable to obtain 
information of the inclusion size not available from standard inclusion analysis techniques. 

11 
 
 
 
 
 
 
 
 
 
 
3.  Application of  deformation model of inclusions to calculate inclusion elongation during rolling 
starting  from  maximum  expected  inclusion  on  as  cast  billet.  Both  deterministic  model  and 
mathematical correlation have been applied in this project. 

4.  Definition of rolling sequence to obtain products fulfilling customers specifications 

The tool is able to give indications to rolling operations. 
The  activities  of  this  project  constitute  the  basis  for  a  new  approach  of  inclusion  analysis  and 
correlation of inclusion population and properties of final product. 

12 
 
 
 
 
  
 
 
SCIENTIFIC AND TECHNICAL DESCRIPTION OF THE RESULTS  

OBJECTIVES OF THE PROJECT  

The project objective is to develop a tool able to forecast the occurrence and geometry of large and 
harmful inclusions on rolled samples. This tool is composed by the following parts. 

• Statistical  inclusion  analysis  carried  out  on  as  cast  billet  according  to  “Extreme  Values 

Analysis (EVA) statistics” 

• Inclusion deformation models. These models take as input the inclusion size expected at the 
end of casting (samples taken from billet or blums), determined using EVA, and the rolling 
parameters,  and calculate the inclusion elongation with rolling up to the final defect.  

• Correlation of defect size with product properties: the fatigue limit has been assumed as the 

product property which can be directly correlated with inclusions .  

Being this project dedicated to the relationships between inclusions in the as cast slab and defect in 
the products after rolling, for the sake of clearness the following convention has been adopted:  

• 
• 

inclusion(s) refers to non metallic particles present in the as cast billet; 
defect(s) refers to the non metallic – elongated – structures present in the rolled products. 

In  the  industrial  world  the  latter  are  commonly  called  inclusions,  while  defect  is  a  not  acceptable 
inclusions.  In  this  project  to  follow  this  common  nomenclature  would  have  generate  a  lot  of 
confusion. Therefore the particles in the products are always called defect, with a further distinction 
in acceptable defects and not acceptable defects. 

COMPARISON OF INITIALLY PLANNED ACTIVITIES AND WORK ACCOMPLISHED  

The  original  plan  included  steel  samples  collected  during  steelmaking  process  (AOD,  LF,  tundish). 
The  inclusion  types  and  size  distribution  collected  during  process,  in  the  original  technical  annex 
would be used to foresee the defect size distribution on rolled products, taking into account the rolling 
program. After the discussion of the Mid Term report the commission asked to the partners to revise 
the technical annex, according to the following issues, also reported in the accompanying letter of the 
new technical annex: 

- analyzing samples from liquid steel production (AOD, LF, tundish…) leads to decision not to further 
work on this field and to focus on as cast and rolled products; 

-  additionally  consortium  got  the  information  that  inside  of  another  ongoing  project  the  inclusion 
behavior  in  the  liquid  phase  and  the  thermal,  chemical  and  metallurgical  relations  to  inclusion 
development and transformation will be investigated in detail  (or shorter); 

-  only  in  solid  phase  inclusions  are  not  influenced  by  chemical  evolution,  therefore  to  set  the  first 
solid product as the initial starting point; 

13 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-  predict  final  inclusion  geometry/elongation  (deterministic  model  analyzing  the  evolution  of 
inclusions  during  mechanical  transformation)  and  predict  behavior  of  inclusion  populations on  final 
product applying data-based techniques to the related distributions; 

- to analyse in detail inclusion evolution considering the full path from liquid to solid. Focusing on 
solid production line will allow to reach proposed objectives; 

- to develop applicable models it's more realistic to withdraw the analysis of liquid phase  

The revised technical Annex has been submitted to the Commission and officially approved. 

According  to  the  new  technical  annex,  the  size  of  the  defect  on  the  rolled  product  is  calculated 
according to the inclusion type and size found on as cast product. 

In this way, considering the as cast product as the starting point has allowed to withdraw the chemical 
evolution  of  the  inclusions  during  casting  &  solidification,  and  this  has  allowed  to  build  a  tool  to 
describe the physical evolution of the inclusions during rolling. Even though, difficulties appear when 
trying  to  obtain  representative  inclusion  population  data  from  as  cast  big  product  sections  (as  from 
billet or bloom) on steels with a low inclusions content (as bearing grades). So, although the method 
has  been  shown  to  be  feasible,  experimental  constrains  for  its  applicability  may  come  from  the 
difficulty to get the as cast inclusion population. Extreme values statistic seems the most reasonable 
technique  to  analysis  steel  samples,  being  used  to  foresee  rare  events  (as  large  inclusion  in  a  clean 
steel). 
All  the  planned  activities,  according  to  the  new  technical  annex  have  been  carried  out  and  the 
technical objectives of the project, as redesigned after mid term report have been fulfilled.  

Regarding  the  ambitious  objectives  of  the  first  version  of  the  Technical  Annex,  have  been  re-
designed. Performed activities (steel sampling, analysis and statistical elaboration of data) at mid-term 
indicated that the correlations among inclusions, calculated defect size and product properties existis 
but  are  not  enough  reliable  to  build  a  control  system  with  it.  Further  carried  out  activities  in  the 
second  part  of  the  project,  confirmed  this  fact.  So,  as  matter  of  fact,  the  suggestion  coming  from 
global results is that investigations solely based on correlation have sufficiently proven that it could 
not  work.  The  final  industrial  trials  demonstrated  that  this  tool  is  useful  to  give  indications  about 
rolling operations, but it is not enough reliable to be used as control system. This tool can be used also 
to evaluate the advantages of improving of the inclusion scenario on the billet on the as cast product.   

14 
 
 
 
 
 
 
 
DESCRIPTION OF ACTIVITIES AND DISCUSSION 

WP1: STEEL SAMPLING ACTIVITY DURING PROCESS AND ON FINAL PRODUCT 
FOR IDENTIFICATION OF DETRIMENTAL INCLUSIONS  

The objectives of WP1 are: 

•  to  define  the  evolution  of  inclusion  scenario  (composition  and  shape)  during  steelmaking 
route  for  selected  steel  grades  and  to  assess  the  stability  and  reproducibility  of  inclusion 
scenario; 

•  to  provide  a  set  of  data  necessary  for  the  development  and  calibration  of  inclusion 

deformation models; 

•  to identify the inclusion family (or inclusions families)  

Steel  samples  have  been  collected  by  Cogne  Acciai  Speciali  (CAS)  and  Sidenor.  The  two 
steelmakers produce different steel grades, (CAS produces stainless and special steel, while Sidenor 
produces  mainly  carbon  special  steels),  with  different  steelmaking  route  and  with  different 
continuous casting and rolling operations. For this reason two parallel sampling activities have been 
carried out. Data from both steelmakers have been used in next WPs. 

Due  to  the  large  variety  of  steel  grades  produced,  each  steelmaker  selected  representative  steel 
grades, on which to focus both sampling and characterization activity. The selection criteria were: 

- 

steel  grades  produced  intensively,  in  order  to  perform  a  well  random  distributed  sampling 
campaign.  

-  different  final  rolled  products,  in  order  to  have  the  possibility  to  compare  the  evolution  of 

inclusion scenario with different rolling programs.  

In  the  first  half  of  the  project  samples  were  collected  during  steelmaking  cycle  (AOD,  ladle  and 
tundish), and rolled products. Then, taking into account the suggestion of the Project Revisors after 
Mid  Term  Report,  project  was  focused  only  on  inclusions  on  as  cast  billet  and  subsequent  rolled 
products.  Moreover  the  results  of  the  analysis  carried  out  in  this  task  confirmed  the  stability  and 
reproducibility of the evolution of the chemistry of inclusion scenario.  
The  results  of  sampling  activities  have  been  presented  in  the  Mid  Term  Report.  In  what  follows 
below a summary of the characterization performed is reported just for completeness. 

TASK 1.1 STEEL GRADES SELECTION (SIDENOR, CAS) 

CAS steel grades selection 

The following steel grades have been identified for this WP activities: 

martensitic  stainless  steel  (420C1); produced  from  continuous  casting  160 x  160  mm  billet, 

1) 
and manufactured as  wire rod 5.50÷13 mm (rolling). 
2) 
billets and manufactured after forging into bars   30÷55 mm.  

duplex stainless steel for tubes (329 A1), produced from continuous casting 160 x 160  mm 

After revision of the technical Annex it has been decided to focus all modelling activities on steel 
420 C1. The characterization activities of steel 329A1 were reported in the mid term report. 

15 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 1 shows the typical chemical composition of the steel 420C1. 

C 
0.45 

S 
0.0010 

P 
0.019 

Si 
0.41 

Mn 
0.48 

Cr 
13.37 

Ni 
0.19 

Table 1  

Chemical composition (%) of 420C1  martensitic stainless steel 

Steel  420  C1  is  used  for  surgical  and  dental  instruments,  spring,  molds  for  plastics,  wear  resistant 
components, pump components, parts in control devices, diesel engine pumps and for manufacturing 
of hand tools for woodworking.  
Steel cleanliness and mechanical properties requests (strongly related to inclusion content) should be 
fulfilled to satisfy the technical specifications. 

SIDENOR steel grade selection 

Also Sidenor, in the first phase of the activity performed liquid sampling for different steel process 
steps. 
After the Mid Term report sampling and analysis was focused on the different rolling steps (initial as 
cast  section,  intermediate  diameters  and  final  product)  to  characterise  the  inclusion  morphological 
evolution along the different rolling routes 

Sidenor  selected  the  bearing  steel  grade  100Cr6  (Table  2)  for  the  sampling  and  characterization 
activities,  as  the  inclusion  content  strongly  affects  its  in-service  behaviour  regarding  fatigue 
resistance. 
The main interest is focused in determining the influence of the different rolling routes to different 
final product diameters on the inclusion scenario, and from this, on the fatigue behaviour. 

C 

Mn 

Si 

P 

S 

Cr 

Ni 

Mo 

Cu 

Al 

Ti 

0.96  0.35  0.29  0.02  0.003  1.46 0.2 0.049 0.19 

0.012 0.003 

O 
(ppm) 
8 

N 
(ppm) 
64 

Table 2 

 Typical  chemical  composition  (%)  of  the  100Cr6  heat  sampled  (from  heat  N. 
47484). 

TASK 1.2 STEEL SAMPLING ACTIVITY (SIDENOR, CAS) 

Both CAS and Sidenor collected steel samples from several heats, within different steelmaking route 
steps. Being the processes different, different sampling strategies were adopted. 

CAS sampling activity 

Four heats have been sampled, according to the following sampling scheme: 

1. 
2. 
3. 

during AOD treatment, after reduction step, to characterize the starting situation,  
during AOD treatment, after desulphurization, 
at the end of LF treatment, to follow the evolution of inclusion scenario, 

16 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4. 

5. 
6. 

in  steel  after  ladle  treatment,  immediately  before  being  poured  into  tundish  (it  will  be 
indicated  as  ‘tundish’  hereinafter),  during  casting  to  characterize  the  inclusion  scenario  just 
before continuous casting, 
as cast billets; the first billet, middle casting and final billet have been sampled, 
rolled products. 

Table  3  reports  all  the  collected  samples  and  the  performed  analysis.  In  order  to  give  a  complete 
view of the carried out activities, in this table are reported also the analysis performed by CSM and 
described in next section.  

samples 

AOD reduction phase (A) 
ADO  desulfurization  phase 
(B) 
end of LF (C) 
Argon 
condition” (D) 

treatment  “Tundish 

as  cast  billets  (first  billet, 
mid casting, end of casting) 

sample 
1 spemis 

1 spemis 

2 spemis 

2 spemis 

18 slices 

rolled billet 

72 samples 

420 C1 

analysis 

OM + SEM + AIA* 

OM + SEM+ AIA* 

OM + SEM+ AIA* 

OM + SEM+ AIA 

OM  +  SEM  +  AIA*  (12  samples  in 
symmetrical  position  prepared  from  3 
slices) 

 OM+SEM+AIA* :  
13mm (12 samples) 
8 mm  (48 samples) 
5,5 mm (12 samples) 

* AIA: automatic image analysis 

Table 3 

List of  collected steel samples by CAS 

Sidenor sampling activities  

In order to assure the material availability to be used for the subsequent tasks, the sampling activity at 
Sidenor has been divided in two sampling strategies: 

1.  First sampling Campaign: stepped sampling of a single heat, getting samples at every process 

step 

2.  Additional sampling Campaigns: extensive sampling of many heats, but reducing the number 

of process steps sampled (as cast material and rolled bars) 

A first stepped sampling has been performed at every manufacturing stage corresponding to the hot 
rolling of one bloom rolled into two billets, and from this into two different bar products: 60mm and 
100  mm  in  diameter.  The  main  difference  between  both  products  consists  on  the  number  of  rolling 
steps  and  the  reduction  per  step  (detailed  later  in  Task  3.2).  Considering  the  185  mm  billet  as  the 
rolling process starting material: 

- D100 mm: 10 mill passes, total reduction 77% 
- D60 mm: 16 mill passes, total reduction 92% 

17 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
In  order  to  dispose  of  enough  material  as  to  characterise  the  mechanical  properties  (Fatigue)  of  the 
products, a provision of bars from the two different diameters was done (see Table 4). These materials 
were subsequently used for fatigue testing in WP4. 

Sample type 

Step 

N of Samples 

Obs. 

Liquid steel 
(lollipop) 

Solid samples 
(Semi-product 
and final bar 
products) 

EAF 
Ladle 1 
Ladle 2 
Tundish 
Bloom 
Billet 
Intermediate 
Round 100 mm 
Round 60 mm * 

1 
1 
4 
8 
2 (300x400 mm x 300 mm long) 
2 (185x185 mm) 
1 (125 mm sq x 300 mm long) 
> 10 (500 mm long) 
> 10 (500 mm long) 

End of fusion 
Before vacuum 
After vacuum 
Mid-cast. 
As cast 
Intermediate rolling 
After first rolling step 
Globular annealing 
Globular annealing 

Table 4 

Details of the first sampling campaign (stepped) at Sidenor. Heat Number 47484 
Bloom casted.  

The  extensive  sampling  was  programmed  (Table  5)  to  obtain  a  high  number  of  sampled  heats 
providing  data  from  a  significant  number  of  heats,  instead  of  a  high  number  of  samples  from  each 
heat (“stepped” sampling). 

Sample type 

Liquid steel 
(lollipop) 

Solid samples 
(Semi-product and 
final bar products) 

Step 

EAF 

Ladle 

Tundish 
Bloom 
Billet 

N of Samples 

None 

None 

4 per heat 
Not for these heats 
1 per heat (300 mm long) 

Intermediate section 

No 

Bar Product 

≥3 per heat (300-500 mm 
length) 

Obs. 
Skipped for the 
extensive sampling 
Skipped for the 
extensive sampling 
Mid-cast. 
185 mm Billet Cast 
As cast material 
Skipped for the 
extensive sampling 
Dependent on 
production program 

Table 5  

Second sampling campaign at Sidenor (Steel grade: 100Cr6).  

TASK 1.3 ANALYSIS OF STEEL SAMPLES (SIDENOR, CSM, CAS) 

The following analysis were carried out: 

•  SEM characterisation activity to determine the chemical composition of the inclusion, and to 

• 

have a first rough idea of inclusion size  
automatic image analysis (AIA) by scanning electron microscope and optical microscope to 
measure inclusion size distribution and inclusion geometrical features. 

18 
 
 
 
 
 
 
 
 
 
 
 
 
1.3.1 Analysis of samples from CAS 

The  microscopic  investigation  of  steel  samples  of  martensitic  steel  revealed  the  presence  of  liquid 
inclusions  of  Si/Mn/Cr  in  the  reduction  phase  of  the  AOD  converter.  Inclusion  scenario  changes 
during desulphurisation with presence of calcium silicates, with small amount of alumina, magnesia 
and minor amount of chromium oxide. During further LF treatments inclusion composition remains 
stable as calcium silicate with variable amount of alumina and calcia both in the range 5-8 %.  
In  the  tundish,  inclusions  are  composed  by  calcium  silicate  and  small  amount  and  alumina  and 
magnesia (around 3-4%). Figure 1 shows examples of inclusions found in steel samples, in the AOD 
in the reduction phase, in the ladle and in tundish. 

 AOD  
reduction 
phase 

Ladle 

Tundish 

billet 

Figure 1 

Evolution of inclusion composition for CAS martensitic steel grade 420 C1, from 
AOD to billet. 

This  evolution  of  inclusion  composition  has  been  confirmed  in  all  the  sampled  heats.  No  source  of 
exogenous  inclusions  has  been  found  during  LF  cycle  and  continuous  casting.  Figure  2  reports  the 

19 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SEM photographs of the defects on rolled steel samples up to a final diameter of 13, 8 and 5,5 mm. In 
the figure it is also reported the typical EDS spectra with chemical analysis. Defects are composed by 
calcium  allumino  silicates.  No  other  chemical  ‘families’  have  been  found,  which  means  that  these 
defects  derive  from  inclusions  found  in  liquid  samples  and  no  significant  modification  of  inclusion 
scenario occurs during continuous casting. 

Wire rod diameter 13 mm 

Wire rod diameter 13 mm 8 mm  Wire rod diameter 13 mm 5.5 

mm 

Figure 2 

Examples of inclusion found on rolled products of CAS steel 420 C1.  

Figure 3 reports, as an example, the size distribution curves1  of as cast inclusion billet and defects on 
rolled products respectively of φ 13, 8 and 5.5 mm. 

1 Size distribution curves are obtained by Automatic Image Analysis (AIA) carried out with scanning electron 
microscope. This technique permits to measure several inclusions geometrical parameters. 
Steel sample is ideally divided into smaller fields. SEM takes a digital image of each field. The size of the field 
(and  the  SEM  magnification)  depends  on  the  average  size  of  the  particles  observed.  In  this  analysis  a 
magnification of 500x, is used.  The digital image is converted into a  binary image (black and white), by Image 
Analysis software. Then the inclusions found within the images are automatically counted and the geometrical 
parameters (area, perimeter etc..) measured. In this analysis, inclusions smaller than 50μm2 (8 μm diameter) are 
not counted. EDS analysis is also performed on the same particles automatically. This permits to verify that the 
inclusion  scenario  is homogeneous from  the  chemical  point of view. In  this  activity  the  following parameters 
have been selected and used: area, maximum length and the shape factor. From the operative point of view, 
this  analysis  can  be  carried  out  both  by  optical  and  electronic  microscope.  As  main  advantage  to  use  the 
electron microscope, EDS analysis can also be performed. This allows first, to filter all the collected data, by 
removing  from  the  analysis  output  all  the  particles  which  are  not  ‘inclusions’  (as  ‘impurities’  deriving  from 
sample  polishing),  but  mainly  to  identify  clearly  the  presence  of  different  inclusions  ‘families’.  As  inclusion 
size,  the  maximum  length  has  been  assumed  as  reference  parameter.  Obviously,  in  case  of  liquid  samples  it 
coincides with its diameter. In case of elongated inclusions (see for example Figure 3), coincides with inclusion 
length. 

20 
 
 
 
 
 
 
 
 
 
                                                      
)

%

(
q
e
r
f

100.00

90.00

80.00

70.00

60.00

50.00

40.00

30.00

20.00

10.00

0.00

Billet 
freq billet

Diam  
freq 13
13 mm 

Diam 
freq 8
8 mm 

Diam  
freq 5.5
5.5 mm 

0.00

50.00

100.00

150.00

200.00

250.00

300.00

size (m icron)

Figure 3 

Size  distribution  curves  of  billet  and  rolled  samples  of  diameter  13,  8,  5.5  mm, 
(CAS steel 420 C1) 

Table  6  reports  the  average  inclusion  and  defect  size,  calculated  for  all  the  samples  along  the 
production route up to rolling. In case of round shape inclusions (AOD, LF, tundish and as cast billet) 
size means inclusions diameter, while in rolled samples size means defect length. 

average 
size 

Table 6 

AOD 
red 

AOD 
des 

LF 

TUND. 

as cast 
billet 

13 mm 

8 mm 

5.5 mm 

18 

20 

31 

18 

16.70 

64.10 

83.30 

108.60 

Average  inclusion  size  in  along  steel  production  route,  up  to  rolling,  for  steel 
420C/1. 

The  microchemical  analysis  revealed  the  presence  of  silicates  in  AOD,  which  evolve  into  calcium 
silicates  and  calcium  silico-aluminates  at  the  end  of  production  route,  in  the  tundish.  The  same 
chemical type has been found in as cast billet and rolled products. 
The examined production route is stable and reproducible. 
As  expected,  the  inclusion  size  distribution  is  shifted  towards  smaller  sizes  during  process  up  to 
tundish  casting.  In  LF,  the  presence  of  larger  inclusions  respect  to  those  found  in  AOD  is  a 
consequence of alloying operations and inclusion agglomeration. 

1.3.2 Analysis of samples from SIDENOR (Bearing steel: 100Cr6) 

In  the  following  the  analysis  is  split  in  two  parts,  corresponding  to  the  two  sampling  campaigns 
performed: 

• 

Inclusion  evolution  along  the  rolling  path:  characterisation  of  the  inclusion  type  and 
population on samples from the first sampling campaign (“stepped sampling” of a single heat 

21 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
including  intermediate  rolling  samples).  Two  different  product  diameters  from  a  common 
initial billet size are analyzed. 
comparison of inclusion in as cast and defects in final products (“Extensive sampling”). 

• 

Regarding the steel samples taken during steelmaking process, EDX analysis has been performed on 
lollypops from: 
- secondary metallurgy in the Ladle Furnace, after Vacuum operation 
- tundish (at mid-cast). 
Analyses  show  that  inclusions  are  mainly  Spinel  type  (Al-Mg-O).  After  vacuum  operations 
contamination from the slag may occur, due to slag emulsification  (inclusion - a - in Figure 4). Such 
inclusions floated during process and are not present in solid products. Also in tundish,  contamination 
from lining may occur (inclusion - b - in Figure 4) increasing the presence of silica in the inclusion 
analysis. This inclusion composition was not found significantly in product samples.  
For this reason all activities were focused on solid alumina  and alumina/spinel inclusions or partially 
solid alumina with maximum 20% of calcia, according to the analysis reported in Tables 12 and 13, 
were several inclusions in bloom and tubes are analysed. 

Ladle (After Vacuum)

O 

Mg 

Al 

Si 

Ca 

Total 

S 1 

50.36 

3.18 

13.91 

3.52 

29.03 

100.00 

Tundish (Mid-cast)

O 
56.29 
56.98 

Mg 
1.07 
3.21 

S 1 
S 2 

Al 
15.64 
18.30 

Si 
17.33 
13.60 

Ca 
3.21 
1.97 

Mn 
2.86 
3.17 

(a) 

(b) 

Figure 4 

Inclusions in lollypops from (a) Ladle (after vacuum degassing) and (b) tundish 

22 
 
 
 
 
 
 
 
 
 
 
(mid-cast) 

Solid  samples  from  bloom,  intermediate  rolling  (of  120  mm  diameter)  and  rolled  bar  of  60  mm 
diameter have ben collected and analysed. Ten oxide inclusions of different sizes have been analysed. 
Tables 7, 8 and 9 include the analysis from as cast bloom, intermediate section and final product. 

Length 
(µm) 
1,4 
2,6 
2,7 
2,8 
3,7 
3,8 
3,9 
4,1 
6,2 
7,5 

O 

Mg 

Al 

Si 

S 

K 

Ca  Mn 

Total 

52,30  10,40  37,30 
53,19  16,60  30,21 
4,87 
39,95  37,21 
54,67 
34,05 
8,55 
52,24  16,10  24,94 
51,21  13,10  32,24 
52,81  11,98  31,26 
7,95 
42,37  14,20 
21,28 
9,77 
47,70 
23,45 
9,72 
48,72 

7,81 

2,75 

1,93 
12,68 
6,67 
5,65 

1,78 
1,18 
1,02 

1,95 
2,26 
1,25 

0,95 
0,78 
2,43 
2,02 
3,52 
4,27 
2,17 

0,85 
2,72 
1,08 

2,01 

100,00 
100,00 
10,16  100,00 
100,00 
100,00 
100,00 
100,00 
16,48  100,00 
100,00 
5,33 
100,00 
7,96 

Table 7 

EDX  analysis  of  then  inclusions  in  the  BLOOM  section  (Transversal).  Heat 
47484. 

Length 
(µm) 
2,1 
3,8 
3,9 
4,4 
7,9 
8,4 
8,8 
15,1 
17,2 
22,0 

O 

Mg 

Al 

Si 

S 

Ca  Mn 

Total 

52,40 
39,20 
8,40 
52,79  18,10  29,11 
24,87 
7,21 
49,95 
54,67 
34,05 
8,55 
52,24  15,82  31,94 
51,21  13,10  32,24 
52,81  11,93  35,26 
52,70  13,77  33,53 
48,37  10,20  27,92 
51,31  13,37  35,32 

7,81 

1,78 

1,02 

0,95 

2,43 

0,90 

5,37 

1,11 

100,00 
100,00 
10,16  100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 

6,13 

Table 8 

EDX  analysis  of  then  defects in  the  INTERMEDIATE  ROLLING  120  mm  sqr 
section (Longitudinal). Heat 47484. 

Length 
(µm) 
3,2 
4,4 
4,6 
4,7 
10,3 
30,2 
40,0 
72,0 
130,0 

O 

Mg 

Al 

Si 

S 

Ca  Mn 

Total 

55,13  11,07  33,80 
52,81 
38,41 
3,71 
53,97  14,46  31,57 
35,02 
52,19 
36,74 
7,31 
53,28 
54,23  11,37  34,40 
54,43  13,43  32,14 
15,72 
33,42 
5,00 
36,10 
50,72 

1,09 

3,99 

12,79 
2,67 

17,08

7,45 
13,18 

21,33 

100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 

23 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 9 EDX analysis of then defects in the 60 mm bar product. Heat 47484. 

Table 10 summarises the analysis conditions and the resulting Density of particles (particles per unit 
area) measured on samples obtained along the hot rolling route for the two diameters produced from 
the single bloom sampled for this heat. 

Automatic Image Analysis results 

Sample/ 
Format/ 
Section 

Analysed  
Area  

(mm2) 

Bloom 
Billet 185 
Interm. Roll. Sect. 120 
mm squr 
Product D100 mm 

Product D60 mm 

50 
200 

50 

100 

100 

Analysed 
Sample 
Section 
(Long / 
Transv) 
T 
L 

Inc. 
Minimum 
Area 
Detected 
(µm2) 
12 
12 

L 

L 

L 

12 

12 

12 

Number of 
defects 

Density of particles 
(N/mm2) 

42 
327 

63 

64 

232 

0,84 
1.64 

1,26 

0,64 

2,32 

Table 10 

Analysis  conditions  and  the  resulting  Density  of  particles  (particles  per  unit 
area) along the rolling process. Heat 47484. 

Figure 5 shows the size distribution evolution along the hot rolling route, measured by image analysis 
after observation with optical microscope. The presence of tails in the distribution of bars of 60mm 
and 100mm is due to inclusion elongation. 

(a) 

24 
 
 
 
 
 
 
 
 
 
 
 
(b) 
Figure 5 

Inclusion  size  distributions  (Normalised)  along  the  rolling  route:  (a)  Inclusion 
Length  distribution,  (b)  Inclusion  Area  distributions  (Normalised)  along  the 
rolling route 

From these results it is roughly appreciated an increase of the detected oxide inclusions length and a 
decrease of its breadth along the rolling, i.e., as the product section decreases the inclusions elongate 
and becomes thinner, as expected. 

It should be mentioned that in order to obtain any result from the Automatic Inspection on the higher 
sections (as cast material and initial rolling sections), a huge amount of “artefacts” had to be rejected 
from  the  “raw”  inspection  set  of  detections.  These  features  were  identified  as  pores.  This  strongly 
interferes with the possibility to perform fast and reliable automatic analyses on the initial sections, as 
the main part of the detections should be discarded.  
This difficulty can be overcame by SEM automatic analysis, coupled with EDS analysis. In fact EDS 
analysis of a pore is the same of the steel, hence data can be filtered. Below are reported (Figure 17) 
as an example, the results of automatic image analysis (AIA) of Sidenor samples analysed by CSM, in 
order to have a set of data homogeneous with CAS, to be used as a database for statistical model (see 
next  paragraph).  Data  of  Figure  6  refers  to  heat  48826  and  final  diameter  of  45  and  36  mm.  Size 
distributions  are  in  agreement  with  what  reported  in  Figure  5  obtained  by  optical  microscope  by 
Sidenor.  

25 
 
 
 
 
 
 
 
 
 
Figure 6 

Size distributions from heat 48826 and final bar diameter of 45 and 36 mm 

CONCLUSIONS OF WP1 

The  analysis  carried  out  in  WP1  permitted  to  define  the  inclusion  and  defect  families  in  CAS  and 
Sidenor steel grades, the size distribution and the reproducibility of the steelmaking cycle. 

Inclusions  presents  in  steel  420C1  billet  (CAS)  are  calcium  silico  alluminates;  inclusion  present  in 
100Cr6 (Sidenor) steel grade are alumina, or alumina spinel inclusion.  

The defects presents in the rolled products belong to the same chemical families, More precisely the 
calcium  silico  alluminates  are  plastic,  deformable,  inclusions,  which  generate  defects  as  long 
structures;  alumina,  or  alumina  spinel  inclusion  generates  defects  from  deformation  of  clusters  of 
grouped particles, while single small particles are negligible as defect precursor. 

In  both  cases,  macroinclusions  found  in  tundish  were  not  found  in  as  cast  products  in  significantly 
amount,  probably  because  floated  or  captured  by  dams  in  tundish.  These  inclusions  wre  not 
considered as defect precursors.  

26 
 
 
 
 
 
 
 
 
 
 
 
 
WP2.  APPLICATION  OF  STATISTICAL  METHODS  FOR  INCLUSION  POPULATION 

CHARACTERIZATION (CSM, BFI) 

The objective of WP2 is to identify measurable parameters of inclusion population which can be used 
as  input  of  inclusion  deformation  models.  According  to  the  project  technical  Annex  the  statistical 
technique selected is the Extreme Values Analysis Statistics.  

The improved steel production of modern steelmaking plants led to a drastic reduction of the amount 
of  inclusions.  Therefore  conventional  approaches  of  detecting  average  dimensions  and  number  of 
particles are not useful for particle rating. Additionally the attention of producers is more focused on 
the size of big inclusions since these in-homogeneities are affecting fatigue strength and in-service life 
of the products. Therefore sophisticated approaches are necessary to supply tools to estimate the size 
of big inclusions basing on measurements not including such big particles. 
One  well-known  method  is  the  Extreme  Value  Analysis  (EVA)  which  bases  on  statistical  theories 
concerning distributions of data.  

It was found that in case of producing several data samples (in our case by analysing a bigger number 
of specimen concerning number and size of inclusions) the collected found maximum values (e.g. of 
inclusion  sizes)  are  belonging  to  an  extreme  value  distribution.  In  statistics  different  extreme  value 
distributions  are  known  (Gumbel,  Fréchet  und  Weibull  distributions).  In  case  of  extreme  value 
problems like those listed below the Gumbel distribution is usually applied to the data analysis. 

Possible questions solvable by Gumbel EVA are: 

• 
• 
• 

“How high must a dam be that it will not be flooded within a 100-years-period?” or 
“What is the probability for a 15% stock market crash?”, or just in our case 
“What size can be estimated for the biggest inclusion in a defined area of a steel sample?” 

The advantage of the application of EVA is that one needs not to collect data of 100-year-periods to 
estimate  the  maximum  event  in  100  years.  Such  an  event  can  be  estimated  for  instance  by  using 
collected data samples of 1-year-periods of the last 50 years.  
In  other  words  EAV  Statistic  is  a  methodology  suitable  to  detect  rare  events,  as  the  occurrence  of 
larger defects in steel. 

27 
 
 
 
 
 
 
Figure 7 

 Concept sketch of executing Extreme Value Analysis 

This technique has been set up by European Structural Integrity Society (ESIS), with the procedure 
ESIS P11-02, “Technical Recommendations for the Extreme Value Analysis of Data on Large Non-
metallic  Inclusions  in  Steels”.  According  to  the  procedure  developed  by  ESIS,  inclusion  size  is 
measured as square root area. This parameter has been used because it can be correlated to the fatigue 
behaviour through a model available in literature, kwon as model of Murakami Endo [2,6,7]. 

The ESIS procedure has been considered into ASTM Standard E 2283 – 03 [3]. 

The distinguish factor between the ESIS and ASTM procedure is that for the ASTM the defect size is 
measured considering the maximum dimension of the defect instead of the square root area 

28 
 
 
 
 
 
 
 
In  the  following,  Task  2.1  e  Task  2.2  are  described  in  the  same  paragraph,  being  the  two  activities 
carried out concurrently. 

TASK 2.1: DEFINITION OF THE PROTOCOL FOR EXTENSION OF THE ESIS 
PROCEDURE (CSM, BFI) 

TASK 2.2:  APPLICATION OF THE EXTREME VALUES STATISTICS FOR THE 
QUANTITATIVE DETERMINATION OF INCLUSION POPULATION. 

Basic concepts statistic of extreme values for defect analysis 
The Extreme Value Analysis (EVA) statistics is applied when the interest is focused on the maximum 
value (or minimum value) of a variable, without interest for the probability of occurrence of the whole 
set of values. This is exactly the case for the maximum size of defect. 
The inspection of the surface of a  steel sample, using a  microscope, reveals the presence of a large 
number of defect; the number of small defect is much bigger than number of large ones. In this case 
the  determination  of  the  distribution  of  the  all  defect  size  (the  so  called  parent  distribution)  is 
practically  impossible.  Actually  the  distribution  of  interest  is  the  distribution  of  extreme  values 
(maximum  values),  that  is  the  distribution  of  the  maximum  values  of  the  defect  size  in  a  prefixed 
number of zones (areas) of the steel samples. 
It can be demonstrated that the distribution of the maximum values (as well as the minimum, but not 
interesting  here)  tends  to  be  insensible  to  the  form  of  the  parent  distribution  when  the  number  of 
maximum values increases, and tends to well defined limit form (asymptotic forms).  

In the case of the defects the distribution of the maximum size follows a well established distribution 
applicable  for  events  with  the  left  size  of  the  maximum  distribution  limited  (zero  in  the  case  of 
inclusions size) and the right side unlimited [8].  
Thus, if we chose the largest defect, after inspection of a sufficiently large number of inspection areas, 
the  distribution  of  the  maximum  values  of  defect  size  in  the  selected  areas  follows  a  distribution 
described by a Gumbel extreme value distribution. 
The  cumulative  probability  (F(x)) of  the  Gumbel  distribution  of  extreme  values  is  expressed  by  the 
Equation (1). 

)x(F

=

⎧
exp
−
⎨
⎩

⎛
exp
⎜
⎝

−

(

x

)

λ−
δ

⎞
⎟
⎠

⎫
⎬
⎭

Where: 

(1) 

is the size of defects 
is the location parameter of the extreme value distribution function 
is the scale parameter of the extreme value distribution function 

x    
λ    
δ    
F(x)   is the probability to find defect up to the size x 

The size of the defect x is measured in two different ways: 

- 

- 

if the ESIS procedure is followed, x indicates the  area  of a defect 
is the ASTM procedure is followed, x indicates the maximum Feret diameter2 of a defect (in 
case of elongated inclusions coincides with inclusion length). 

2 It is defined as the distance between two parallel tangents of the particle at an arbitrary angle. In practice the 
Minimum  XF,min  and  Maximum  Feret  Diameter  XF,max,  the  Mean  Feret  Diameter    and  the  Feret  Diameters 

29 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                      
The parameters of the Gumbel distribution λ e δ can be calculated solving the linear system qwith the 
Equations 2 [8]: 

E(x) = λ + δ⋅0.5772 

sqm

)x(

=

π
6

δ

(2) 

E(x) is the average value of the distribution and sqm(x) is the standard deviation, both calculated by 
the experimental data of inclusions size.  

In  order  to  have  a  linear  relationship  on  the  graphs  it  is  reported  the  reduced  variate  “y”  of  the 
distribution function, defined by Equation (3): 

y

=

(

x

)
λ−
δ

and considering the logarithm of Equation (1). 

−

Ln

(

−

(
xFLn

(

)))

y
==

(

x

)
λ−
δ

(3)

(4)

The big benefit of this approach is that the existence of a distribution mathematically defined of the 
maximum  values  permits  to  evaluate  the  probability  of  occurrence  of  defect  size  from  a  smaller 
number of measured values. 
For this evaluation it is used the concept of return period, T. 
T is the reciprocal of a probability. Hence being F(x) the cumulative probability of finding defect up 
to the size x, the probability to find a defect of size greater than x is 1-F(x), and T=1/(1-F(x)). 

T

=

1
(
xF

))

1(

−

(5)

obtained  at  90°  to  direction  of  the  Minimum  and  Maximum  Feret  Diameters  XF,max90  are  used  for  particle 
measuring. 

30 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                                     
 
In the context of defect analysis, if the distribution of maximum defect size has been determined in a 
set of area of size S0 the concept of return period is used to predict the probability of largest defect in 
a reference area which is T times larger than the control area S0.  

The mathematical demonstration of the above equations can be found in literature (8, 10). 

The  following  paragraph  summarises  all  the  steps  necessary  to  obtain  a  sample  characterisation 
according to the Extreme Values Statistics [3]. 

Description of the Extreme Values Analysis for defects 
This  methodology  has  been  set  up  by  European  Structural  Integrity  Society  (ESIS),  with  the 
procedure  ESIS  P11-02,  “Technical  Recommendations  For  The  Extreme  Value  Analysis  of  Data  on 
Large  Non-metallic  Inclusions  in  Steels”.  The  ESIS  procedure  has  been  considered  into  ASTM 
Standard E 2283 – 03; the distinguish factor between the ESIS and ASTM procedure is that for the 
ASTM the inclusions size is measured considering the length instead of sqrt(area). 
The  measurement  of  inclusion  length  is  much  easier  and  faster  (and  it  is  the  parameter  measured 
according  to  ASTM  standard),  even  if  not  directly  applicable  to  predictive  model  of  fatigue 
behaviour.  

In  order  to  obtain  the  Gumbel  distribution  for  a  given  steel  sample  the  following  steps  must  be 
followed: 
- 

define a control area S0 in which the maximum defect must be recorded. The recommended 
value  for  S0  is  at  least  100  mm2  if  ESIS  procedure  is  followed  and  150  mm2  is  ASTM  is 
followed; in this measurement ASTM standard is adopted; 
prepare enough samples (each having area equal to S0) in order to have a statically significant 
area;  if  ESIS  procedure  is  followed  the  global  scanned  area  must  be  at  least  of    3000-4000 
mm2  (30-40  samples  each  with  S0=100  mm2).  ASTM  requirements  are  very  similar:  24 
samples  (each  with  area  of  150  mm2)  must  be  observed;    if  a  smaller  number  of  sample  is 
available  (for  example  eight  samples),  these  samples  must  be  re-polished,  after  observation 
and observed again. So, if for example eight samples are available, these must be repolished 
two more times in order to provide 24 sample observed. 
in each S0 the maximum inclusion (as square root of area or length, or both) is measured 
the Gumbel statistic is calculated 
the largest expected defect is calculated, considering a return period T=1000 

- 

- 
- 
- 

Application of Extrem Values Analysis to inclusions in as cast products 
The application of the EVA statistics to defects in products is well documented. 
A  major  issue  of  this  project  is  to  apply  the  EVA  technique  to  steel  samples  taken  from  as  cast 
products3.   
In  principle  this  should  be  obvious,  because  also  in  this  case  the  interest  is  on  extreme  (maximum) 
distribution  of  inclusion  size,  and  moreover  the  inclusions  are  the  precursor  of  the  defects.  The 
technical objective of the project is to demonstrate that the technique is reliable and robust to allow 
the prediction of final defect size and a practical industrial application. 

3  As  mentioned  above,  in  the  original  technical  plan  the  idea  was  to  analyse  inclusion  in  tundish,  after 

discussion with the TGS3 revisors it was decieded to focus attention on inclusion analysis in as cast products.   

31 
 
 
 
 
 
 
 
 
                                                      
Industrial application: example of analysis of samples from CAS 
EVA statistics has been applied to CAS samples of steel 420 C1 reported in Table 1.  
The procedure used to produce this results illustrates how the technique can be practically applicable 
in industrial routine. 

The  first  step  is  to  collect  steel  samples  and  determine,  at  the  microscope,  the  maximum  size  of 
inclusions found in 24 areas of the samples, each of 150 mm2, measuring the Feret diameter defined 
according to the ASTM Standard E 2283 – 03 described above. 
In  this  project  the  Feret  diameter,  typically  used  for  defects,  has  been  adopted  as  characteristics 
parameter also for inclusions. 
An example of measured values of Feret diameter of the largest inclusions in the 24 examined areas of 
steel sampled in a CAS billet are listed below, ordered by size. 

Rank 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
mean 
standard deviation 

Feret diameter 
(μm) 
53.70 
55.15 
55.73 
56.70 
63.95 
65.56 
66.73 
68.04 
68.13 
75.12 
77.85 
80.39 
84.18 
88.45 
89.80 
94.89 
99.65 
118.46 
119.50 
133.80 
134.20 
136.08 
144.90 
162.05 
91.37 
32.33 

Mean of the Feret diameter:  
Standard Deviation: 

91.37 μm. 
32.33 μm 

From  the  measured  values  can  be  plotted  according  to  the  Gumbel  distribution  of  the  cumulative 
probability. Figure 8A shows the cumulative probability distribution of inclusion size calculated using 
the  formula  of  the  Gumbel  distribution  described  by  equation  (1)  and  the  parameters  λ  and  δ 
calculated from equations (2) and statistical parameters of the distribution: mean value and standard 
deviation. 

32 
 
 
 
 
 
 
The meaning of the distribution curve reported in Figure 9 is that the probability that all the inclusions 
have a diameter equal or less than 162 mm is 0.97.  

The concept of EVA extrapolation is to calculate the diameter for which the probability that all the 
inclusions are smaller is much higher. According to the ASTM standard the probability value must be 
set to 0.999 (or 99.9%). 
The return period corresponding to 0.999 is T=1/(1-0.9999)=1000 (Equation (5)).  
From a statistical point of view this corresponds to an inspection area 1000 times larger than the area 
used physical point of view this probability  

From  a  mathematical  point  of  view  the  calculation  of  the  maximum  diameter  with  probability  of 
0.999 is just to find the value of x for which the equation (2) gives 0.999. 

To  simplify  the  graphical  representation  a  common  practice  is  to  use  a  logarithm  plot.  Actually  a 
double logarithm is necessary for the linearization of equation (4).  

The  construction  of  the  linearized  plot  is  simple.  For  a  set  of  probability  value:  Pi  (i=1:N),  the  bi-
logarithm expression is calculated: 

yi=-Ln(-Ln(Pi)) 

and from equation (3) 

xi=yi·δ+λ 

Where δ and λ are calculated by the Equations (2) and mean value and standard deviation of the set of 
measured inclusions size. 

The expected maximum size can be calculated with P=0.999 according to the ASTM standard.. 

Figure 8B shows the logarithm plot for the measured inclusion size in the CAS billet and the expected 
maximum value of inclusion size calculated from the probability distribution, setting the probability at 
0.999  

33 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
y
t
i
l
i

b
a
b
o
r
p
e
v
i
t
a
u
m
u
c

l

A 

)
)

P

(
n
l
-
(
n
l
-
=
y

measures

distribution

1,0

0,9

0,8

0,7

0,6

0,5

0,4

0,3

0,2

0,1

0,0

0,00

50,00

100,00
inclusion size (μm)

150,00

200,00

estimated distribution

95% confidence lower bound

95% confidence upper bound

measured values

max measured

max expected

8

7

6

5

4

3

2

1

0

0

50

100

150

200

250

300

-1

-2

B 

defect size, mm

Example  of  distribution  of  inclusion  size  calculated  from  the  measured  largest 

Figure 8 
inclusion Feret diameters found in the 24 areas of the samples taken from CAS billet  
Figure A shows the cumulative probability distribution (Gumbel distribution). Figure B shows 
the  linearized  distribution,  the  measured  average  and  maximum  value  and  the  expected 
maximum value calculated by EVA. The lower and upper confidence bounds are also reported 

34 
 
 
 
 
 
In Figure 8 some specific features of the extreme values statistics are also reported: 

1.  The average value of inclusion size (the average is calculated over 24 samples observed) 
2.  The maxim value of measured inclusion 
3.  The maximum expected defect for a return period T = 1000 
4.  The 95% confidence lower and upper bounds  

The  confidence  bounds  are  calculated  from  statistical  parameters  of  the  set  of  measured  inclusions 
data. The mathematical demonstration of the equations can be found in literature [8]. The confidence 
bounds  depends  on  the  number  of  observed  defects  (ASTM  standard  [3]  has  been  followed  in  this 
activity,  with  24  fours  defects  observed),  the  standard  deviation:  higher  standard  deviation  means  a 
bad fitting of the distribution function with the measured defects and as consequence the range of the 
confidence bands is very wide. 

In this project the innovative application of EVA statistics is on the analysis of inclusions in as cast 
products. However it has been also applied, in a more conventional way, to the analysis of defects in 
rolled products in both the industrial partners. Approach and procedure are the same as in the example 
described above for the inclusions analysis in a CAS billet.  
In  any  case  the  benefit  of  the  application  is  to  determine  the  maximum  size  of  the  defect  from  a 
limited number of analyses. It is worth to notice the conceptual difference of the application of EVA 
to  inclusions  and  to  defect.  In  the  first  case  the  goal  is  to  determine  an  input  for  the  deformation 
model,  in  order  to  predict  the  maximum  expected  defect  size.  In  the  second  case  EVA  is  used  to 
verify the output of the deformation model.  

Figure 9 below reports the distribution function calculated from measured  inclusions in as cast billet 
and observed defects in rolled products for steel 420 C1. The confidence bands are not reported just to 
avoid  the  overlapping  of  too  many  curves.  The  elongation  of  the  inclusion  during  rolling  is 
represented by the distribution function “y”. More elongated is the inclusion, more the curve is shifted 
towards larger values and the slope passes from almost vertical (billet) to almost horizontal (wire rod 
of  5,5  mm  diameter).  In  the  Figure  it  also  reported  the  inclusion  size  distribution  of  the  samples 
collected in the tundish. From the statistical point of view the distribution function of inclusion in the 
billet  and  inclusion  in  the  tundish  are  comparable,  which  means  that  have  larger  defects  with 
comparable sizes. 

35 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
as cast
billet

tundish

Diameter 13 mm  

13 

Diameter 8 mm  

8 mm

)
)
P
(
n
l
-
(
n
l
-
=
y

4

2

0

-2

5.5 mm
Diameter 5.5 mm 

0

50

100

150

200

250

300

350

400

450

500

Inclusion size - Feret diameter [µm]

Figure 9 

Distribution functions of steel samples (steel grade 420 C1) from tundish to wire 
rods having diameter of 13mm, 8mm and 5 mm. 

Table 11 reports the main data determined by extreme values analysis. In the first row of the tables it 
is reported also the average inclusion size determined by AIA analysis. 

TUND. 

as cast 
billet 

Wire rod 
diameter 13 mm 

Wire rod 
diameter 8 mm 

Average inclusion 
size (AIA) 
Average inclusion 
size 
Maximum defect  

18.0 

19.3 

34.0 

16.7 

14.2 

22.1 

return period 
T=1000  

64.0 

38.00 

64.1 

120.0 

200.5 

376.0 

83.30 

140 

250 

524.0 

Wire rod 
diameter 5.5 
mm 
108.6 

239.0 

410.0 

988.2 

Table 11 

Inclusion  size  (μm)  calculated  with  extreme  values  statistic.  Average  inclusion 
size measured by AIA analysis is also reported (steel 420C1) 

Analysis of samples from Sidenor 
Samples  have  been  delivered  from  Sidenor  to  CSM  to  perform  statistical  analysis  according  to 
Gumbel statistic. 

The list of Sidenor samples delivered to CSM and the performed analysis have been already reported 
in Tables 4 and 5. 

36 
 
 
 
 
 
 
 
  
 
 
 
The  application  of  the  extreme  value  statistic  to  Sidenor  samples  permitted  to  characterize  the 
inclusion evolution and the effect of rolling. Figures 10  reports the difference of distribution function 
for rolled products of 45mm and 32mm of final diameter. 

)
)
P
(
n
l
-
(
n
l
-
=
y

8

6

4

2

0

-2

Diameter 45 mm  

Diameter 32 mm  

y 49323
y 48827

49323 diam 32
48827 diam 45

0

100

200

300

400

500

Inclusion size - Feret diameter [µm]

Figure 10 

Distribution  functions  of  Sidenor  samples  on  rolled  products  having  final 
diameter of 32 mm and 45 mm. 

Tables 12-17  report all the size parameters obtained from extreme value statistical analysis. 
These  data  describes  the  evolution  of  inclusion  population  and  have  been  used  for  model 
development. 

 Heat 47484 
Average 
inclusion size 
Maximum 
defect  
return period 
T=1000 

tundish 

13 

21 

51 

185 125
18  14,5

24 

27 

45  67,2

100 

78 

163 

290 

60 
130 

305 

611 

Table 12 

Evolution  of  inclusion  population according  to  extreme  values  statistics  of  heat 
number 47484. Size expressed in μm. 

 Heat 48825 
Average 
inclusion size 
Maximum 
defect  
return period 
T=1000 

tundish 

185 billet 

11.3 

20 

34 

16.5 

22.0 

40.0 

36 

140 

210 

420 

Table 13 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the 
heat number 48825. Size expressed in μm 

37 
 
 
 
 
 
 
 
 
 
 
 
 49324 
Average 
inclusion size 
Maximum 
defect  
return period 
T =1000 

tundish 

185 billet 

36 

9 

18 

16.0 

21.5 

21,5 

40.0 

Table 14 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the 
heat number 49324. Size expressed in μm 

48826  
Average 
inclusion size 
Maximum 
defect  
return period 
T=1000 

tundish 

185 billet 

10,5 

20 

31 

15.3 

22.3 

40.0 

45 

115 

300 

326 

Table 15 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the 
heat number 48826. Size expressed in μm 

 48827 
Average 
inclusion size 
Maximum 
defect  
return period 
T=1000 

tundish 

185 billet 

14 

21 

33 

16.3 

24.5 

42.5 

45 

105 

200 

366 

Table 16 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the 
heat number 48827. Size expressed in μm 

49323  
Average 
inclusion size 
Maximum 
defect  
return period 
T=1000 

tundish 

185 billet 

8,5 

16 

18 

15.0 

21.3 

40.0 

45 

93 

195 

320 

Table 17 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the 
heat number 49323.  Size expressed in μm 

In Tables above it is reported also the inclusion size in tundish. According to the new technical annex 
discussed with Commission, all the subsequent activities focused on inclusion present on as cast billet 
and rolled products. From the statistical point of view, and regarding the application of the model of  

inclusion modification during rolling the distribution function of inclusion size in the tundish and in 
the  as  cast  billet  belong  to  the  same  dimensional  class.  This  concept  will  be  also  considered  in  the 
final discussion of the project results. 

38 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Correlation of data from inclusion and defect analysis 

All  data  coming  from  inclusion  size  distribution  function  and  extreme  values  analysis  have  been 
considered by BFI to find correlations among inclusion size during rolling (starting from inclusion on 
as cast billet) and to develop a statistical model of inclusion evolution during rolling. 

Compiling the inclusion data of Sidenor and CAS leads to the following diagrams (Figures 11 and 12) 
in which defects size is reported as a function of products diameter.  
Correlations have been carried out taking the data from automatic inclusion image analysis.  

Figure 11 

Analysis of inclusion features of CAS samples (upper diagram) and 
Sidenor samples (lower diagram) 

39 
 
 
 
 
 
 
 
Figure 12 

Analysis of inclusion features of CAS and Sidenor data, commonly consideration 
for regression  

The  diagrams  show  the  features  from  automatic  image  analysis  (AIA)  of  samples  of  CAS  and 
Sidenor. By means of a regression analysis the exponential regression was chosen to achieve the best 
fits for the three features of inclusion populations investigated with AIA. For the CAS data the billet 
dimension of 160x160 was changed into a related round format of 180 mm diameter (same billet cross 
section area).  
From  the  regression  analysis  one  can  see  that  the  parameters  of  the  found  regression  functions  for 
CAS  and  Sidenor  are  of  the  same  dimension.  Also  the  common  use  of  both  data  sets  achieves 
comparable results (see Figure 12) for the exponential regression functions. 

From  the  statistical  point  of  view,  and  regarding  the  application  of  the  model  of  inclusion 
modification during rolling, the distribution function of inclusion size in the tundish and in the as cast 
billet  belong  to  the  same  dimensional  class.  This  concept  will  be  also  considered  in  the  final 
discussion of the project results. 

CONCLUSIONS OF WP2 

The  application  of  EVA  statistics  to  inclusions  data  from  as  cast  products  permits  to  determine 
maximum size of inclusions to be expected in a billet. The technique can be applied in a simple way, 
following a defined procedure. Hence it is transferable for practical industrial application. 

The  next  WP  in  the  project  is  dedicated  to  develop  tools  for  predicting  size  of  defect,  and  the 
consequent mechanical  properties  of  steel  product  (namely  fatigue  limit)  from  this  information.  For 
this purpose in the project deterministic and statistic models are developed.  
To build a basis of data for the development of the statistic model mathematical correlations between 
maximum size of inclusions and defect size have been derived. 

40 
 
 
 
 
 
 
 
 
 
 
WP3: EVOLUTION OF THE INCLUSION SCENARIO ALONG THE PRODUCTION 
ROUTE 

The objective of WP3 is to develop models able to predict defect size in rolled products starting from 
the inclusion size in as cast products in order to formulate hypothesis on the expected quality, before 
rolling, and possibly to readdress the rolling operation. 

The models development has been carried out following two parallel activities: 

-  Collection of data from inclusion analysis for a statistical elaboration (BFI) 
-  A deterministic model based on Finite Element Model (CSM) 

The  results  of  the  model  will  be  correlated  also  with  the  fatigue  properties  of  the  steel  and  with 
product inclusion characterization carried out with currently used ranking method.  

TASK 3.1 MODELLING (BFI, CSM) 

Statistical model (BFI) 

It is worth to notice two aspects in the description of this Task 3.1.  

1) The statistical approach has been applied to both inclusions in as cast product and during rolling 
operations  and    in  defects  in  rolled  final  products.  To  avoid  boring  repetition,  in  what  follows  the 
name “particle(s)” is used to call inclusions and defects without distinction. 

2) The statistical model uses information obtained by the EVA statistics. The two “statistics” must be 
not  confused.  EVA  statistics  is  applied  to  obtain  information  about  the  maximum  expected  size  of 
inclusions or defect. The developed statistic model is to correlate this information (and other ones) for 
the population of inclusions and the population of defect.  

Data on inclusions and defect obtained in task 2.2 constitute the basis to develop a model based on 
neural network approach. All these data have been analysed to find correlations between the observed 
inclusions at the different process steps and the related deformation conditions. One approach to be 
investigated was the extraction of features from the measured particle distributions. This means that 
the  observed  frequency  distributions  (like  shown  in  Figures  5  and  6)  have  been  investigated  to 
identify features describing the properties of the distribution.  

The following figure explains this approach. 

Figure 13 

Concept for combined analysis: inclusion information + process parameters 

41 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
For  the  analysis  the  collected  inclusions  and  defects  populations  of  CAS  and  Sidenor  were  used  to 
calculate statistical features to describe these populations.  

The features based on all detected particles of each analysed sample (with AIA) were: 

•  mean value of length inside one particle  population  
• 
std. deviation of length  
• 
skewness of length distribution 
•  Kurtosis of length distribution 
•  10% quantile of length distribution 
•  90% quantile of length distribution 
•  T1000 extreme value of a set of samples from the same product 

These features were combined with the deformation ratio of the rolling processes. This deformation 
ratio was calculated as  

As  input  parameters  for  the  model  development  the  above  listed  statistical  features  and  the 
deformation ratio was used, as output parameters the mean value of inclusion length and the T1000 
EVA value were applied. 

The  developed  models  estimates  the  expected  mean/T1000  value  of  defect  size  of  a  rolled  product 
based on the distribution features of a measured inclusion population and the deformation rate.  

For model development a linear regression approach and a MLP neural network were tested. 

The following diagrams show the results of some of these tests. 

Figure 14 

Result of linear regression of mean defect length 

42 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 15 

Result of MLP neural net modelling of mean defect length 

The results show that the MLP model seems to be the better approach to estimate the resulting defect 
length after rolling.  

For the T1000 EVA value the following diagrams show the results of modelling approaches. 

Figure 16 

Result of of linear regression of T1000 EVA value of defect length 

43 
 
 
 
 
 
 
 
 
 
 
 
Figure 17 

Result of MLP neural net modelling of T1000 EVA value of defect length 

The results of estimating the T1000 EVA value are slightly worse than the results of the mean value 
estimation of defect size, but with linear correlation factors of around 0.8 the accuracy is quite high. 
Tests were carried out by leaving out one of the above listed input parameters to check id there is a 
redundancy of information. But it was found that this procedure decreases the accuracy of the tested 
model approaches, so the used input parameters are all statistical features and the added deformation 
ratio. 

But when looking at the results of these modelling approaches one have to consider, that the evaluated 
data samples are of a relatively low size (50 data sets). Additionally the found relationships between 
an  inclusion  distribution  and  the  deformation  ratio  to  the  resulting  defect  size  were  analysed  for 
samples of single steel grades. 
But  nevertheless  the  results  show  that  is  possible  to  estimate  the  mean  value  of  defect  size 
(respectively the T1000 EVA value) with a satisfactory level of accuracy.  

Deterministic model (CSM) 

CSM  developed  a  deterministic  model  of  inclusion  deformation.  The  objective  of  the  model  is  to 
calculate the deformation of the inclusions, taking ad input data: 

- 
- 
- 

- 

rheology properties of the steel 
behaviour of the inclusions during rolling 
rolling  parameters,  including  rolling  sequence  for  bloom  to  rod,  reduction,  strain  rate  and 
temperature estimation for each stand 
typical inclusion size on as cast billet, which is the starting size for modelling 

Basic Hypothesis of the model 
According  to  the  observed  inclusions  types,  (on  both  CAS  and  Sidenor  samples)  and  to  the 
corresponding observed behaviour during rolling, the following classification of inclusion behaviour 
has been identified: 

• 
• 

Fully plastic inclusions at any temperature (inclusion formed by a glassy phases, as silicates), 
Fully rigid inclusions at any temperature (as Alumina types), 

44 
 
 
 
 
 
 
 
 
 
 
 
• 

Inclusion behaving as cluster of rigid inclusions (rigid inclusions as alumina or spinel). 

These  inclusions  types  interact  with  steel  matrix  during  rolling.  The  different behaviour,  which  is  a 
consequence of the different inclusion types, determines three different situations [1,9,11 ]: 

1. 

2. 

3. 

Round  shape  starting  inclusion  which  elongates  plastically  without  detachment  at  the 
steel/inclusion interface 
Round  shape  starting  inclusion  which  remains  rigid  during  rolling  with  detachment  at  the 
steel/inclusion interface and voids formation 
Rigid  inclusion  cluster  elongation  along  the  rolling  direction  without  detachment  at  the 
steel/inclusion interface. 

Figure 18 represents the different steel/inclusion interface behaviour. 

Figure 18 

Scheme  of  different  mechanisms  of  inclusion  interaction  with  steel  matrix  and 
deformation during rolling 

For  all  the  three  types  of  inclusion  modification  during  rolling,  the  deformation  model  has  been 
developed according to the following basic issues: 

•  Mesoscale  approach:  a  portion  of  steel  sufficiently  larger  that  inclusion  size  has  been 

individuated as control volume.  

•  FE  modelling  of  a  volume  comprising  the  inclusion.  Figure  20  reports  the  FEM  grid  in  the 
considered reference volume, in case of starting inclusion considered as a sphere (one quarter 
reported) and in case of a cluster (simplified with four particles) 

•  Remote, macrostress/strain applied to the volume boundary 
•  Macrostress/strain history derived from process macro models: according to rolling program 

(for both CAS and Sidenor)  
Inclusion and matrix treated as separate bodies. 
Interaction through the interface. 
Inclusion/matrix modelled as elasto-plastic material. 

• 
• 
• 
•  Non linear FE with large strain, large displacement, updating procedures. 

45 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 19 reports a simplified scheme of the control volume used for calculations 

Applied 
stress

Reference
volume

matrix

inclusion

Figure 19 

Simplified scheme of model control volume (mesoscale approach) 

Figure 20 reports the FEM grid used for calculation 

Round shape inclusion 

Cluster of four inclusions 

Figure 20 

FEM grid for calculation 

Steel reology (stress) is evaluate according to the Hansel-Spittel formulation 

σ

=

Ae

1

m T m
ε
2

m
4
e ε

m
&
ε
3

where: 
A, m1, m2, m3, m4: 
T  
(cid:31)  

constants determined by steel rheological properties. 
temperature (K) 
strain MPa 

46 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Hansell-Spittel 
Parameters  

A  

m
1

m
2

m
3

m
4

420 C1 

3662  

-0,00307  

0,12765  

0,09731  

-0,00618  

Table 18 

Parameters used in the Hansell-Spittel formulation for steel 420C1  

Stress as a function of steel strain has been calculated for steel 420C1 and for steel 100Cr6.  

Figure  21  reports  as  an  example  the  stress  as  a  function  of  steel  strain  at  different  temperatures  for 
CAS steel 420C1.  

420 - ε '=3 s-1

500

400

300

200

100

)
a
P
M

(
s
s
e
r
t
s

0

0

900

1000

1100

1200

1300

1

2

3

4

5

plastic strain

Figure 21 

Steel reology property used in the model: stress/strain curve according to Hansel 
Spittel formulation for steel 420C1 at different temperatures  

Annex 2 reports all the collected rolling parameters for CAS and Sidenor used for modelling purpose. 
Below  are  reported  the  main  characteristics  of  both  rolling  programs.  The  following  rolling 
parameters have been used for FEM calculations: 

CAS 420 steel grade: 

•  Temperature: 1250°C at beginning of rolling; 1000°C at last step of rolling 
•  Strain rate: comprised between 3 and 300 1/s 
•  Total reduction: 6.8 (true) 
•  Equivalent diameter: from 164 to 5.5 mm 

47 
 
 
 
 
 
 
 
 
 
 
 
 
Sidenor C6 steel grade: 

•  Temperature: from 1200°C to 965°C during rolling 
•  Strain rate: comprised between xx and xx 1/s 
•  Total reduction: 2.4  (true) 
•  Equivalent diameter: from 185 to 36 mm 

Figure 22 reports the inclusion elongation calculated by the FEM model, starting from a round shape 
particle and simulating a plastic behavior. 

Starting 
round  shape 
inclusion 
(one  quarter 
represented) 

Starting 
elongation 

of 

Elongation 
during 
rolling 

Elongation 
during 
rolling 

Final 
elongation 
(reduction 
ratio) 

Figure 22 

Results of FEM calculation of inclusion elongation 

An  important  parameter  present  in  this  calculation  is  the  inclusion  plasticity.  The  first  calculations 
were  carried  out  assuming  for  the  inclusions  of  steel  420C1  the  same  plasticity  of  the  steel.  These 
calculations  overestimated  the  inclusion  elongation.  So  inclusion  plasticity  has  been  calibrated  with 
experimental data. Inclusion plasticity has been put 1/3 of steel plasticity. 

The  calculation  shows  how  the  inclusion  elongates,  and  depending  on  the  rolling  process  inclusion 
fragmentation is possible. No interface detachment among steel and inclusion forms in case of plastic 
behaviour.  This  is  also  confirmed  by  the  experimental  observation.  Figure  23  reports  a  detail  of 
calculation of elongation of plastic inclusion, in which fragmentation phenomena is starting, without 
detachment from steel interface. The fragmentation of plastic inclusion is a well kwon phenomena and 
experimentally observed. 

48 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 23 

OM  images  of  inclusion  observed  on  5.5    mm  wire  rod  and  comparison  with 
FEM  calculation.  In  Figure  it  is  reported  the  inclusion  zone  in  which 
fragmentation occurs 

Figure 24 reports the result of the simulation of the fully rigid inclusion. In this case the matrix is a 
deformable body, while inclusion remains rigid and fixed. Temperature is considered constant during 
simulation.  

49 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a 

b 

c 

e 

d 

f 

Figure  24  FEM  simulation  of  rolling,  with  steel  matrix  deformation  around  a  fully  rigid 
inclusion. 

Calculation shows that during rolling a void forms between the inclusion and the steel matrix, along 
the direction of rolling. During rolling the size of this void increases and also a small folder forms. At 
the end of rolling the folder closes and the size of the void is decreased, (see for example picture d and 
f in Figure 24). Results of calculations indicate that during rolling, the presence of small undeformable 
inclusions can be neglected. 

Figure 25 reports the results of simulation of inclusion cluster. The calculation has been carried out 
considering as starting point four square inclusions (in this example each one of 5 (cid:31)m size) and the 
matrix  modeled  as  deformable  body.  The  inclusions  are  modeled  as  rigid  bodies  and  the  rolling 
reduction applied by rigid surface motion. The steel behavior is, as in all other cases elastic-plastic, 
while inclusion behavior is fully rigid. Temperature is kept constant during calculation 

50 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Starting  simmetric  cluster  of 
four small inclusions 
Elongation during rolling 

Elongation during rolling 

Elongation during rolling 

Elongation during rolling 

Final  rolling  with  the  four 
inclusion  along  a  straight  line 
and grouped at two extremes 

Figure 25 

FEM simulation of a cluster of four rigid inclusions during rolling 

Elongation of a cluster of four small inclusions during rolling shows that inclusions have the tendency 
to dispose in a straight line and are grouped at the two extremes. 

TASK 3.2 ASSESSMENT OF THE METHOD (SIDENOR, CSM, CAS) 

According to the performed inclusions analysis and observation, the inclusion present in steel 420C1 
have been considered as plastic. This assumption is based on observed aspect of elongated inclusion 
and  it  is  in  agreement  with  literature  [1],  which  attributes  plastic  behaviour  at  calcium  allumino 
silicates inclusions. 
The  elongation  of  Sidenor  inclusions  has  been  simulated  considering  that  the  starting  inclusion 
(alumina,  alumina  scarcely  modified  by  calcia,  or  alumina/spinel)  behaves  as  a  cluster  of  rigid 
particles. This is in  agreement with literature  [1],  which attributes a rigid behaviour to alumina and 
alumina spinel inclusions, characterised by fragmentation and disposition of fragments along rolling 
direction. 

In  order  to  validate  the  model  the  specific  sampling  campaigns  have  been  carried  out  by  CAS  and 
Sidenor. 

CAS collected the following samples: 

•  Sampling of three slices of 160 x 160 billett 
•  Sampling of wire rods of the following final diameter 

Diameter 5.5 mm 
Diameter 6.5 mm 
Diameter 7.5 mm 
Diameter 9.6 mm 
Diameter 14 mm 
Diameter 26 mm 

51 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Due to the need to correlate the fatigue behaviour with inclusion size (see details in next paragraph) 
steel samples have been prepared and observed in both longitudinal and transversal direction. 
The  steel  samples  have  been  analysed  according  to  Extreme  Values  Analysis    statistics,  and  three 
values  of  inclusion  size  have  been  determined:  the  average  inclusion  size,  the  maximum  measured 
inclusion and the  maximum expected inclusion. 

Table 19 reports the results of the extreme values statistic: length in the longitudinal direction and the 
length  in  the  transversal  direction.  In  the  transversal  direction  inclusions  have  circular  shape  and 
length is practically inclusion diameter. 

Sample 
160 mm cast billet  
26 mm 
14 mm 
12 mm 
9.6 mm 
7,5 mm 
6.5 mm 
5,5 mm 

Longitudinal 
max. 
meas. 
31 
160 
280 
290 
300 
430 
450 
460 

average 
16 
91 
170 
170 
190 
280 
300 
290 

max 

expect.  average 

58 
270 
450 
500 
520 
770 
841 
800 

16 
5,5 
4,0 
4,0 
3,8 
3,1 
3,0 
3,1 

Transversal 
max. 
meas. 
31 
11,1 
8,4 
8,3 
8,1 
6,8 
6,6 
6,6 

max 
expect. 
58 
21,9 
17,0 
16,1 
15,8 
13,0 
12,4 
12,8 

Table 19 

Results of the extreme values statistic (length in the longitudinal direction and 
the length in the transversal direction). For CAS steel 420C1 

Calculation  of  inclusion  elongation  through  model  application  The  model  has  been  applied  three 
times,  assuming  as  starting  inclusion  value  in  the  billet  the  three  values  calculated  with  statistical 
elaboration. 
Figure  26  reports  the  evolution  of  inclusion  length  during  rolling,  expressed  as  logarithm  of  total 
reduction4. Experimental points reported in the Figure 37 refers to wire rod of 5.5 mm diameter (the 
smallest). 

4 Total reduction = ln(S1/S2) 

S1: section at time t during rolling 
S2: section at time t+Δt during rolling  

52 
 
 
 
 
 
 
 
 
 
 
                                                      
 
 
)

m
μ
(
h
t
g
n
e
 l
n
o
s
u
c
n

i

l

i

900

800

700

600

500

400

300

200

100

0

0

420 C1

2

4

6

8

total reduction (logarithmic)

Figure 26 

Evolution of inclusion length during rolling, expressed as reduction ratio. (Stell 
grade 420C1, CAS) 

The agreement between experimental data and model calculation is good. 

The model simulates the inclusion elongation. Then from inclusion length it is derived the inclusion 
transversal  section,  assuming  the  initial  inclusion volume  as  constant  and  considering  the  elongated 
inclusion as a cylinder. Practically, during rolling, inclusion length increases and transversal section 
decreases. 

Inclusions  analysed  in  100Cr6  Sidenor  steel  grade  have  been  attributed  to  cluster  behaviour,  being 
these inclusions solid at liquid steel temperature (alumina and spinel). The sampling campaign carried 
out at Sidenor to validate the model has been the following: 

•  Casting of 185 x 185 billett 
•  Rolling up to following diameters: 
-  Diameter 50 mm (1 sample) 
-  Diameter  45 mm (1 sample) 
-  Diameter  40 mm (1 sample) 
-  Diameter  36 mm (4 samples)  

Table  19  reports  the  measured  statistic  parameters  (EVA).  The  table  reports  the  values  in  the 
longitudinal direction and in the transversal direction. In the as cast billet round shape inclusions are 
present and for this reason the size is the same in both directions of observations 

53 
 
 
 
 
 
 
 
 
 
 
sample 

185 mm cast billet  
50 mm 
45 mm 
40 mm 
36 mm 

longitudinal 
average   max. meas. max expect.
23.5 
335 
330 
330 
360 

14.6 
160 
160 
164.3 
188 

45 
480 
470 
480 
540 

transversal 
average   max. meas.  max expect. 

14.6 
10.5 
5.4 
5.2 
4.7 

23.5 
13.2 
12.0 
12.0 
11.0 

45 
33.1 
34.2 
22.7 
30.0 

Table 20 

Results  of  the  extreme  values  statistic  (length  in  the  longitudinal  direction  and 
the length in the transversal direction) for Sidenor steel 100Cr6 

Figure 27 reports the model calculation; dots represent the experimental measurements. 

Figure 27 

Evolution of inclusion length during rolling, expressed as reduction ratio (Steel 
grade 100Cr6, Sidenor) 

The agreement between experimental data and model calculation is good. 

Figure 28 compare the results of statistical model (described in Figure 22 and 23) and deterministic 
model. 

54 
 
 
 
 
 
 
 
 
 
 
 
FEM model 100Cr6

FEM model 420C1

stat model

900

800

700

600

500

400

300

200

100

0

)
n
o
r
c
i
m

(

t
h
g
n
e
l

n
o
i
s
u
l
c
n

i

0

5000

10000

15000

20000

25000

30000

billet/wire rod area (mm2)

Figure 28 

Comparison of statistical model and deterministic model 

The agreement is good for steel 420C1. 
For steel 100Cr6 the agreement is for large areas, which corresponds to the first steps of rolling. 
The statistical model predicts smaller inclusion respect the FEM model. This is due to the fact that it 
includes  data  from  automatic  image  analysis  which,  has  explained  before,  does  not  consider  that 
inclusions can undergo to fragmentation during elongation. 
This fact is more evident for inclusion of Sidenor, which are strongly fragmentized during elongation. 
In  fact  to  take  into  account  this  phenomenon  FEM  model  approximates  the  behavior  of  such 
inclusions  as  the  elongation  of  a  cluster  of  a  rigid  inclusions,  and  not  as  plastic  deformation  of  a 
particle. 

TASK 3.2 DEFINITION OF CORRELATIONS BETWEEN INCLUSION QUANTITIES 
AND FEATURES AT THE DIFFERENT PROCESS STAGES (SIDENOR, CSM, CAS) 

In order to implement the deformation model that could help to define correlations between the initial 
(as cast product) and final product (bars and wire rods) inclusion populations, it has been provided for 
each of the final bar products the rolling sequence in which it is detailed the specific characteristics at 
each of the rolling steps (temperature, reduction ratio …). 

This sequence for each final diameter has been calculated by means of an specific software optimised 
to design the rolling route combined with pyrometric temperature measurements made in situ during 
the  hot  rolling  of  the  first  heat  sampled  (“stepped”  sampling  on  heat  47484).  During  this  initial 
sampling, the rolling start temperature was monitored using a pyrometer, being almost the same for 
both final diameters (∅ 100 mm and 60 mm): about 1200ºC. Thus, the rolling route calculations for 
the other final diameters have been done considering the same starting conditions: 185 mm billet at 
1200ºC. 
In the following Table 21 it is summarised the rolling sequence for the sampled products. It should be 
noticed that depending the final product diameter, the number of rolling passes (mill housings) used is 
different. This also implies different reduction ratios per rolling step and at different temperatures. For 
each of the products (final diameters) it is indicated the temperature, semi-product section (in mm2) 
and the reduction ratio in relation to the previous step. In addition it is indicated at which step of the 
rolling  sequence  the  intermediate  product  samples  have  been  obtained  (“stepped”  sampling  of  the 

55 
 
 
 
 
 
 
 
 
47484 heat). On the other hand, as mentioned earlier, the higher the final diameter, the less the rolling 
passes needed. In the cases in which any rolling pass has been omitted it is indicated in the table as 
“By-pass”. 

56 
 
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57 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Additional sampling for Models validation (Heat #54062): 
In  order  to  provide  new  samples  from  the  models  validation  a  new  sampling  campaign  was 
performed.  One  single  heat  (#54062)  was  sampled  along  the  rolling  path.  Samples  from 
185mmx185mm billet, two intermediate sections (about 120 mm sqr & about 90 mm round) as well 
as the final 65mm diameter bar were sampled. Samples were sent to the corresponding partner (CSM) 
in  order  to  perform  the  complete  inclusions  characterisation.  In  this  occasion  the  Image  Analysis 
coupled to Optical Microscopy was skipped according to the discussion presented in WP1 in relation 
to  the  feasibility  of  this  technique  to  obtain  reliable  results.  Following  this,  the  Inclusions 
Distributions tests were transferred to the IA coupled with SEM with EDX analysis available at the 
CSM. 

In addition, two billets (185 mm sqr) from the same heat have used to perform a specific rolling order 
within the WP4. As it will be shown later, one of the billets was rolled to 100mm bars  
and  the  other  to  62  mm  bars  and  provided  material  for  the  final  assessments,  including  the  Fatigue 
Characterisation as a function of the rolling path. 

CONCLUSIONS OF WP3 
In  WP3  models  to  predict  the  evolution  from  inclusions  to  defects  during  rolling  have  been 
developed: a deterministic model and a statistical model.. 
The  models  resulted  substantially  able  to  predict  the  evolution  of  the  size  of  the  particles.  Some 
differences  in  the  comparison  between  prediction  and  measure  of  defect  size  in  case  of  alumina 
clusters  originated  defects  can  be  attributed  to  inadequateness  of  the  adopted  inspection  technique, 
which was Automatic Image Analysis from microscopic photos. The technique tends to underestimate 
the size of defect from cluster which is not continuous, while the effect on the quality of the product 
of this type of defect is in line with the prediction of the deformation models. 

Comparison  between  predictions  of  the  models  at  different  step  during  rolling  operations  indicates 
that all the evolution of the inclusion size is described by the models and not only the final size. This 
result  n  makes  the  model  reliable  for  making  decision  on  rolling  operation,  which  is  the  activity 
carried out in WP4. 

58 
 
 
 
 
  
 
WP4: FROM INCLUSION ANALYSIS TO INTELLIGENT MANUFACTURING  

The  objective of the WP4  is to establish relationships between defect size and fatigue limit  in the 
product  and  to  use  this  relationships  to  predict  fatigue  limit  in  the  rolled  product  from  inclusion 
analysis in the as cast product. 

Defect size can be detrimental because can cause a direct rejection of the product, as in the case of 
420C1  (CAS),  where  the  acceptability  of  customers  is  based  on  the  defect  ranking  according  to 
International standard E45. 
The  presence  of  defect  also  affects  the  resistance  to  fatigue.  Hence  the  fatigue  limit  of  a  material, 
imposed by the presence of defects, can be used as criteria to establish the acceptability of a defect. 
This approach has the advantage that can be quantified with objective measurement, not depending 
on  operator  evaluation  (like  in  evaluation  of  defect  size  from  microscopic  photos)  and  supplies 
information on global performance of the product strictly linked with the final requirements.  

This step concludes the logical sequence in the project in order to have all the information necessary 
to  make  decision  about  rolling  procedure  to  be  adopted  to  avoid  unacceptable  defect  and 
unacceptable material characteristics. 

In WP4 the sequence has been tested in industrial tests. 

Task 4. 1 Effect of inclusion size of the quality of the product after rolling (Sidenor, CAS) 

Generally speaking, the influence of the microstructure and of the distribution of the defect on fatigue 
strength of steels can be investigated according to the Murakami-Endo’s model. 
The effect of the microstructure is considered by hardness value, while the influence of the inclusions 
is  represented  by  the  maximum  expected  defect  (experimentally  determined  according  to  extreme 
values  statistic).  In  addition  the  model  can  take  into  account  the  in-service  load  conditions  of  the 
component. 
In particular Murakami and Endo proposed an empirical equation in order to predict the fatigue limit, 
in terms of a material parameter (Vickers hardness, HV) and a geometric parameter of the maximum 
expected  inclusion  (√area).  Usually  the  fatigue  limit  refers  to  a  total  fatigue  life  of  10  millions  of 
cycles.  
The empirical model of Murakami and Endo can be validated by experimental fatigue tests performed 
on standard specimens considering the real in-service loading conditions.  

CAS measured the fatigue limit of steel specimen prepared according to the geometrical specifications 
reported in Figure. Usually the fatigue limit can be evaluated using “stair case method” according to 
the standard  ASTM procedure. In order to reduces the fatigue scatter band at least a number of fifteen 
(15) fatigue specimens are required. Neverless fatigue limit can be extrapolated from Woelher curves 
obtained from standard fatigue tests according to a simplified procedure already used and validated in 
another RFCS project, (MICROFAT, RFSR-CT-2005-00034), 

Sidenor characterized the fatigue behavior of the steel using two different experimental methods, in 
order to perform a larger number of measurements and to have a statistical validation of the effect of 
the inclusions on the steel properties. Three different data treatments have been used for determining 
the effect of the inclusion size on the fatigue performance: 

59 
 
 
 
 
 
 
 
 
 
 
 
 
•  Weibull analysis of the fatigue life populations: The L10 index has been calculated for all the 
tests performed, considering in each case the total set of tests as a single population tested at a 
single set of conditions (constant Hertz pressure in the case of the Rolling Contact Fatigue, or 
applied load for the Resonant test on cylinders described elsewhere). This treatment shows an 
improvement of the fatigue with increasing reduction, but considers the total set of failures, 
even those that may have been originated by the matrix failure but not from a sub-superficial 
inclusion. As discussed in Annex V,  for the RCF test it is not always accessible the fracture 
plane,  so  it  can  not  be  stated  the  real  cause  of  the  failure.  For  the  resonant  test,  the  Life 
analysis  is  done  considering  the  complete  population  tested  at  a  fixed  load  (22KN).  To 
compare to the RCF test, the complete population of failures is considered, and the Weibull 
analyses conduces to an equivalent result compared to the RCF test. 

•  Fatigue life as a function as a function of the local stress at the failure origin: by this analysis 
it  is  evidenced  the  reduced  fatigue  performance  associated  to  the  higher  dimension  of  the 
inclusions in the lower product diameter of a single heat.  

•  Fatigue  limit  calculation  according  to  Murakami  Endo  √(area)  model  using  the  measured 
dimensions of the inclusions found in the resonant fatigue specimens. This calculation allows 
estimating the effect of the inclusions elongation on the fatigue performance and to compare 
the experimental results to the model predictions. 

Fatigue limit measurement (CAS) 
The fatigue limit is the mechanical property that can be correlated directly with inclusion size [7,8,9]. 
For this reason it has been considered as the main property to correlate the inclusion population with 
product properties. In any case, it is important to consider that customers classify steel products with 
standard ranking methods. Results from fatigue limit measurements, results of calculation of fatigue 
limit  (according  to  the  literature  model  of  Murakami-Endo)  and  qualitative  considerations  from 
standard  ASTM  E45  ranking[6]    must  be  considered  for  a  correlation  of  inclusion  population  and 
product characteristics. 
The standard method ASTM E45 is widely used in the technical specifications of the customers, even 
if actually replaced by EN10247.  
Steel  samples  of  rolled  products  from  at  least  two  different  heats    having  different  inclusion 
distributions has been taken. This comparative analysis will be carried out on steel 420C1 

Specimen for fatigue testing have been prepared form a 26 mm diameter wire rod. Following figure 
reports the geometrical size of fatigue sample. 

60 
 
 
 
 
 
 
 
 
 
Figure 29 

Technical drawing of specimen for fatigue limit test 

Inclusions  are  elongated  in  the  axial  direction  of  the  specimen.  The  inclusion  size  which  affect  the 
fatigue resistance is the one transversal  to the direction of application of the load [7]. In this specific 
case the inclusion size that has to be considered is the transversal section. The Murakami Endo model 
which is applied and compared with measured fatigue limit is  expressed according to the following 
formulation: 

where  

•  C is adimensional constant 1.41 
•  HV is the material hardness expressed in Vickers 
•  Area is the inclusion area on the transversal section expressed in mm2 

Results of fatigue tests: σ =310 ± 20 MPa 

Following Figure reports the extreme values distribution of inclusions measured in the as cast billet 
and  on  the  transversal  section  of  wire  rod  (rolled  up  to  26  mm  of  diameter).  Inclusion  size  is 
expressed as square root area of transversal section. 

61 
 
 
 
 
 
 
 
 
 
As cast billet 

26 mm wire rod 

Figure 30 

Extreme  values  distribution  of  inclusions  measured  in  the  as  cast  billet  and  on 
the  wire  rod  (rolled  up  to  26  mm  of  diameter).  Inclusion  size  is  expressed  as 
square root area of transversal section 

Following  table  reports  the  calculation  of  the  fatigue  limit.  The  calculation  has  been  carried  out 
considering as starting inclusion the three results obtained from extreme values statistic. The average 
inclusion, the maximum measured and the maximum expected defect. As inclusion size is considered 
the square root area of the transversals section. 

Regarding the correlation with fatigue limit, the larger is the inclusion on as cast billet, the smaller is 
the fatigue limit on the rolled product. 

The longer is the considered inclusion on the rolled product, the smaller is the transversal section and 
higher is the resulting fatigue limit. 

62 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Calculated from average inclusion size in billet 
Calculated  from  maximum  measured  inclusion  in 
billet 
Calculated from inclusion size at T=1000  
Measured fatig. limit  

Fatigue limit MPa 
320 

310 
300 
310 

Table 22 

Calculation  of  the  fatigue  limit  of  steel  420C1  according  to  the  inclusion  size 
measured on the wire rod of 26 mm diameter 

In case of correlation with inclusion ranking the inclusion dimension  which is to be considered is the 
inclusion length. In case of product classification, longer inclusion gives the lower ranking and as a 
consequence possible claims from customers.  
The sampled heat gave the following ranking: 
Inclusion type: C (silicates) 
Grade: 2,5 

The results of the extreme value statistic gave the following result (Figure 31) 

estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,0422x - 3,2397

0

50

100

150

200

250

4

3

2

1

0

-1

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)
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P
(
n
l
-
(
n
l
-
=
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average 
max. measured 
Expected at T=1000 

defect size, μm

91 micron 
162 micron 
270 micron 

Figure 31 Distribution function and main features obtained from extreme value statistic applied 
to the wire rod of 26 mm  

63 
 
 
 
 
 
 
 
 
 
 
 
 
This heat, can be assumed as a reference situation, being the obtained ranking accepted by customers: 

E45 grade: 2,5 
Maximum expected defect 270 micron 

It is important to remark the maximum expected defect that is calculated from application of extreme 
value statistic to inclusion analysis, respect conventional parameters, as average inclusion size. In fact, 
it  can  happens  that  two  steel  samples  have  the  same  average  size  of  defects,  but  two  different 
distribution functions and as a consequence two different expected defects. In this case one product 
will be accepted and the second product will be rejected by customer. Figure 32 (data are purposely 
put  for  this  example)  explains  this  concept.  The  distribution  functions  in  the  figure  have  both  the 
average value of inclusion length of 91 micron, but two different values of expect defect (at T=1000): 
270 micron in curve 1 and 360 micron in curve 2.  

8

7

6

5

4

3

2

1

0

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P
(
n
l
-
(
n
l
-
=
y

0

-1

-2

1 

2 

100

200

300

400

defect size, μm

Figure 32 

Example of distribution function having the same average value of inclusion size, 
but different maximum expected defect 

Measurement of fatigue properties and correlation with inclusion presence (Sidenor) 

In order to evaluate the effect of the inclusions present in the steel regarding its effect on the fatigue 
performance, two alternatives have been considered: 

-  Rolling Contact Fatigue (RCF) test, 
-  Compressive high frequency fatigue test on hollow cylinders. 

In  the  following  only  principal  remarks  on  these  techniques  and  the  obtained  results  are  discussed: 
more details (including of figures and tables) are reported in Annex 5. 

Rolling Contact Fatigue (RCF) test 
This test is very time consuming and, in addition, a high amount of test specimens should be tested to 
get  statistically  representative  set  of  results  that  could  allow  to  perform  the  corresponding  Weibull 
analysis.  The  test  arrangement  consist  on  a  commercial  bearing  (spherical  rollers)  transmitting  the 

64 
 
 
 
 
 
 
 
 
 
 
 
 
load applied through a lever on a flat washer (the sample). The maximum Contact Pressure is fixed by 
the proper combination of rollers (balls) and applied load. 
Regarding the inclusions characterisation, on the RCF specimens, it should be mentioned that: 

- 

- 

due to the method for fracturing the specimens, in some cases, in the origin of the pitting, the 
inclusion  is  not  accessible  or,  if  accessible,  part  of  it  (or  even  the  complete  inclusion)  may 
have  been  lost.  This  difficulties  have  caused  problems  in  the  determination  of  the  inclusion 
chemical composition and total dimension, 
the inclusion size is usually underestimated. In fact, on the one hand, part of the inclusion can 
be  hidden  or  even  lost  when  fracturing  the  sample  and,  on  the  other  hand,  the  samples  are 
extracted from transversal sections of the bars. In this second case, thus, the inclusions near 
the surface usually intercepts the sample surface, i.e, the original inclusion in the bar product 
may be longer than the actual observed one. 

Compressive high frequency (resonant) fatigue test on hollow cylinders (tubes)  
This  test  has  been  developed  in  order  to  get  a  “fast”  fatigue  test,  sensible  to  the  longer  inclusions. 
Failures, due to inclusions subject to a tensile stress field, occur much faster than in RCF test, and the 
sensibility  to  very  long  inclusions  is  high  (as  the  load  are  applied  transversally  to  the  inclusion 
length).  
The performed simulation calculations showed how the most stressed (tensile) areas are located in the 
upper  and  lower  internal  surface  and  in  the  lateral  external  surfaces.  This  method  is  proved  to  be 
directly  sensitive  to  the  inclusion  length  and  can  be  correlated  with  result  of  inclusion  elongation 
models. 

Comparison between the two techniques 
The inclusions found in the crack origin of the tubular specimens, tested in the resonant equipment, 
are in general longer than the ones, found in the flat washer type specimens, used for the RCF test. 
The inclusions that cause the pitting in the RCF specimens are not always accessible for analysis and 
are usually underestimated in size in relation to the real inclusion in the original rolled bar product, 
from which the sample has been extracted. On the contrary, in the case of the tubular specimens, the 
inclusions  are  usually  accessible  in  its  complete  dimension  and  the  risk  of  loosing  them  during 
specimen  preparation  is  much  lower.  This  allows,  on  the  one  hand  the  proper  EDX  analysis  of  the 
inclusion and, on the other hand, the better evaluations of the inclusion total length. In fact, this test 
has  revealed  the  existence  of  long  inclusions  not  revealed  in  the  RFCS  test,  i.e.,  it  is  much  more 
sensible to the extreme long inclusions present in the steel. 

Due to the difficulty to determine whether a failure has been caused by the presence of an inclusion in 
the  subsurface  for  the  RCF  test  or  not,  the  complete  set  of  failures  have  been  considered  in  the 
statistical analysis of the Fatigue Life’s population (Weibull Analises). For the heat 47484 rolled to 
100mm and 60 mm bars, such an analysis has shown an improvement of the fatigue life of the smaller 
diameter  (L10  index  is  230%  better  for  the  D60  mm  product  compared  to  D100mm  product),  i.e., 
with increased maximum inclusion size, in opposition to the expected result. 

On the other hand, the resonant Test on Cylinders, shows a similar result when the overall population 
of failures are considered (i.e., originated on inclusions or caused by matrix failure) and analysed as a 
single population tested at a fixed maximum load of 22KN. The Weibull analysis of such a set of data 
also shows an improvement of the L10 index when reduction degree increases (142% higher for D60 
mm product compared to D100 mm). 

But  as  discussed  later  in  the  annex,  on  the  Cylindrical  specimens  tested  transversally  there  exist  an 
stress gradient as a function of the angular position (and also as a function of the position along the 
specimen length due to boundary effects in the extremes of the sample). By assigning to each failure 

65 
 
 
 
 
the  calculated  stress  intensity  value  in  the  position  of  the  failure  initiation,  the  resulting  data 
population clearly shows how the longer inclusions causes fatigue failures at lower stress levels, and 
these mainly occur on the lower diameter product (that is, the one with the higher reduction). 
This can be seen in Figure 33 in which it is represented Resonant Fatigue test on Cylinders performed 
on the two products of the heat 47484 (D100 mm and D60 mm bars). In this figure, for each specimen 
the local stress at the fracture initiation is calculated (details in the corresponding annex) as a function 
of the fracture origin position. Every data point is represented by a circle with an area proportional to 
the inclusion length. 

Heat 47484-Fatige Life vs Rupture Stress & Inc Length

700

)
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600

i

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t
S

500

400

300

200

100

0
1,E+00

1,E+01

1,E+02

C47484-D60

C47484-D100

1,E+06

1,E+07

1,E+08

1,E+03

1,E+04
Fatigue Life (Cycles)

1,E+05

Figure 33 

Heat 47484: Resonant fatigue on cylinders as a function of the local stress at the 
fracture origin. Area of the data “discs” is proportional to the inclusion length in 
the fracture origin. 

Considering the maximum value of the SqrRoot (Inc. Area) in the Murakami Endo formula (C=1,41 
and  HV=765)  the  expected  Fatigue  limits  as  a  function  of  the  product  diameter  are  shown  in  the 
following table: 

66 
 
 
 
 
 
 
 
 
 
 
 
 
 
Heat 

Bar 
Diameter 

47484 
47484 

100 
60 

√Area(inc)|max 
(LengthxWidth=Area) 
(μm2) 
448 
803 

Inc Length 
(μm) 

Inc Width 
(μm) 

2235 
14350 

90 
45 

M-E  Calcullated 
Fatigue 
Limit  
(MPa) 
451 
409 

In conclusion, the resonant transversal test (newly developed): 

- 

- 
- 

- 

is able to discriminate, for different products, the effect of the long inclusions in the fatigue 
performance of the material 
is sensible to the presence of inclusions in the subsurface of the specimen 
failure mode (longitudinal crack all along the section of the specimen) allows an easy access 
to  the  fracture  surface  and  to  the  crack  initiation  point,  so  that  it  can  be  easily  determine 
whether the cause of the failure is related to an inclusion in the steel or not. In addition, the 
total  length  of  the  inclusion  is  accessible,  so  it  is  possible  to  determine  it  real  total 
dimensions,  and  from  this,  it  is  possible  to  calculate  the  corresponding  Fatigue  Limit 
according to the Murakami Endo model as the parameter selected for model validation. 
the individual specimen test is much shorter (longer tests takes about 3 days) while the RCF 
individual runnouts spend more than a month. 

Thus, this test was considered to be useful for the evaluation of the material quality in relation to the 
occurrence of very large inclusions. . The great advantage of the new method is the time required to 
evaluate a similar number of specimens, as it is drastically reduced. According to these results, it was 
decided to adopt this test for the final industrial application to a real case, as it was the only feasible 
way to get any characterisation of the fatigue performance within the remaining project schedule. 

Task 4.2 Definition of the protocol  (Sidenor, CSM)  

On the basis of the performed activities, a protocol of analysis of samples, definition of the expected 
quality of the cast product and definition of the subsequent manufacturing operations (as rolling) has 
been defined.  

For Sidenor and CAS, and considering the industrial facilities and production scheme, the alternative 
rolling  routes  means  the  different  attainable  diameters  for  the  product.  If  the  final  diameters  are 
different enough the rolling operation is done with a different set of mill housings and reduction rates 
conducting to a different deformation sequence and degree. 

The application of the protocol is carried out according to the following steps: 

1.  Sampling of steel from as cast billet to define the starting inclusion size. This sampling 
activity can be done only in those steel shops in which some hours (more than ten) pass 
from casting to rolling. This the case of CAS and Sidenor. Alternatively, samples may be 
collected from tundish. This may causes loss of information from eventual formation 
2.  of macro inclusion during continuous casting, but has the advantage of fast sampling. In 
this  case  the  procedure  described  in  Annex  2  can  be  followed  for  sample  cutting  and 
preparation. According to the results obtained in this project (see for example Figure 9) 
the distribution functions of inclusion size in tundish and as cast billet overlap. 

3.  Inclusion analysis according to Extreme Values statistic as reported by ASTM E 2283  
4.  Model  application  to  calculate  inclusion  elongation  starting  from  maximum  expected 

inclusion on as cast billet 

5.  Definition  of  rolling  sequence  which  will  allow  to  obtain  products  with  characteristic 

which fit with customers specifications 

67 
 
 
 
 
 
Task 4.3 Application to a real case (Sidenor, CSM, CAS) 

Industrial  application  of  developed  tool  has  been  tested  at  Cogne  and  Sidenor  steel  plant.  The 
application has been carried out according to the following sequence of activities: 

Task 4.3.1: Application of the predictive tool to the definition of the hot rolling route 
1. 

Selection of the steel heat and sampling of as cast product. Enough as cast material should be 
reserved  as  to  perform  the  subsequent  hot  rolling  according  to  the  route  to  be  defined  after 
models application. 
Analysis on as cast steel samples (billet) to evaluate the initial inclusion scenario 
Prediction of the inclusion scenario on the product by applying the developed models to the as 
cast inclusion populations. 
Definition  of  the  rolling  routes  on  the  basis  of  the  defined  inclusion  scenario,  in  order  to 
obtain two different final product inclusion scenarios (different product diameters or different 
rolling routes for the same diameter) 

2. 
3. 

4. 

Task 4.3.2: Sampling and validation 
1. 
2. 

Hot rolling of the selected heat according to the defined routes 
Sampling of intermediate rolling sections (if needed) and final products produced according 
to the routes defined in previous task. 
Inclusion analysis on the product (as rolled according to the procedure defined in the previous 
point)  
Fatigue testing on bearing steel  
Inclusions ranking according to current standards (as ASTM E45) for the Stainless grades. 

3. 

4. 
5. 

Industrial trial with steel 420C1 (CAS) 
According to the technical Annex, after casting of  billett (160mm x 160mm). three slices have been 
cut from three billet and samples prepared according to the scheme in Annex 4. 
The twelve samples have been repolished two times, for a total  number of 24 observations [3].  

The extreme values statistic gave the following results 
average  
17 
Maximum measured inclusion  38 
66 
expected 

μm  
μm 
μm  

Figure 34 reports the Extreme values distribution function of the inclusions measured in the as cast 
billet.  In  the  same  figure  it  also  reported  the  distribution  function  of  Figure  9.  It  is  important  to 
observe  that,  while  the  average  inclusion  size  is  similar,  the  slope  of  the  curves  is  different.  This 
means that the expected defect is significantly different. In fact, in the new samples billet the expected 
defect is 62 μm length, while in the previous activity was 45 μm length. 

68 
 
 
 
 
 
 
 
 
Distr. function of Figure 9 

Industrial 
trial 

Figure 34 

Extreme values statistic on as cast billet of industrial trial and comparison with 
previously performed analysis 

The  application  of  the  FEM  elongation  model  (in  case  of  plastic  inclusion)  has  been  carried  out 
starting  from  the  maximum  expected  inclusion  on  the  billet.  The  calculation  has  been  carried  out 
simulating rolling up to two diameters: 20 mm and 5,5 mm diameter. 

The size of the transversal ration has then been calculated and corresponding fatigue limit calculated. 

The obtained results are reported in below: 

Calculated inclusion size (μm)  200 longitudinal / 5,5 transversal
Calculated fatigue limit (MPa) 

290 

20 mm wire row (μm) 

5.5 mm wire rod (μm) 
940 longitudinal/ 3.2 transversal 
-- 

The obtained fatigue limit is lower than the reference value of 310 MPa as defined in the WP3, due to 
the larger starting inclusion size. The calculation of fatigue limit in case of diameter of 5,5 mm has 
not been carried out because no comparison with experimental measurement is available for so small 
diameters.  

Following Figure reports the results of characterization of inclusion population found after rolling at 
20 and 5,5 mm final diameter 

69 
 
 
 
 
 
 
 
 
   
 
 
 
 
4

3

2

1

0

-1

-2

)
)

P
(
n
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(
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=
y

0

50

100

150

200

4

3

2

1

0

)
)

P
(
n
l
-
(
n
l
-
=
y

-1

-2

0

200

400

600

800

1000

1200

defect size, μm

defect size, μm

20 mm wire rod. Max expected defect 210 micron. 
Microscopic picture represent maximum measured 
defect 

5,5  mm  wiew  rod.  Max  expected  defect  960 
micron.  Microscopic  picture  represent  maximum 
measured defect 

Figure 35 

Characterization  of  inclusion  population  found  after  rolling  at  20  and  5,5  mm 
final diameter, starting from 160 x 160 billet of steel 420C1 of CAS. 

The  inclusions  found  on  the  rolled  products  have  similar  length  to  what  calculated  by  model. 
Calculated fatigue limit of 290 MPa, is quite similar to reference measured value of 310 MPa. 

Anyhow the inclusion length (both calculated and then experimentally measured on samples of wire 
rods) indicates that this material may be rejected after E45 ranking and should be rolled up to the final 
diameter larger than 5.5  mm. 

70 
 
 
 
 
 
 
 
 
SIDENOR ACTIVITY: 
Since all the previous characterization of the material mechanical performance (fatigue tests) had been 
done on the 100mm and 60 mm diameter products, it was decided to manufacture the same products 
for these trials. For the industrial final trials, two billets from the same heat (n°54062) were rolled to 
obtain two different product diameter: 100mm and 62 mm in diameter. These two products allow: 
- to compare the fatigue behaviour of two different heats, rolled through equivalent rolling paths. 
- to compare the two most extreme diameters available considering the product ranges of the Basauri 
rolling mill.  
In fact, on the one hand, the 100 mm bars are the maximum size that can be rolled in this facility and, 
on  the  other  hand,  the  60  mm  product  (62  mm  in  this  case)  is  the  smallest  diameter  that  allows 
manufacturing the fatigue specimens.  
Details  of  the  mechanical  testing  of  these  samples  are  included  in  Annex  6.  Moreover,  samples  of 
billet section, intermediate rolling sections and final products were delivered to the CSM for inclusion 
evaluation. 

Similarly  to  what  performed  with  CAS  samples,  billet  samples  after  casting  have  been  delivered  to 
CSM for EVA analysis and calculation of inclusions length of rolled products of 100 and 62 mm of 
diameter, purposely produced for the project. 

The extreme values analysis of inclusion on as cast billet gave the following results: 
Average inclusion: 16 μm 
Maximum measured inclusion: 29 μm 
Maximum expected defect: 48 μm 

According to FEM model simulation the length of the maximum expected defect will be: 
100 mm diameter: 100 μm 
62 mm diameter: 370 μm 

The calculation of fatigue limit (according to Murakami Endo model) gives the following values: 
100 mm diameter: 720 MPa 
62 mm diameter: 660 MPa  

Fatigue tests: Comparison between samples from different heats 

The Weibull life analysis was performed and compared to the previously obtained data on heat 47484. 

For  these  analyses  it  has  been  considered  the  complete  set  of  data  (Failures  and  Runnouts),  and 
statistically treated as tested at a single applied load level (Test Maximum Load = 22 KN). 

In the following figure it is compared the cumulative failure probability for both heats concerning the 
D62mm  samples.  It  appears  a  clear  improvement  of  the  fatigue  performance  of  the  62  bar  product 
(from heat 54062) compared to any of the corresponding products from heat 47484.  

71 
 
 
 
 
 
 
 
 
Figure 36 

Cumulative Failure Probability comparison between heat 54062 and 47484. 

D100  mm  samples  from  heat  54062  failed  due  to  the  matrix  collapse  (see  details  in  Annex  5):  the 
cause  of  such  a  poor  fatigue  behaviour  may  be  due  to  surface  defects  caused  during  the  specimens 
machining, not to inclusion presence. A comparison between the critical index L10 are reported in the 
table 23. 

Diam (100mm) 

Diam (60mm) 

Heat 47484 

Heat 54062 

Heat 47484 

Heat 54062 

L10 
(Pulses) 

4.300 

2.786 

L10 
(Pulses) 

10.175 

971.188 

Table 23 

Comparison of heat 54062 and heat 47484 products. 

72 
 
 
 
 
 
 
 
 
Data treatment considering local stress level at the failure origin 

From  the  Numerical  simulation  of  the  Stress  distribution  within  the  transversally  loaded  cylindrical 
specimen, and considering the position of the failure (angular, in relation to the top, an linear, along 
the specimen length) it is possible to split in different stress levels the failures in the material. 

This has been done for the next set of graphics, in which it is represented the Fatigue life as a function 
of the local stress (Figures 37, 38, 39). In addition, each of the data points has been represented with a 
disc  whose  area  is  proportional  to  the  dimension  (length,  width  or  equivalent  diameter)  of  the 
inclusion  found  as  the  cause  of  failure.  Thus  it  is  highlighted  the  effect  of  the  different  inclusion 
geometrical features on the fatigue performance of the different products as a function of the inclusion 
size. 

Fatige Life vs Rupture Stress & Inc Equiv Diam

700

600

500

400

300

200

100

C47484-D60

C47484-D100

C50624-D62

C54062-D100

)
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t
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s
s
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S

0
1,E+02

Figure 37 

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Fatigue  life  as  a  function  of  the  Local  Stress  at  the  failure  origin.  The  Area  of 
each  data  “disc”  is  proportional  to  the  Inclusion  EQUIVALENT  DIAMETER 
(Sqr Root of the total inclusion area) 

73 
 
 
 
 
 
 
 
 
 
Fatige Life vs Rupture Stress & Inc Length

C47484-D60

C47484-D100

C54062-D62

C54062-D100

700

600

500

400

300

200

100

)
a
P
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i

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t
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t
a
s
s
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S

0
1,E+02

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Figure 38 

Fatigue  life  as  a  function  of  the  Local  Stress  at  the  failure  origin.  The  Area  of 
each data “disc” is proportional to the Inclusion LENGTH 

74 
 
 
 
 
 
 
 
 
 
Fatige Life vs Rupture Stress & Inc Width

C47484-D60

C47484-D100

C50624-D62

C54062-D100

700

600

500

400

300

200

100

)
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i

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t
a
s
s
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S

0
1,E+02

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Figure 39 

Fatigue  life  as  a  function  of  the  Local  Stress  at  the  failure  origin.  The  Area  of 
each data “disc” is proportional to the Inclusion WIDTH 

From these graphs it can be stated: 

-  The  Inclusions  length  increase  (correspondingly  the  Equivalent  Diameter)  associated  to  a  higher 
reduction of the 60 mm compared to the 100 mm products from Heat 47484 conduces to failures at 
lower  stress  levels  and  with  reduced  fatigue  life.  Thus,  the  increase  in  length  of  the  inclusions 
conduces to a decrease of the fatigue limit of the material. 

- The 60 mm product from Heat 54062, behaves similarly to the D100 mm product from heat 47484. 
The former heat in fact shows shorter inclusions that the later.  

- The D100 mm product from Heat 54062 behaves the worst, but its reduced performance has been 
associated to specimens manufacturing defects. A lack of time has not allowed repeating these tests 

From  these  results  it  can  be  stated  that  the  inclusion  scenario  present  in  the  Heat  47484  strongly 
degrades the fatigue performance for the lower rolling diameters, compared to the Heat 54062. 
In spite of this, for the final decision of the minimum product size to which one specific heat can be 
processed  an  extended  database  of  Heats  should  be  required.  Anyway,  the  combination  of  this 
experimental  methodology  combined  to  the  predictive  model  for  the  estimation  of  the  maximum 
inclusion size, should provide a powerful tool to estimate the expected effect of the different possible  
rolling paths on the final performance of the material.  

75 
 
 
 
 
 
 
 
 
 
 
 
The  analysis  on  industrial  samples  confirmed  these  findings,  in  fact  the  maximum  expected  defect 
found in case of diameter 60 mm of heat 47484 is 611 μm, while the maximum expected defect of 
diameter 60 for the heat 54062 is 380 μm.  

The indication given by the tool application of a possible material downgrading was correct. 

CONCLUSIONS OF WP4 

In  WP4  defect  size  and  final  product  properties,  represented  by  fatigue  limit  and  also  by  Inclusion 
ranking according to standard methods have been correlated.  
The  agreement  among  measured  fatigue  limit  in  CAS  and  calculated  fatigue  limit  from  defect  size 
was good.  
In  case  of  CAS  steel,  defects  ranking  is  also  important  for  product  acceptability  from  customers. 
Defects  ranking  methods  are  correlated  with  length.    A  quantitative  correlation  among  standard 
ranking  methods  (as  currently  used  E45  standard  method)  and  defect  size  was  not  possible,  but 
qualitative correlation has been observed. It has been also verified that the indications obtained from 
simulation  of  final  defect  length,  from  inclusion  size  observed  on  as  cast  billet  are  useful  and  in 
agreement with what observed on rolled products. 

In case of Sidenor steel grade, a good agreement has been observed by measured fatigue behavior and 
observed  defects.  The  degrading  of  fatigue  properties  due  to  large  defects  deriving  from  large 
inclusions on as cast billets has been confirmed by industrial trials. 

76 
 
 
 
 
 
 
 
 
 
WP 5 FINAL REPORT 

Integration from RFCS TGS2 experts comments 

The  project  objective  is  the  development  of  a  tool,  able  to  calculate  the  size  of  defects  on  rolled 
products. This type of tool may be used preliminarily to rolling, to optimize the final production of 
rolled products reducing the number of claims from final customers. 

In the first version of the project  defects on rolled products would be calculated taking as input the 
size  of  inclusions  sampled  during  liquid  steel  fabrication.  After  the  mid-term  report  discussion  the 
Experts of the Technical Group (TGS2) asked to consider as starting point for modeling the formation 
of defects, the inclusion on as cast product. 
The Programme Officier of the European Commission pointed out also that the project target was too 
ambitious.  

However  a  quite  innovative  application  of  inclusion  analysis  has  been  described  in  this  report. 
Inclusion  analysis    has  been  used  not  for  product  characterization,  or  for  process  diagnosis,  but  for 
“intelligent manufacturing”.  

In  fact,  despite  an  immense  number  of  projects  about,  inclusions  still  represent  a  problem,  causing 
defects in products. The work performed in the last forty years gave a deep knowledge on inclusion 
genesis  during  steelmaking  routes  and  control  techniques.  But  a  number  of  uncontrolled  events  can 
cause the presence of large inclusions or clusters in the slab that can be transformed in not acceptable 
defects in the rolled product. 

The  idea  of  this  project  is  to  develop  a  set  of  tools  allowing  foreseeing  starting  from  information 
retrieved from analysis on cast billets. On the basis of this information the steelmaker  can decide  a 
different  rolling  procedure,  changing  the  final  destination  of  the  product,  in  order  to  reduce  the 
probability of downgrading the product, reducing the number of claims from customer. 

Even  if  the  results  obtained  within  the  project  can  not  be  transferred  directly  into  a  rolling  control 
system, the consortium considers as a quite a new and innovative approach to inclusion analysis. 
It is important to mention that, while project activities were ongoing, a paper was published, by other 
Research  Centre  on  a  very  similar  topic  (Yuichi  Kanbe  et  al,  Application  of  Statistics  of  Extreme 
Values  for  Inclusions  in  Stainless  Steel  on  Different  Stages  of  Steel  Making  Process,  ISIJ 
International, Vol. 51 (2011), No. 12, pp. 2056–2063).  
In  this  paper  a  similar  application  of  inclusion  analysis  (carried  out  using  the  Extreme  Values 
Statistic)  was  described.  In  the  conclusions  of  the  paper,  it  is  stated  that  “..in  the  case  of  elongated 
inclusions like Type B1, the described method is applicable with sufficient accuracy for prediction of 
the  maximum  size  of  inclusions  in  the  hot  rolled  steel  based  on  the  Extreme  Value  Distribution 
(EVDs) for inclusions in the molten steel and slab samples”. 

In this project (similarly to the mentioned paper), the Extreme values statistic has been used because it 
allows to estimate the size of bigger inclusions from a relatively small number of samples. Inclusion 
size is measured both as length and square root area. All these aspects are described in task 3.1 and 
3.2; the square root area is the amount used in the Murakami Endo model which calculates the fatigue 
from steel hardness and inclusion size. On the other side  length is easier to be measured (specially in 
case of inclusion fragmentation) and it is proportional to area.  

77 
 
 
 
 
 
 
 
 
 
 
 
It must be remarked that a certain correlations among inclusions, calculated defect size and properties 
of  rolled  products  have  been  found,  but  reliability  to  build  a  control  system  with  them  should  be 
strongly improved. 
So,  as  matter  of  fact,  the  indication  coming  from  global  results  of  Inclusion  project    is  that 
investigations and projects, simply based on correlations are prone to fail, in the sense that are able to 
give general indications (or tendency) about the downgrading of product properties with defect length. 
Future  projects  aiming  only  at  establishing  such  kinds  of  correlations  would  not  bring  any  other 
significant  contribution.  Very  strong  advanced  modelling  including  the  modification  of  the  physic-
chemical  and  properties  of  the  particles  as  a  function  of  process  parameters  (temperature,  pressure, 
time...) is necessary to give a deeper view on the way how inclusion can affect the characteristics of 
rolled products. 

Task 5.1  Discussion of results (All)  

An innovative application of inclusion analysis has  been described in this report. Inclusion analysis  
has  been  used  not  for  product  characterization,  or  for  process  diagnosis,  but  for  intelligent 
manufacturing.  
Despite an immense number of projects about, inclusions still represent a problem, causing defects in 
products.  The  work  performed  in  the  last  forty  years  gave  a  deep  knowledge  on  inclusion  genesis 
during steelmaking routes and control techniques. But a number of uncontrolled events can cause the 
presence of large inclusions or clusters in the slab that can be transformed in not acceptable defects in 
the rolled product. 
The  idea  of  this  project  is  to  develop  a  set  of  tools  allowing  foreseeing  –  in  a  relatively  quick  and 
cheap  way  -  the  probability  of  occurrence  of  not  acceptable  defects  produced  in  given  rolling 
operations,  starting  from  information  retrieved  from  analysis  on  cast  billets.  On  the  basis  of  this 
information the steelmaker can decide a different rolling procedure, changing the final destination of 
the product, in order to reduce the probability of downgrading the product. 

Inclusions present in the slab in continuous casting can generate defects after rolling. The formation 
of a defect depends on:  

• 

• 

size  of  the  starting  inclusion  and  the  subsequent  size  of  the  elongated  inclusions  as  a 
consequence of rolling operation  
reduction ratio applied in the rolling: the larger is the reduction ration, the larger will be the 
length of the resulting inclusion 

Basically, the formation of a defect can be prevented acting on: 

1.  reduction of inclusion size and amount in the steelmaking route, in order to obtain an as cast 

product with an inclusion scenario formed by a small size inclusion 

2.  adapting the rolling program (the reduction ratio) to the inclusion scenario; in other words, if 
the final product is a wire rod, a billet production having a small size inclusion scenario can 
be  rolled  up  to  thin  diameter,  while  a  billet  production  with  larger  inclusion  scenario  are 
rolled following a different program, with smaller reduction ratio. 

The  first  action  to  improve  product  quality  is  currently  defined  as  clean  steel  production.  As  clean 
steel  is  addressed  the  production  of  steel  with  low  level  of  impurities.  Referring  to  inclusion,  steel 
cleanliness is controlled by a wide range of operating practices throughout the steelmaking processes. 
These  include  the  time  and  location  of  deoxidant  and  alloy  additions,  the  extent  and  sequence  of 
secondary metallurgy treatments, optimization of stirring and transfer operations, shrouding systems, 

78 
 
  
 
 
 
 
 
tundish  geometry  and  practices,  the  absorption  capacity  of  the  various  metallurgical  fluxes,  and 
casting practices. 

The  second  approach  is  a  new  one,  and  it  is  the  objective  of  this  work.  This  approach  is  not 
alternative,  but  complementary  to  clean  steel  production.  In  fact,  clean  steel  production  is  aimed  at 
reducing  the  impurities  and  inclusion  content  during  steelmaking  route;  with  the  application  of  this 
approach  the  objective  is  to  optimize  the  rolling  program  in  order  to  limit  the  rejection  of  material 
from customers, once steel is produced and cast. 
A preliminary activity of process examination to follow the whole history on inclusion during process 
has been carried out; then having verified the process stability, and taking into account the suggestion 
of  the  project  Revisers,  the  activity  focused  on  inclusion  on  as  cast  billet  and  subsequent  rolled 
products. 

Statistical correlations have been established among inclusions in billed and rolled products. For the 
statistical analysis and modeling of inclusion data it can be concluded that these approaches, basing 
on  statistical  features  of  measured  inclusion  distributions,  can  achieve  high  levels  of  accuracy.  The 
described approaches can be helpful to estimate the final product quality using information from the 
as-cast product state. 

A FEM model has been then developed, to calculate inclusion elongation during rolling, starting from 
inclusion in as cast billet. 
The agreement (proved with samples from the two different steel shops participating to this project) 
between experimental and calculated inclusions is good. 
An important aspect in modeling was: which is the starting inclusion size on as cast billet to be used 
as  input  for  the  elongation  model.  In  this  activity  the  starting  inclusion  is  the  maximum  expected 
defect  which  can  be  determined  through  Extreme  values  statistic  analysis  on  samples  from  as  cast 
billet. This approach has the advantage to take into account presence of macro defects which may not 
be found with conventional methods of analysis. 
The utilization of maximum expected inclusion to predict product properties gives the most reliable 
result. Both deterministic and statistical model are able to simulate inclusion deformation knowing the 
starting inclusion size and rolling program and steel rheology. 
The correlation of inclusion size and product properties has been carried out considering the inclusion 
ranking with conventional standard methods (ASTM E45), fatigue limit and fatigue behavior. 

Comparison  of  statistical  model  and  FEM  (deterministic  model)  showed  that  the  two  model  are  in 
good agreement in case of inclusion scenario of steel 420C1 (silicates), which have a plastic behavior. 
In case of inclusion of steel 100Cr6, from Sidenor, the statistical model underestimates the length of 
defects on final products. This is due to the specific behavior of rigid inclusions, which undergo to a 
strong  fragmentation  during  rolling.  In  this  case,  in  order  to  obtain  a  reliable  result,  FEM  model 
approximates this inclusions as a cluster of rigid particles. The database used to build the statistical 
model  has  been  built  with  automatic  image  analysis,  which  has  some  difficulties  to  consider  an 
elongated and fragmented inclusion as a single particle.  

The correlation of fatigue properties of steel, with fatigue properties calculated from inclusion size has 
been verified and it is in good agreement. 

The  final  industrial  trials  demonstrated  that  this  tool  is  useful  to  give  indications  about  rolling 
operations.  This  tool  can  be  used  also  to  evaluate  the  advantages  of  improving  of  the  inclusion 
scenario on the billet on the as cast product.   

79 
 
 
 
 
 
 
 
 
The  activities  of  this  project  constitute  the  basis  for  a  new  approach  of  inclusion  analysis  and 
correlation of inclusion population and properties of final product. 

REFERENCES 

1.  R. Kiessling, Non metallic inclusions in steel, the institute of metals, London 

2.  ESIS  P11-02  “Technical  Recommendations  For  The  Extreme  Value  Analysis  Of  Data  On 

Large Non-metallic Inclusions In Steels” 

3.  ASTM  E  2283  –  03  “Standard  Practice  for  Extreme  Value  Analysis  of  Non-metallic 

Inclusions in Steel and Other Microstructural Features” 

4.  ASTM E3-01, Standard guide for preparation of metallographic specimens 

5.  ASTM E 7 - 03, Standard terminology relating to metallography 

6.  ASTM E45-97 Standard test methods for determining the inclusion content of steel 

7.  Y.  Murakami,  M.  Endo,  Effects  of  Defects,  inclusions  and  inhomogeneities  on  fatigue 

strength, Fatigue, 1994, Vol 16, April, pp 163-182 

8.  S. Beretta, Y. Murakami, Largest–Extreme Value Distribution Analysis of Multiple Inclusion 
Types in Determining Steel Cleanliness, Metallurgical and Materials Transactions B, Volume 
32B, June 2001-517 

9.  Pekko  Juvonen,  effects  of  non-metallic  inclusions  on  fatigue  properties  of  calcium  treated 
steels, Dissertation for the degree of Doctor of Science in Technology, Helsinki University of 
Technology, 2004 

10.  S. Beretta, Clive Anderson, Y. Murakami, extreme value models for the assessment of steel 

containing multiple types of inclusion, Acta Materalia, 54, (2006) 227-2289 

11.  Prediction of microstructure and constraint effects on fatigue behaviour in engineering 

applications(MICROFAT), Contract Number: RFSR-CT-2005-00034 

EXPLOITATION AND IMPACT OF THE RESEARCH RESULTS  

Application: the project results can be applied in those steel shops in which the as billets are not rolled 
immediately.  In  this  case  if  there  some  hours  available  for  billet  sampling  and  analysis  (by  optical 
microscope) the expected quality on rolled products can be evaluated 

Advantages: reduce the amount of steel products rejected by customers 

Patent: to transform the developed tool into a patent a subsequent industrial validation is necessary  

Publication: During the project no publication has been produced. 

Other  aspects:  project  results  constitute  the  basis  for  a  control  system.  Industrial  validation  is 
necessary  to have  a  fine  tuning of  this  tool;  with  larger  industrial  trials  it  will  be  better  defined  the 
correlation between foresee inclusion scenario on products and product properties 

80 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
LIST OF TABLES 

Table 1  

Chemical composition (%) of 420C1  martensitic stainless steel 

Table 2  

Typical composition (%) of the 100Cr6 heat sampled (from heat 47484) 

Table 3  

List of  collected steel samples by CAS 

Table 4 

Details  of  the  first  sampling  campaign  (stepped)  at  Sidenor.  Heat  Number  47484 
Bloom casted.  

Table 5  

Second sampling campaign at Sidenor (Steel grade: 100Cr6).  

Table 6  

Average inclusion size in along steel production route, up to rolling, for steel 420C/1. 

Table 7 EDX analysis of then inclusions in the BLOOM section (Transversal). Heat 47484. 

Table 8 

EDX  analysis  of  then  inclusions  in  the  INTERMEDIATE  ROLLING  120  mm  sqr 
section (Longitudinal). Heat 47484. 

Table 9  

EDX analysis of the defects in the 60 mm bar product. Heat 47484. 

Table 10 

Table 11 

Table 12 

Table 13 

Table 14 

Table 15 

Table 16 

Table 17 

Analysis  conditions  and  the  resulting  Density  of  particles  (particles  per  unit  area) 
along the rolling process, Heat 47484. 

Inclusion  size  (μm)  calculated  with  extreme  values  statistic.  Average  inclusion  size 
measured by AIA analysis is also reported (steel 420C1) 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  of  heat 
number 47484 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the  heat 
number 48825 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the  heat 
number 49324 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the  heat 
number 48826 

25 Evolution of inclusion population according to extreme values statistics or the heat 
number 48827 

Evolution  of  inclusion  population  according  to  extreme  values  statistics  or  the  heat 
number 49323 

Table 18 

parameter used in the Hansell-Spittel formulation for steel 420C1 and 100Cr6 

81 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 19 

Table 20 

Results  of  the  extreme  values  statistic  (length  in  the  longitudinal  direction  and  the 
length in the transversal direction). For CAS steel 420C1 

Results  of  the  extreme  values  statistic  (length  in  the  longitudinal  direction  and  the 
length in the transversal direction) for Sidenor steel 100Cr6 

Table 21 

Rolling route parameters corresponding to the different products sampled 

Table 22 

Calculation  of  the  fatigue  limit  of  steel  420C1  according  to  the  inclusion  size 
measured on the wire rod of 26 mm diameter 

Table 23 

Comparison of heat 54062 and heat 47484 products. 

82 
 
 
 
 
 
 
 
 
 
 
 
LIST OF FIGURES 

Figure 1 

Evolution of inclusion composition for martensitic steel grade 420 C1, from CAS. 

Figure 2 

Examples of inclusion found on rolled products of CAS steel 420 C1.  

Figure 3 

Figure 4 

Figure 5 

Inclusion  size  distribution  measured  by  SEM  automatic  image  analysis,  steel  grade 
420C1, CAS steel sample. 

Inclusions  in  lollipop  form  (a)  ladle  after  vacuum  degassing  and  (b)  tundish  (mid 
cast) 

Inclusion size distributions (Normalised) along the rolling route: (a) Inclusion Length 
distribution, (b) Inclusion area distribution (Normalised) along the rolling route 

Figure 6 

Size distributions from heat 48826 of final bar diameter of 45 and 36 mm 

Figure 7 

 Concept sketch of executing Extreme Value Analysis 

Figure 8 

Example  of  distribution  of  inclusion  size  calculated  from  the  measured  largest 
inclusion Feret diameters found in the 24 areas of the samples taken from CAS billet  
Figure A shows the cumulative probability distribution (Gumbel distribution). Figure 
B  shows  the  linearized  distribution,  the  measured  average  and  maximum  value  and 
the  expected  maximum  value  calculated  by  EVA.  The  lower  and  upper  confidence 
bounds are also reported 

Figure 9 

Distribution functions of steel samples (steel grade 420 C1) from tundish to wire rods 
of 13, 8 and 5.5 mm 

Figure 10 

Distribution functions of Sidenor samples on rolled products having final diameter of 
32 mm and 45 mm. 

Figure 11 

Analysis of inclusion features of CAS samples 

Figure 12 

Analysis of inclusion features of Sidenor samples 

Figure 13 

Concept for combined analysis: inclusion information + process parameters 

Figure 14 

Result of linear regression of mean inclusion length 

Figure 15 

Result of MLP neural net modelling of mean inclusion length 

Figure 16 

Result of of linear regression of T1000 EVA value of inclusion length 

Figure 17 

Result of MLP neural net modelling of T1000 EVA value of inclusion length 

Figure 18 

Scheme  of  different  mechanisms  of  inclusion  interaction  with  steel  matrix  and 
deformation during rolling 

83 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 19 

Simplified scheme of model control volume (mesoscale approach) 

Figure 20 

FEM grid for calculation 

Figure 21 

Steel reology property: stress/strain curve according to Hansel Spittel formulation for 
steel 420C1 at different temperatures  

Figure 22 

Results of FEM calculation of inclusion elongation 

Figure 23 

OM  images  of  inclusion  observed  on  5.5    mm  wire  rod  and  comparison  with  FEM 
calculation. In Figure it is reported the inclusion zone in which fragmentation occurs 

Figure 24  

FEM  simulation  of  rolling,  with  steel  matrix  deformation  around  a  fully  rigid 
inclusion. 

Figure 25 

FEM simulation of a cluster of four rigid inclusions during rolling 

Figure 26 

Evolution of inclusion length during rolling, expressed as reduction ratio. (Stell grade 
420C1, CAS) 

Figure 27 

Evolution of inclusion length during rolling, expressed as reduction ratio (Steel grade 
100Cr6, Sidenor) 

Figure 28 

Comparison of statistical model and deterministic model 

Figure 29 

Technical drawing of specimen for fatigue limit test 

Figure 30 

Extreme  values  distribution  of  inclusions  measured  in  the  as  cast  billet  and  on  the 
wire rod (rolled up to 26 mm of diameter). Inclusion size is expressed as square root 
area of transversal section 

Figure 31 

Distribution function and main features obtained from extreme value statistic applied 
to the wire rod of 26 mm, used for fatigue  

Figure 32 

Example of distribution function having the same average value of inclusion size, but 
different maximum expected defect 

Figure 33 

Heat  47484:  Resonant  fatigue  on  cylinders  as  a  function  of  the  local  stress  at  the 
fracture origin. Area of the data “discs” is proportional to the inclusion length in the 
fracture origin. 

Figure 34 

Extreme  values  statistic  on  as  cast  billet  of  industrial  trial  and  comparison  with 
previously performed analysis 

Figure 35 

Characterization  of  inclusion  population  found  after  rolling  at  20  and  5,5  mm  final 
diameter, starting from 160 x 160 billet of steel 420C1 of CAS. 

Figure 36 

Cumulative Failure Probability comparison between heat 54062 and 47484. 

84 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 37 

Fatigue life as a function of the Local Stress at the failure origin. The Area of each 
data “disc” is proportional to the Inclusion EQUIVALENT DIAMETER (Sqr Root of 
the total inclusion area) 

Figure 38 

Fatigue life as a function of the Local Stress at the failure origin. The Area of each 
data “disc” is proportional to the Inclusion LENGTH 

Figure 39 

Fatigue life as a function of the Local Stress at the failure origin. The Area of each 
data “disc” is proportional to the Inclusion WIDTH 

85 
 
 
 
 
 
 
 
ANNEX 1 – LIQUID STEEL SAMPLING 

In order to ensure a representative steel sampling, a preliminary and specific study has been carried 
out by CSM to establish the best strategy in the ladle for correct sampling.  

In fact, during fabrication route steel analysis changes as a consequence of: 

• 
• 
• 
• 

alloying elements addition, 
interaction with slag, 
degassing treatments, 
gas stirring 

For these reasons, steel bath may not be homogeneous and as a consequence, steel samples may not 
be representative of the whole ladle. It is important to evaluate the time necessary, after each step in 
the  fabrication  route,  to  have  a  quite  homogeneous  bath,  also  avoiding  to  take  samples  in  specific 
ladle parts which are not well mixed or affected by local phenomena (as slag emulsification due to 
Ar  stirring).  For  this  reason,  fluid  dynamic  simulations  have  been  performed  to  study  the  most 
representative sampling operating practice. 

Flow field in presence of stirring agitation (plant parameters of CAS have been considered) has been 
simulated with CFD code (ANSYS-FLUENT). 

The Cogne Acciai Speciali 80-ton ladle bath homogenization has been simulated simulating the steel 
composition variation after alloying addition and gas stirring from a porous plug with gas flow rate 
80 l(STP)/min. 
The  evaluation  criterion  to  identify  the  most  reliable  point  for  sampling  was  the  following.  The 
proper point is that where the local composition (e.g., referring to an element) reaches more quickly 
in a stable way the target value.  

86 
 
 
 
 
 
 
 
 
 
A 

B 

C 

D 

E 

Simulated  sampling 
position  respect 
to 
the ladle eye 

Homogenisation time
Homogenisation time

0
0
0
0
1
1
*
*
n
n
o
o
i
i
t
t
a
a
r
r
t
t
n
n
e
e
c
c
n
n
o
o
c
c

s
s
s
s
e
e
l
l
n
n
o
o
i
i
s
s
n
n
e
e
m
m
D
D

i
i

Time (s)
Time (s)

Figure A1-1  Calculated  flow  field  of  CAS  ladle,  during  Ar  stirring  and  simulate  sampling 
position respect to the ladle eye and  mixing time curves as a function of position 

According to the results of the simulation activity, the best position to take steel sample is at 180°C 
from the ladle eye (C position in Figure A1-1). 

87 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ANNEX 2 - EDX ANALYSES ON 100CR6  (SIDENOR STEEL GRADE)  

-SECOND SAMPLING CAMPAIGN: LOLLIPOP (LIQUID) BILLET AND 
PRODUCT. 

Heat48826 

N  Length 

O 

Mg 

Al 

Si 

S 

K 

Ca  Mn 

Total 

(µm) 
2,5 
2,8 
2,9 
3,3 
3,8 
4,1 
5,2 
7,3 
18,8 

1 
2 
3 
4 
5 
6 
7 
8 
9 

h
s
i
d
n
u
T
6
2
8
8
4
H

p
o
o
p
i
l
l
o
L

52,93  21,74  25,33 
23,84  24,56 
51,6 
51,72 
20,49 
4,96 
52,35  27,64  20,01 
3,08 
48,42 
21,14 
14,47  31,73 
53,8 
49,18  10,59  19,58 
19,45 
-- 
47,17 
21,69 
5,34 
47,75 

-- 
-- 
2,51 
-- 
2,59 
-- 
1,71 
2,58 
1,51 

-- 
-- 
2,83 
-- 
0,35 
-- 
-- 
0,25 
0,62 

-- 
-- 
3,35 
-- 
-- 
-- 
-- 
0,25 
-- 

-- 
-- 
14,14 
-- 
24,41 
-- 
18,95 
30,3 
23,09 

-- 
-- 
-- 
-- 
-- 
-- 
-- 
-- 
-- 

100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 

Table A2-1 

EDX analysis of 9 inclusions on the Tundish lollipop from Heat48826 

O  Mg 

Al 

Si 

S 

K 

Ca  Mn 

Total 

N  Length
(µm) 
1,7 
2,2 
2,3 
2,7 
2,8 
2,8 
2,9 
2,9 
3,9 
4,1 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

T
E
L
L
I
B
m
m
5
8
1

–
6
2
8
8
4
H

Table A2-2 

5,27  2,10 4,27
7,80 
12,68

100,00
4,56  100,00
10,16  100,00
2,52 16,48  100,00
100,00
7,96  100,00
100,00
0,85  100,00
100,00
100,00
EDX analysis of 10 inclusions in the 185 mm as cast billet from Heat 48826 

53,79  14,41 31,79
47,70  9,77  24,18 2,16
4,87 
39,95  37,21
32,37  29,20
6,74 
54,99  14,41 30,60
49,09  9,38  23,32 1,45
53,19  16,60 30,21
53,31  20,15 24,94
53,76  14,98 31,26
52,92  15,04 32,04

5,65  0,98 2,17

0,75 

R
A
B
m
m
5
4
∅
–
6
2
8
8
4
H

N  Length 

(µm) 
1,9 
2,3 
2,4 
3,1 
4,2 
4,3 
4,4 
4,5 
4,5 
6,1 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

O 

Al 

S 
0,79 

Mg 
52,37  14,29  30,89 
53,73  14,36  31,91 
53,81  14,29  31,90 
54,23  14,51  31,26 
52,54  12,89  33,02 
54,51  13,43  32,06 
41,47  12,63  21,91  10,07 
37,06 
7,91 
55,02 
53,72  12,85  33,43 
54,04  13,16  28,81 

0,59 

Ti  Mn 
1,65 

Total 
100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
13,92  100,00 
100,00 
100,00 
100,00 

1,08 

2,32 

Table A2-3 

EDX analysis of 10 inclusions in the ∅ 45 mm bar product from Heat 48826 

88 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
N  Length 

R
A
B
m
m
6
3
∅
–
6
2
8
8
4
H

1 
2 
3 
4 
5 
6 
7 
8 
9 

(µm) 
2,2 
2,3 
2,6 
2,7 
2,9 
3,3 
5,9 
6,0 
11,9 

Si 
O  Mg  Al 
52,92 13,19 29,48
-- 
50,3  0,38  17,81 5,76
-- 
54,91 12,24 32,85
-- 
51,33 13,6  27,36
-- 
50,12 40,62 7,97 
-- 
54,17 14,48 31,35
-- 
46,59 11,93 21,09
53,2  15,1  31,7 
-- 
52,38 4,83  20,91 5,39

S 

-- 

--  25,76
-- 

Ca  Mn  Total 
1,54 1,11  1,76  100,00 
--  100,00 
--  100,00 
2,59 0,59  4,52  100,00 
0,78  100,00 
0,51
--  100,00 
-- 
8,75 5,58  6,07  100,00 
--  100,00 
--  100,00 

-- 
16,5 

-- 
-- 

-- 
-- 

Table A2-4 

EDX analysis of 9 inclusions in the ∅ 36 mm bar product from Heat 48826 

89 
 
 
 
 
 
 
 
 
 
 
 
Heat 48825 

T
E
L
L
I
B
m
m
5
8
1
−
5
2
8
8
4
H

Length 
(μm) 
2,9 
4,1 
10,2 
2,8 
2,9 
2,4 
4,9 
1,9 
2,7 
3,2 

N  

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

O  Mg  Al 

Si 

S 

Ca  Mn  Total 

52,28  13,29 26,96 1,55 1,13  4,79 
100,00 
13,13
54,52  8,93  21,97 1,45
100,00 
100,00 
20,80
48,52  6,88  21,88 1,92
43,66  6,87  19,40 2,11 10,46 16,82 0,68  100,00 
100,00 
52,44  8,74  24,96 1,96 1,94  9,96 
100,00 
51,07  7,30  22,31 4,52 1,78  13,02
100,00 
52,51  11,39 24,84
4,69  6,57 
100,00 
54,07  15,74 30,19
100,00 
50,27  7,71  15,86 1,92 10,31 13,93
100,00 
51,96  12,90 29,95 1,78 0,84  2,57 

Table A2-5 

EDX analysis of 10 inclusions in the 185 mm as cast billet from Heat 48825 

N  

O  Mg 

Al 

Si 

S 

Ca  Mn  Total 

Length 
(mm) 
3,1 
3,2 
3,4 
3,4 
3,5 
3,6 
4,4 
5,0 
7,9 
10,5 

R
A
B
m
m
6
3

–
5
2
8
8
4
H

Table A2-6 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
EDX analysis of 10 inclusions in the ∅ 36 mm bar product from Heat 48825 

15,95 18,12 4,15  100,00 
7,95  1,75  100,00 
6,77 
100,00 
0,67 
8,47  10,50 1,87  100,00 
100,00 
100,00 
100,00 
8,19 
100,00 
4,85  1,73  100,00 
100,00 
27,04

38,61  5,89  17,28
51,44  9,46  21,72 0,91
53,88  14,47 30,98
51,74  5,60  21,82
54,65  14,34 31,01
52,13  8,91  26,35 2,35
52,37  22,70 24,93
49,08  11,52 28,98 1,80
53,58  9,82  25,69
48,82  0,39  16,80 6,95

0,43 
4,33 

8,29 

1,97 

90 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
 
 
Heat 48827 

N  

T
E
L
L
I
B
m
m
5
8
1
–
7
2
8
8
4
H

Table A2-7 

R
A
B
m
m
5
4

–
7
2
8
8
4
H

Table A2-8 

O  Mg 

Al 

Si 

S 

Ca  Mn  Total 

Length 
(μm) 
2,1 
2,4 
2,6 
3,2 
3,3 
3,5 
4,1 
8,9 
11,2 
13,9 

1,14 
3,97  0,30

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
EDX analysis of 10 inclusions in the 185 mm as cast billet from Heat 48827 

19,56 23,54 4,31  100,00 
0,81 
0,95 
100,00 
6,61  2,52  100,00 
7,87 
100,00 
6,68  1,29  100,00 
100,00 
0,27 18,64 12,38 7,13  100,00 
100,00 
24,66
100,00 
29,36
100,00 
25,63

34,30  17,15
50,22  43,75
47,32  20,06 15,62
48,16  51,84
46,39  31,56
49,80  2,50  20,95 1,58
36,06  25,52
47,63  3,51  23,11 1,09
45,24  1,66  22,05 1,69
50,01  3,03  18,47 2,86

7,79 
0,28  24,89

6,29 

N  

O  Mg 

Al 

Si

S 

Ca  Mn 

Total 

Length 
(μm) 
2,4 
2,4 
2,5 
2,7 
2,7 
2,9 
4,2 
5,6 
6,9 
9,1 

4,55 
19,20 1,02
1,26 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
EDX analysis of 10 inclusions in the ∅ 45 mm bar product from Heat 48827 

51,22  15,01 27,23
37,63  18,89 15,79
53,34  13,95 29,61
54,80  13,86 31,34
51,36  25,92 22,72
38,88  17,13 20,24
51,74  16,55 30,25
52,08  16,43 31,49
53,33  14,95 31,72
52,69  13,72 31,08

1,99  100,00 
7,47  100,00 
1,84  100,00 
100,00 
100,00 
10,09  100,00 
100,00 
100,00 
100,00 
0,71  100,00 

13,66
1,46 

0,67  1,13

91 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
 
Heat 49323 

T
E
L
L
I
B
m
m
5
8
1
–
3
2
3
9
4
H

Table A2-9 

Length 
(μm) 
2,0 
2,7 
2,9 
3,4 
3,5 
3,8 
4,7 
5,1 
7,7 
9,7 

N  

O  Mg 

Al 

Si 

S 

K 

Ca  Mn  Total 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

100,00 
100,00 
100,00 
100,00 
100,00 
100,00 
0,97  100,00 
7,42  6,40  100,00 
100,00 
100,00 
EDX analysis of 10 inclusions in the 185 mm as cast billet from Heat 49323 

54,17  14,20 31,63
54,47  14,21 31,32
54,22  14,45 31,33
53,63  13,52 30,58
0,76 0,43
53,97  12,13 28,45 0,60 0,64 0,92
52,54  11,00 30,05 2,02 0,39 0,71
53,12  14,40 30,50
45,47  8,26  23,53 1,39 7,02 0,51
53,90  6,82  39,28
49,35 

1,08 
3,29 
3,29 

25,87 1,81

22,97

1,01

R
A
B
m
m
2
3

–
3
2
3
9
4
H

N  

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

Length 
(μm) 
2,9 
2,9 
3,2 
3,8 
3,8 
4,5 
5,0 
5,2 
5,7 
5,9 

O  Mg 

Al 

Si

S 

K 

Ca  Mn  Total 

55,05  14,29 30,66
54,28  14,07 31,65
51,36  15,25 33,39
53,42  14,71 31,87
53,62  11,73 34,65
54,30  12,69 29,02
54,94  14,28 30,78
52,33  14,84 32,83
53,42  13,60 32,22
48,17  5,53  24,13

100,00 
100,00 
100,00 
100,00 
100,00 
1,46 0,79  100,00 
100,00 
100,00 
0,31
100,00 
8,23 1,58 6,82 5,54  100,00 

0,45

1,74

Table A2-10  EDX analysis of 10 inclusions in the ∅ 32 mm bar product from Heat 49323 

92 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
  
  
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
 
Heat 49324 

T
E
L
L
I
B
m
m
5
8
1
–
4
2
3
9
4
H

N  

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

Length
(μm) 
1,8 
2,1 
2,1 
2,7 
2,7 
3,9 
4,4 
4,5 
4,6 
5,8 

O  Mg 

Al 

Si 

S 

K 

Ca  Mn 

Total 

35,97  11,16 20,97
26,03  10,91 20,96
50,52  15,95 18,86
46,46  10,54 20,67 1,35
52,64  8,13  17,47 3,59
50,40  6,46  23,44 3,06
53,27  6,90  23,36 1,98
53,67  14,78 31,55
52,92  15,11 31,97
53,86  6,82  18,14 1,84

7,05  12,32  100,00
12,53
9,19  15,10  100,00
17,81
1,34  100,00
6,32 
7,01 
6,15  100,00
6,01 
8,82 
100,00
7,12  1,29
9,76 
100,00
0,61  2,89 13,14
100,00
0,28  1,28 12,93
100,00
100,00
100,00

12,26

7,08 

Table A2-11  EDX analysis of 10 inclusions in the 185 mm as cast billet from Heat 49324 

R
A
B
m
m
6
3

–
4
2
3
9
4
H

N  

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

Length
(μm) 
3,6 
3,1 
2,6 
4,8 
4,1 
4,9 
3,8 
2,7 
4,9 
3,7 

O  Mg 

Al 

Si 

S 

K 

Ca  Mn  Total 

0,76  0,73

0,30 

53,99  14,29 30,64
53,85  14,72 31,43
55,08  10,39 31,75 1,29
54,32  14,60 31,08
37,62  5,19  13,71 0,29 16,37
50,11  7,05  23,72 4,05
2,28 
54,89  14,12 30,99
55,91  13,88 30,21
53,33  14,11 32,56
53,82  14,71 31,47

0,78  100,00
100,00
100,00
100,00
18,97  7,85  100,00
11,71  1,08  100,00
100,00
100,00
100,00
100,00

Table A2-12  EDX analysis of 10 inclusions in the ∅ 36 mm bar product from Heat 49324 

93 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
  
  
  
  
  
 
 
 
 
ANNEX 3 - EVA ANALYSIS OF CAS SAMPLES 

Details of inclusion analysis of steel samples from steel 420C1 are reported.  

Details of Analysis carried out of steel samples in WP1 

samples 

sample  

420 C1 

analysis 

AOD reduction phase (A) 

1 spemis 

OM + SEM + AIA 

ADO  desulfurization  phase 
(B) 

1 spemis 

OM + SEM+ AIA 

end of LF (C) 

2 spemis 

OM + SEM+ AIA 

treatment  “Tundish 

Argon 
condition” (D) 

2 spemis 

OM + SEM+ AIA 

as  cast  billets  (first  billet, 
mid casting, end of casting) 

18 slices 

OM  +  SEM  +  AIA  (12  samples 
in symmetrical position prepared 
from 3 slices) 

rolled billet 

72 samples 

Table A3-1List of collected samples for WP1 activities 

 OM+SEM+AIA :  

13mm (12 samples) 

8 mm  (48 samples) 

5,5 mm (12 samples) 

%

50

45

40

35

30

25

20

15

10

5

0

60

50

40

%

30

20

10

0

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-
100

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-
100

particle size (m icron)

particle size (m icron)

94 
 
 
 
 
 
 
 
AOD, reduction 

AOD desulp. 

50

45

40

35

30

25

20

15

10

5

0

%

90

80

70

60

50

40

30

20

10

0

0-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90 91-100

particle size (micron)

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-
100

particle size (m icron)

LF 

Tundish 

Figure A3-1 AIA analysis of samples collected during steelmaking process (150 mm2 analysed) 

%

90

80

70

60

50

40

30

20

10

0

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-
100

particle size (micron)

Figure A3-2 AIA analysis of as cast billet (150 mm2 analysed) 

95 
 
 
 
 
 
 
 
Details of Analysis carried out in WP2  

Analysed samples: extreme values analysis of the same samples collected in WP 1 

)
)

P
(
n
l
-
(
n
l
-
=
y

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

-1,0

-2,0

D

C

A

B

sample A

sample B

sample C

sample D

0

20

40

60

80

100

defect size (micron)

AOD reduction phase (A) 

average inclusion size 

29  micron 

maximum inclsuion size 

45  micron 

maximum expected size 

88  micron 

ADO desulfurization phase (B) 

average inclusion size 

32  micron 

maximum inclsuion size 

52  micron 

maximum expected size 

83  micron 

end of LF (C) 

average inclusion size 

30  micron 

Argon  treatment  “Tundish  condition” 
(D) 

maximum inclsuion size 

52  micron 

maximum expected size 

80  micron 

average inclusion size 

18  micron 

maximum inclsuion size 

34  micron 

maximum expected size 

64  micron 

Figure A3-4 EVA distributions for steel samples in AOD, LF and tundish (24 four fields observed) 

96 
 
 
 
 
 
estimated LEVD

4

95% confidence lower bound

95% confidence upper bound

3

data

Lineare (estimated LEVD)

y = 0,25x - 2,5

)
)

P
(
n
l
-
(
n
l
-
=
y

2

1

0

-1

-2

0

10

20

30

40

defect size, μm

average 

maximum measured 

maximum expected 

12 mcron 

16 micron 

37 micron 

Figure A3-5 EVA analysis of as cast billet (24 four fields observed) 

estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,025x - 2,5

0

100

200

300

400

4

3

2

1

0

)
)

P
(
n
l
-
(
n
l
-
=
y

-1

-2

defect size, μm

average 

maximum measured 

maximum expected 

120 mcron 

200 micron 

370 micron 

Figure A3-6 EVA analysis of 13 mm diameter wire rod (24 four fields observed) 

97 
 
 
 
 
4
estimated LEVD

95% confidence lower bound

y = 0,01x - 1,8

95% confidence upper bound

3

data

Lineare (estimated LEVD)

)
)

P
(
n
l
-
(
n
l
-
=
y

-200

2

1

0

-1

-2

0

200

400

600

800

defect size, μm

average 

230  mcron 

maximum measured 

470  micron 

maximum expected 

870  micron 

Figure A3-7 EVA analysis of 8 mm diameter wire rod 

average value (micron) 

D 13 mm 

D 8  mm 

D 5.5 mm 

54 

68 

107 

Figure  A3-8  Measured  inclusion  size  (150  mm2  analysed)  on  wire  rod  samples  of  three  different 
diameters of CAS samples. Each point on Figure represent a measured inclusion 

98 
 
 
 
 
  
4
estimated LEVD

95% confidence lower bound

y = 0,0167x - 1,8333

95% confidence upper bound

3

data

Lineare (estimated LEVD)

2

1

0

-100

0

100

200

300

400

500

)
)

P
(
n
l
-
(
n
l
-
=
y

-1

-2

average 

maximum measured 

maximum expected 

defect size, μm

140 mcron 

250 micron 

540 micron 

Figure A3-9 EVA analysis of 5.5 mm diameter wire rod (24 four fields observed) 

99 
 
 
 
 
Details of Analysis carried out in WP3 

Sampled steel: 420C1 

List of collected samples: 

•  As cast billet, 3 slices 
•  Wire rods of diameters: 

- 
- 
- 
- 
- 
- 
- 

26 mm 
14 mm 
12 mm 
9.6 mm 
7.5 mm 
6.5 mm 
5.5 mm 

Performed analysis: 

•  Automatic image analysis by scanning electron microscope 
•  Extreme values analysis by optical microscope 

6

5

4

3

2

1

0

)
)

P
(
n
l
-
(
n
l
-
=
y

-10

0

10

20

30

40

50

60

70

-1

-2

95% conf idence lower bound

95% conf idence upper bound

defect size, μm

data

distribution function

average 

maximum measured 

maximum expected 

16 micron 

31 micron 

25 micron 

Figure A3-10 EVA analysis of as cast billet  (24 four fields observed) 

100 
 
 
 
 
estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,0422x - 3,2397

0

50

100

150

200

250

4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

defect size, μm

average 

maximum measured 

maximum expected 

91 micron 

160 micron 

270 micron 

Figure A3-11 EVA analysis of 26mm wire rod  (24 four fields observed) 

estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,022x - 3,1602

0

100

200

300

400

500

4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

defect size, μm

average 

maximum measured 

maximum expected 

170 micron 

280 micron 

460 micron 

Figure A3-12 EVA analysis of 14 mm wire rod  (24 four fields observed) 

101 
 
 
 
 
 
estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,0193x - 2,7821

0

100

200

300

400

500

4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

defect size, μm

average 

maximum measured 

maximum expected 

175 micron 

290 micron 

500 micron 

Figure A3-13 EVA analysis of 12 mm wire rod  (24 four fields observed) 

diam 9,6

x_lower

x_upper

plot_pos

diam 9,6

diam 9,6

0

100

200

300

400

500

600

700

)
)
P
(
n
l
-
(
n
l
-
=
y

4

3

2

1

0

-1

-2

defect size, mm

average 

maximum measured 

maximum expected 

190 micron 

295 micron 

micron 

Figure A3-14 EVA analysis of 9.6  mm wire rod  (24 four fields observed) 

102 
 
 
 
 
estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,0127x - 2,9731

0

200

400

600

800

4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

defect size, μm

average 

maximum measured 

maximum expected 

280 micron 

430 micron 

780 micron 

Figure A3-15  EVA analysis of 7.5 mm wire rod  (24 four fields observed) 

estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,0118x - 3,0646

0

200

400

600

800

4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

average 

maximum measured 

maximum expected 

defect size, μm

300 micron 

450 micron 

840 micron 

Figure A3-16 EVA analysis of 6.5  mm wire rod  (24 four fields observed) 

103 
 
 
 
 
estimated LEVD

95% confidence lower bound

95% confidence upper bound

data

Lineare (estimated LEVD)

y = 0,0115x - 2,8161

0

200

400

600

800

4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

average 

maximum measured 

maximum expected 

defect size, μm

300 micron 

500 micron 

845 micron 

Figure A3-17 EVA analysis of 5.5 mm wire rod  (24 four fields observed) 

104 
 
 
 
 
 
Analysis WP4 

Sampled steel: 420C1 

List of collected samples: 

•  As cast billet, 3 slices 
•  Wire rods of diameters: 
•  20 mm 
•  5.5 mm 

Performed analysis: 

Automatic image analysis by scanning electron microscope 

Extreme values analysis by optical microscope 

estimated LEVD

4

95% confidence lower bound

95% confidence upper bound

data

3

Lineare (estimated LEVD)

2

1

0

y = 0,129x - 1,6758

-10

0

10

20

30

40

50

60

)
)

P
(
n
l
-
(
n
l
-
=
y

-1

-2

defect size, μm

average inclusion size 

18  micron 

maximum inclsuion size 

38  micron 

maximum expected size 

66  micron 

Figure A3-18 EVA distribution of as cast billet 

105 
 
 
 
 
 
 
4

3

2

1

0

-1

-2

)
)

P
(
n
l
-
(
n
l
-
=
y

0

50

100

150

200

defect size, μm

average inclusion size 

77  micron 

maximum inclsuion size 

143  micron 

maximum expected size 

211  micron 

Figure A3-19 EVA distribution of wire rod 20 mm diameter 

4

3

2

1

0

)
)

P
(
n
l
-
(
n
l
-
=
y

-1

-2

0

200

400

600

800

1000

1200

defect size, μm

average inclusion size 

260  micron 

maximum inclusion size 

560  micron 

maximum expected size 

970  micron 

Figure A3-20 EVA distribution of wire rod 5.5  mm diameter 

106 
 
 
 
 
250,00

200,00

)
n
o
r
c
i
m

(

t
h
g
n
e

l

n
o
i
s
u
l
c
n

i

150,00

100,00

50,00

0,00

average value (micron) 

as cast billet 

D 20 mm 

D 5,5 mm 

16,50 

60,40 

104,50 

Figure  A3-21  AIA  analysis  of  as  cast  billet  and  wire  rods  of  20  and  5.5  mm  diameters.  Each  point 
represent a measured inclusion.(150 mm2 analysed) 

107 
 
 
 
 
  
 
 
 
 
 
 
 
 
ANNEX 4 – SAMPLE PREPARATION FROM LF AND TUNDISH SAMPLING 

The experimental method 
In this first set of analysis, in order to have a reference situation, the standard ASTM procedure has 
been applied: 
- 
- 

standard metallographic preparation has been used, 
24 fields are analysed having size of 150 mm2; 24 four fields can be examined examining 8 
fields and then re-polishibg them two other times, up to 24 observations;  
inclusion size expressed with diameter in case of liquid inclusions and with length in case of 
elongated inclusions. 

- 

The  sampling  activity  of  steel  samples  in  LF  or  tundish  does  not  permit  to  collect  a  number  of 
samples (lollipop or spemis) as large as required by AST standard (minimum eight samples). In fact, 
only one or two samples can be reasonably collected in a short time during process. Figure 21 shows 
the  scheme  of  cutting  and  sample  preparation  of  TOS  or  lollipop.  The  steel  sample  is  cut  into  two 
parts in order to obtain two samples to be observed (Figure below).  

Figure  A4-1  Scheme of cutting and sample preparation from Spemis or lollipop 

In  order  to  evaluate  if  the  collection  of  a  small  number  of  sample  can  give  statistically  significant 
results, a preliminary activity has been carried out. Three lollipops have been sampled in LF and three 
Gumbel distribution function have been calculated: 
- 

taking 2 samples from one lollipop (cut into two parts) and re-polishing eleven times the two 
samples, to have 24 samples observed; 
taking 4 samples from two lollipops and re polishing five times up to 24 samples observed;   
taking 6 samples from three lollipops and re polishing three times up to 24 samples observed;   

- 
- 

Figure A4-2 reports the distribution function for the three examined cases. The confidence bands (at 
95%)  have  been  reported  only  for  the  first  cases,  to  avoid  too  many  curves  on  the  same  graph 
generating confusion. In any case the distribution functions are very close and are comprised within 
the confidence bands.  

108 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
)
)

P
(
n
l
-
(
n
l
-
=
y

4

3

2

1

0

-1

-2

3 lollipop

1 lollipop

2 lollipops

0

10

20

30

40

50

60

95% confidence
lower boud

defect size, μm

95% confidence
upper boud

Figure  A4-2  Extreme values statistic of inclusions taken in LF, in three cases: 

- 

- 

- 

taking two samples from one lollipop (cut into two parts) and re-polishing eleven 
times the two samples, to have tweenty four samples observed; 
taking  four  samples  from  two  lollipops  and  re  polishing  five  times  up  to  twenty 
four samples observed;   
taking six samples from three lollipops and re polishing three times up to twenty 
four samples observed;   

In case of samples from a billet slice, four samples are taken in symmetrical position, as reported in 
Figure below. 

D 

A 

C 

B 

Figure A5-3 

 Scheme of sampling from billet slice of CAS 

Indications for sample preparation. 

Sample preparation has been carried out according to standard practice (ASTM E3).  

The main aspects related to sample preparation are: 

109 
 
 
 
 
 
 
 
 
 
 
- 

- 

- 

- 

In  cutting  the  metallographic  specimen  from  the  main  body  of  the  material,  care  must  be 
exercised to minimize altering the structure of the metal 
Sample size should not exceed 12 x 25 mm, otherwise it would be difficult to prepare a flat 
surface satisfactory for microscopic observation 
Cleanliness  during  specimen  preparation  is  essential.  All  greases,  oils,  coolants  and  residue 
from  cut-off  blades  on  the  specimen  should  be  removed  by  some  suitable  organic  solvent. 
Failure to clean thoroughly can prevent cold mounting resins from adhering to the specimen 
surface.  Ultrasonic  cleaning  may  be  effective  in  removing  the  last  traces  of  residues  on  a 
specimen surface 
Sample preparation and cleaning is carried out with proper solvent, water is not used to avoid 
dissolution of inclusion component (as calcia) 

Specific indication for automatic SEM analysis (ASTM E768) 

The inclusions in the plane of polish must be fully preserved and clearly visible. Preparation should 
not produce excessive relief around the perimeter of the inclusions that would exaggerate the size and 
number of inclusions on the plane of polish.  
In many cases, the preparation of specimens for inclusion rating is more readily performed after the 
specimens  have  been  hardened  by  a  suitable  heat  treatment  procedure  (austenite,  quench  to  fully 
martensitic structure, temper at a relatively low temperature). 
SEM observation must be carried out as soon as possible after sample preparation, in order to avoid 
the presence of dust of local oxidation of the sample which may interfere with the analysis. Even if 
the presence of particles which are not ‘inclusions’ can be counted by the system and eliminated with 
successive filtering of the data (having performed EDS analysis), this in any case increases analysis 
time and generates bigger data files. 
For similar reasons, the presence of porosity on the sample must be avoided. It is important to observe 
preliminarly the sample by optical microscope and individuate a representative area. 

110 
 
 
 
 
 
ANNEX 5: FATIGUE TESTST ON 100CR6 GRADE 

ROLLING CONTACT FATIGUE TEST CONDITIONS 

The  test  conditions  are  the  usual  for  the  bearing  steel  grades  in  order  to  highlight  the  inclusion  effect  under 
rolling contact performance. 

The  Figure  A5-1  shows  the  RCF  facility  at  the  Sidenor  lab.  The  test  arrangement  consist  on  a  commercial 
bearing  (spherical  rollers)  transmitting  the  load  applied  through  a  lever  on  a  flat  washer  (the  sample).  The 
maximum Contact Pressure (Hertz Pressure: Po) is set by the proper combination of rollers (balls) and applied 
load, as indicated in the Table A5-1, in which it is indicated the test conditions for the tests at Po=4,2 GPa. 

Figure A5-1: RCF test facility at the Sidenor lab. 

P Hertz: Po 
(GPa) 

N of Rollers 

Applied Load 

(Kg) 

Max Test Duration 

(Runnout Criteria) 

Pulses 

Days 

4,2 

10 

59 

375.000.000 

34,7 

Table A5-1: Experimental conditions for the RCF test. 

Considering  that  the  rolling  path  diameter  is  25  mm  in  radius  and  about  0,45  mm  in  width  and  that  typical 
maximum depth of the origin of the pitting generation in an inclusion is about 120 μm, the total volume tested 
per specimen is about 8,5 mm3. 

111 
 
 
 
 
 
 
 
COMPRESIVE AXIAL RESONANT FATIGUE TEST 

Setting up of the Compressive Resonant Fatigue test on Tubes 

The test conditions that have been used in the experimentations are the following: 

Alternate loading: Pmin= -1 KN, Pmax= -22 KN 

These conditions have been selected since they show the higher probability to detect the inclusions in the steel. 

Under these conditions, the simulation calculations performed with the DEFORM software shows how the most 
stressed (tensile) areas are located in the upper and lower internal surface and in the lateral external surfaces, as 
shown in the Figure A5-2. 

Figure A5-2: Stress distribution in the tubular sample at the maximum applied load (P= 22KN). 

From  the  previous  figures  it  is  noticed  how  the  tensile  stresses  are  located  in  a  narrow  (angular)  area  in  the 
internal surface of the sample, while the tensile stresses appears in a more extended region. This is shown in the 
Figure A5-3, in which it is represented the maximum stress distribution in a central section of the sample as a 
function of the angular position (origin on the top). It is represented the maximum stress in the internal radius 
(red line) and the external radius (blue line). This is in agreement with the results of the test in which the failures 
occurred in the angular regions highlighted in the Figure A5-3. 

112 
 
 
 
 
 
 
 
 
 
 
 
FigureA5-3: Maximum stress vs. angular position (origin on the top) in the central section of the sample. 
Blue line: external radius; Red line: internal radius. 

Figure A5-4: Heat 47484, D60mm: Histogram of the angular position of the failures  

As  the  failures  occur  at  different  angular  positions,  it  has  been  calculated  the  Stress  profile  generated  by  the 
compressive load along the specimen at different angular positions. In the following figures it is represented the 
Stress  profile  along  the  specimen  length  at  three  critical  positions:  0º  (Figure  A5-5),  90º  (Figure  A5-6)  and 
180º (Figure A5-7) (angular positions measured from the top of the sample). According to these calculations the 
stress level at which the fracture is initiated is very dependent of the position on the specimen. These data have 
been obtained from the FE simulation performed with the DEFORM software. 

113 
 
 
 
 
 
Figure A5-5: Stress distribution along the internal surface of the specimen at 0º (Top of the sample), in 
the surface and 0.1 mm in depth 

Figure A5-6: Stress distribution along the external surface of the specimen at 90º (equatorial plane of the 
sample), in the surface and 0.1 mm in depth 

114 
 
 
 
Figure A5-7: Stress distribution along the internal surface of the specimen at 180º (Bottom of the sample), 
in the surface an 0.1 mm in depth 

The test specimens were cylinders extracted in the longitudinal direction in relation to the bar axis. In at way, 
inclusions are parallel to the cylinder walls. The external and internal diameters were 50mm and 40mm (wall 
thickness 5 mm). The samples length was 50 mm.  

An estimation of the tested volume is done considering the angular position range of the failures (+-15º for the 
equatorial  fractures,  and  +-1º  when  failures  occur  in  the  top  or  bottom  of  the  specimen).  Typically,  the 
maximum  depth  at  which  the  failure  is  originated  is  about  40μm.  Under  these  assumptions,  the  total  volume 
tested in a cylindrical specimen (that is, the volume under tensile stresses in which an inclusion has been found 
to be the cause of the fracture) is about 1,4 mm3 in the top or in the bottom (2,8 mm3 in total) and about 26 mm3 
in  the  equatorial  plane  (about  52  mm3  in  total).  Considering  the  top/bottom  and  equatorial  zones,  the  total 
tested volume per specimen is estimated to be about 55mm3. 

FATIGUE TEST RESULTS OBTAINED ON HEAT 47484 

ROLLING CONTACT FATIGUE on Heat 47484 rolled to 100mm and 60mm  

In the following tables it is summarized the fatigue behaviour of each of the RCF sets of 20 specimens tested 
from each of the two bar diameters rolled from heat 47484. Samples in which a pitting was generated before the 
runnout limit (fixed on 375x106 pulses) were fractured by the pitting section, and fracture surface analysed by 
SEM  to  identify  the  origin  of  the  failure.  When  an  inclusion  was  found  to  be  in  the  origin  of  the  pitting 
generation, EDX analysis was performed to identify its composition. In the tables it is included the inclusions 
composition, length and width. The Area is estimated as the product (length x width). 

115 
 
 
 
 
 
 
It should be mentioned that some specimens did not fractured by the pitting section, so it was not possible to 
state whether the cause of the pitting was related to an inclusion. 

Nº 

Pitting/ 
Runnout 

Fatigue 
Life 
(Cycles) 

Pitting 
Origin 

Inc. 
Length
 (µm) 

Inc. 
Width
 (µm) 

Area 
 (sqr 
µm) 

SqrRoot 
(Area) 
 (µm) 

1  PITTING 

33.525.000   

2  PITTING 

51.615.000 

Al2O3 + (4% MgO+ 
3%CaO) 

331 

21 

6.951 

3  PITTING 

74.700.000 

4  PITTING 

121.095.000 

Al2O3 

Al2O3 

174 

260 

8 

14 

1.340 

3.640 

83 

37 

60 

5  PITTING 

153.450.000   

6  PITTING 

198.270.000   

7-10  RUNNOUT 

375.750.000    

11  RUNNOUT 

376.200.000    

12  RUNNOUT 

376.515.000    

13  RUNNOUT 

376.560.000    

14  RUNNOUT 

377.775.000    

15  RUNNOUT 

379.845.000    

16  RUNNOUT 

387.000.000    

17  RUNNOUT 

388.215.000    

18  RUNNOUT 

391.500.000    

19  RUNNOUT 

460.125.000    

TableA5-2: RCF on D60mm from Heat 47484. Specimens fatigue life and failure cause. 

Nº 

Pitting/ 
Runnout 

Fatigue 
Life 
(Cycles) 

Pitting 
Origin 

Inc. 
Length
 (µm) 

Inc. 
Width
 (µm) 

Area 
(sqr µm) 

SqrRoot
(Area) 
 (µm) 

1  PITTING 

13.905.000 

Al2O3 +8%MgO 

284 

14 

3.976 

63 

2  PITTING 

14.895.000 

3  PITTING 

35.325.000 

4  PITTING 

35.775.000 

Al2O3 

Al2O3 

5  PITTING 

82.980.000 

Al2O3 +7%MgO 

200 

320 

606 

10 

9 

20 

2.000 

2.944 

45 

54 

12.120 

110 

116 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
6  NOT VAL. 

89.910.000 

7  PITTING 

91.800.000 

Al2O3 

8  PITTING 

100.800.000 

Al2O3 +2%MgO 

813 

168 

15 

11 

11.789 

109 

1.878 

43 

9  PITTING 

105.435.000 

10  PITTING 

120.285.000 

Al2O3 +9%CaO 

342 

9 

3.078 

55 

11  PITTING 

149.535.000 

 12  PITTING 

275.040.000 

Al2O3 
+7%MgO+1%CaO 

1.750 

47 

82.600 

287 

13-15  RUNNOUT 

375.750.000    

16  RUNNOUT 

377.325.000    

17  RUNNOUT 

377.550.000    

18  RUNNOUT 

391.950.000    

19  RUNNOUT 

394.020.000    

20  RUNNOUT 

397.530.000    

Table A5-3: RCF on D100mm from Heat 47484. Specimens fatigue life and failure cause. 

The  Weibull  analysis  of  the  Fatigue  data  allows  estimating  the  L10  index  in  order  to  compare  the  fatigue 
performance. The index Ln indicates the expected fatigue life at which the n% of the specimens in a population 
is expected to fail. The higher is the value of Ln, the higher the fatigue performance of the material under the 
tested conditions is. 

The results are summarised in the Table A5-4 , in which it is evidenced a significant improvement of both index 
for the D60 mm product compared to the D100 mm product. 

Diam (mm) 

100 

60 

D60 vs D100: 

% Improvement 

L10 
(Cycles)  

19.452.500 

68.726.300 

253% 

TableA5-4: Weibull analysis from RCF test results on D100 and D60mm bars from heat 47484 

117 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
This improvement in the fatigue life as the reduction degree increases it is also shown in the Figure 
A5-8,  in  which  the  Cumulative  Failure  Probability  versus  the  individual  specimens  Fatigue  Life  is 
represented. 

In the following Figure A5-9 & Figure A5-10, it is illustrated a representative failure originated in an 
alumina  inclusion  on  specimens  from  60  mm  diameter  product  and  100mm  diameter  product 
respectively. 

Figure A5-8: Cumulative Failure probability for the FCR specimens from Heat 54062. 

Figure A5-9: Heat 47484, product diameter 60 mm, specimen fatigue life 121.095.000 cycles. 

118 
 
 
 
 
 
 
 
Figure A5-10: Heat 47484, product diameter 100 mm, specimen fatigue life 13.905.000 cycles.  

Regarding the inclusions characterisation on the RCF specimens it should be mentioned: 

- 

- 

due to the method to fracture the specimens, in some occasions it is not accessible the inclusion in the 
origin of the pitting. Or if it is accessible, part of it (or even the complete inclusion) may have been 
lost. These difficulties have caused problems the determination of the inclusion chemical composition. 

the  inclusion  size  is  usually  underestimated.  In  fact,  on  the  one  hand,  part  of  the  inclusion  can  be 
hidden or even lost when fracturing the sample and, on the other hand, the samples are extracted from 
transversal  sections  of  the  bars.  In  this  second  case,  thus,  the  inclusions  near  the  surface  usually 
intercepts the sample surface, i.e, the original inclusion in the bar product may be longer than the actual 
observed one. 

COMPRESSIVE  HIGH  FREQUENCY  (RESONANT)  FATIGUE  TEST  on  Heat  47484  rolled  to  100mm  and 
60mm   

Compressive high frequency (resonant) fatigue tests were conducted on hollow cylinders (tubes) on heat 47484 
rolled to 100mm and 60mm. The test results are summarised in the Table A5-5 for the D60mm product and on 
Table A5-6 for the D100mm product. 

Failure Mode 

Inclusion Characteristics 

Nº 

Fatigue Life 
(Cycles) 

Failure/ 
Runnout Angular  
Position (º) 

Inc./
Matrix

Analysis (EDX) 

Length 
(µm) 

Width 
(µm) 

Area 
(µm^2) 

Diam 
Eqiv.
(µm) 

1 

2 

3 

4 

12.340 

12.951 

19.365 

25.627 

F 

F 

F 

F 

-177 

M 

180 

-87 

180 

Inc  Al2O3 (Trazes Mg/Na/K/Ca)

14.350 

45 

645.750 

907 

Inc 

Inc 

Al2O3 (Traces Ti, P) 

5.000 

35 

175.000 

472 

Al2O3 

460 

20 

9.200 

108 

119 
 
 
 
 
 
 
  
  
  
  
  
5 

6 

7 

8 

9 

35.159 

58.596 

76.984 

85.827 

87.414 

10 

92.425 

11 

136.921 

12 

154.262 

13 

163.550 

14 

256.032 

15 

293.046 

16 

361.553 

17 

455.809 

18 

869.788 

19 

1.511.586 

20 

1.829.906 

21 

2.422.987 

22 

3.585.828 

23 

3.603.405 

24 

4.667.669 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

25 

7.016.405 

R 

93 

-75 

0 

-82 

-90 

98 

-79 

112 

-100 

85 

180 

86 

94 

110 

-110 

108 

180 

180 

0 

180 

X 

Inc 

M 

Inc 

Inc 

Inc 

Inc 

Inc 

M 

M 

M 

Inc 

Inc 

M 

M 

M 

M 

Inc 

Inc 

Inc 

Inc 

X 

Al2O3 

8.700 

12 

104.400 

365 

Al2O3 (Mg) 

1.140 

Al2O3 (Mg/Na/K) 

700 

9 

6 

10.260 

114 

4.200 

73 

Al2O3 (Na/Mg) 

1.310 

10 

13.100 

129 

Al2O3 (Mg, P) 

2.475 

0 

0 

Al2O3-MgO (Na) 

225 

7 

1.575 

45 

Al2O3 (Mg/Si) 

8.600 

15 

129.000 

405 

Al2O3 

3.060 

10 

30.600 

197 

Al2O3-SiO2-CaO 

570 

20 

11.400 

120 

 Al2O3-MgO  

Al2O3 (Na/Mg) 

1.100 

Al2O3-CaO-(Si2O3) 

700 

8 

5 

8.800 

106 

3.500 

67 

Table A5-5: Fatigue results on D60 from heat 47484  

Nº 

Fatigue 
Life  
(Cycles) 

Failure
/ 
Runno
ut 

1 

2 

3 

4 

8.773 

19.455 

24.516 

30.191 

F 

F 

F 

F 

Failure Mode 

Inclusion Characteristics 

Angular  
Position 
(º) 

Inc/
Matr
ix 

Analysis (EDX) 

Length 
(μm) 

Widt
h 
(μm) 

4 

Inc 

MgAl+Mg, K, Ti, Na 

2.235 

180 

Inc 

Al (Mg (Si, P, K, Ca)) 

1.150 

0 

0 

Inc 

Inc 

Mg, Al (Na, K, Ca) 

1.297 

MgAl (Ti, Na, K) 

2.500 

90 

40 

31 

75 

Area 
(μm^2
) 

201.15
0 

Diam 
Eqiv.
(μm) 

506 

46.000 

242 

40.207 

226 

187.50

489 

120 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
5 

6 

7 

8 

9 

41.924 

51.691 

60.107 

64.217 

72.165 

10 

72.205 

11 

107.658 

12 

120.679 

13 

161.879 

14 

229.841 

15 

291.245 

16 

512.248 

17 

634.078 

18 

925.878 

19 

1.313.488 

20 

3.904.790 

21 

4.065.930 

22 

8.034.445 

23 

8.089.034 

24 

9.250.169 

25 

11.203.12
1 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

F 

R 

R 

R 

R 

0 

Al2O3+CaO+ 

(MgO/SiO2)(K;Na;Sca) 

2.350 

55 

129.25
0 

406 

Inc 

Inc 

Inc 

0 

0 

0 

4 

Al-(MgAlSiCa)+Sca 

825 

Al2O3, (Mg), P 

1.625 

Inc  MgAl+Al(Mg, Na, Ca) 

1.175 

-3 

180 

Inc 

Inc 

MgAl+Al, Mg, K, Ca  

AlSiCa+Sca 

0 

Inc 

AlCa, MgAl, Ti, Ca, K 

180 

Inc 

Al2O3 

275 

340 

390 

510 

-3 

Inc 

Al2O3-CaO (Mg, P) 

2.981 

180 

180 

Inc 

Al (Mg, Na, Ca) 

750 

Inc  MgAl+Al(Ca, Na, Mg) 

1.752 

0 

Inc 

Mg, Al2O3, CaO 

500 

22 

15 

10 

40 

21 

20 

17 

60 

30 

20 

35 

18.150 

152 

24.375 

176 

11.750 

122 

11.000 

118 

7.140 

95 

7.800 

100 

8.670 

105 

178.86
0 

477 

22.500 

169 

35.040 

211 

17.500 

149 

180 

Inc 

MgAlCa (Na, K, Ti, 
P,Zr) 

2.300 

35 

80.500 

320 

180 

Inc 

Al2O3-CaO (Mg, Na, 
K) P 

1.300 

40 

52.000 

257 

MgAl (+ SCa traza) 

MgAl + (SCa, K) 

210 

425 

MgAlSiCa+Sca 

1.100 

6 

22 

25 

1.260 

40 

9.350 

109 

27.500 

187 

180 

180 

0 

X 

X 

X 

X 

Inc 

Inc 

Inc 

X 

X 

X 

X 

Table A5-6Fatigue results on D100 from heat 47484 

Data treatment as a function of the Applied Load 

In this case all the failures are considered and treated statistically as a single population tested at 22 KN 

The  Weibull  analysis  of  the  Fatigue  data  allows  estimating  the  L10  index  in  order  to  compare  the  fatigue 
performance. Results are summarised in the Table A5-7. 

121 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
Diam (mm) 

L10 (Cycles)  

100 

60 

60 vs 100: % 
Improvement  

4.300 

26.130 

508% 

Table A5-7: Weibull analysis from Fatigue on Cylinders test results on D100mm and D60mm bars from 
heat 47484 

The Fatigue indexes show an improvement of the Fatigue performance of the smaller diameter, in the same way 
that the RFC test. Thus, this test is able to discriminate the fatigue performance improvement.  

Data Treatment as a function of the Local Stress at the fracture initiation Point. 

As it has been previously shown, there is a stress gradient in the specimen. Thus, the stress level at which each 
failure  has  been  originated  may  be  different  for  each  specimen.  This  local  stress  value  is  a  function  of  the 
angular position of the fracture and the linear position along the specimen lentgth at which the fracture initiation 
point.  

In order to perform the analysis of the failures, it is important to define their angular position. Moreover, in the 
following, this analysis will be discussed separately for D60mm and D100mm. 

For  this  test  the  specimens  are  extracted  longitudinally  in  relation  to  the  rolling  direction,  and  tested 
transversally. As described before, the failures are generated in the regions of the sample under tensile stresses, 
that  is,  on  the  top  (0º)  and  bottom  (180º)  internal  surfaces  and  in  the  equatorial  (+/-  90º)  external  surfaces. 
Angular positions of the fractures are measured from the top of the specimen, as shown in the Figure A5-11. 

Figure A5-11: Angular positions on the tubular specimens 

Diameter 60 mm 

122 
 
 
 
 
 
 
The angular position of the failures matches well to the angular spread of the areas under tensile stresses of the 
sample, as it is illustrated in the Figure A5-12, in which is represented the histogram of the angular positions of 
the failures, considering the top of the sample as the angular origin. 

Figure A5-12: D60mm: Histogram of the angular position of the failures 

In all the cases the inclusions found were mainly alumina, with occasional contamination from slag (CaO) or the 
refractory (MgO). 

In the Figure A5-13 it is shown an example of the type of inclusions in the origin of the failures. 

Figure  A5-13:  Inclusion  causing  the  failure  of  a  sample  from  the  D60mm  material.  Fracture  at  180º 
(internal bottom surface). 

Size of the inclusions are very long (higher than 1000 micrometres). In that way, the objective to design a test 
sensible to the longer inclusions is reached. In the Figure A5-14 and the Figure A5-15, the length and width 
histograms corresponding to the inclusions causing the fatigue failures are shown. 

123 
 
 
 
 
 
 
 
Figure A5-14: D60mm : Length histogram of the inclusions causing the fatigue failures. 

Figure A5-15: D60mm: Width histograms of the inclusions causing the fatigue failures. 

Diameter 100 mm 

A  significant  difference  when  comparing  to  the  smaller  diameter  is  that  all  the  failures  are  developed  in  the 
vertical plane (that is at 0º or 180º), while no crack appeared in the horizontal one, as highlighted in the crack 
angular position histogram shown below (Figure A5-16). 

124 
 
 
 
 
 
 
Figure A5-16: D100mm: Histogram of the angular position of the failures 

Nevertheless, the type of inclusions is equivalent to those in the smaller diameter, i.e., alumina inclusions with 
an eventual CaO portion from the slag or MgO from the refractory (Figure A5-17). 

Figure  A5-17:  Inclusion  causing  the  failure  of  a  sample  from  the  D100mm  material.  Fracture  at  180º 
(internal bottom surface) 

For the D100mm samples, the inclusion size distributions show shorter and thicker inclusions than on the D60 
mm, as expected. 

125 
 
 
 
 
 
 
 
 
Figure A5-18: D100mm : Length histogram of the inclusions causing the fatigue failures. 

Figure A5-19: D100mm: Width histograms of the inclusions causing the fatigue failures. 

HEAT 47484: D100 vs D60 as a function of the local stress at the fracture initiation. 

When the local failure stress is considered, the each of the two populations are splitted in different stress levels. 
In  this  way  it  is  evidenced  the  effect  of  the  inclusion  size  on  the  fatigue  performance,  This  is  shown  in  the 
following figure, in wich it is represented the fatigue life of the specimens as a function of the local stress at the 
crack initiation point. 

126 
 
 
 
 
 
 
 
HEAT  47484

C47484-D60

C47484-D100

700

600

500

400

300

200

100

)
a
P
M

(

i

t
n
o
P

n
o
i
t
a
i
t
i
n

I
e
r
u
t
c
a
r
f

e
h
t

t
a
s
s
e
r
t
S

0
1,E+02

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Figure A5-20 Fatigue life as a function of the local stress 

If in this representation it is included the characteristic dimension (length, width or equivalent diameter) of the 
inclusion found in the origin of the failure it is evidenced the effect of the different production paths through the 
effect  of  the  increased  elongation  of  the  inclusions  for  the  higher  reduction.  This  is  done  in  the  following 
representations,  in  which  every  data  point  is  represented  by  a  disc  with  an  area  proportional  to  the  inclusion 
length (Figure A5-21), width (Figure A5-22) and equivalent diameter (Figure A5-23). 

127 
 
 
 
 
 
 
 
 
 
 
Heat 47484-Fatige Life vs Rupture Stress & Inc Length

700

)
a
P
M

600

i

(
t
n
o
P
n
o
i
t
a
i
t
i
n

I
e
r
u
t
c
a
r
f
e
h
t

t
a
s
s
e
r
t
S

500

400

300

200

100

0
1,E+00

1,E+01

1,E+02

1,E+03

1,E+04
Fatigue Life (Cycles)

1,E+05

1,E+06

1,E+07

1,E+08

Figure A5-21: Fatigue life as a function of the local stress an Inclussion LENGTH 

Heat 47484-Fatige Life vs Rupture Stress & Inc Width

700

)
a
P
M

600

i

(
t
n
o
P
n
o
i
t
a
i
t
i
n

I
e
r
u
t
c
a
r
f
e
h
t

t
a
s
s
e
r
t
S

500

400

300

200

100

0
1,E+00

1,E+01

1,E+02

1,E+03

1,E+04
Fatigue Life (Cycles)

1,E+05

1,E+06

1,E+07

1,E+08

Figure A5-22: Fatigue life as a function of the local stress an Inclussion WIDTH 

C47484-D60

C47484-D100

C47484-D60

C47484-D100

128 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Heat 47484-Fatige Life vs Rupture Stress & Inc Equiv Diam

700

)
a
P
M

600

i

(
t
n
o
P
n
o
i
t
a
i
t
i
n

I
e
r
u
t
c
a
r
f
e
h
t

t
a
s
s
e
r
t
S

500

400

300

200

100

0
1,E+00

1,E+01

1,E+02

C47484-D60

C47484-D100

1,E+06

1,E+07

1,E+08

1,E+03

1,E+04
Fatigue Life (Cycles)

1,E+05

Figure A5-23: Fatigue life as a function of the local stress an Inclusion EQUIVALENT DIAMETER 

From these representations it is evidenced that the longer inclusions found in the D60 product causes failures at 
lower local stress levels and conduces to lower fatigue life’s. 

Fatigue Limit (M-E model) 

In order to correlate the fatigue results to the model predictions, the Murakami Endo Fatigue limit model has 
been applied. 

Where: 

-  C=1,41 

-  HV=470 

-  Area=Length x Width 

In the Table A5-8 it is shown the Minimum values of the Fatigue Limit for the two products and the dimensions 
of the corresponding inclusion 

Diameter 

Inc Length 

Inc Width 

Area 

SqrRoot(Area) 

Fat Lim 

60 

100 

14.350 

2.981 

45 

90 

645.750 

201.150 

907 

506 

273 

301 

Table  A5-8:  Minimum  values  of  the  Fatigue  Limit  for  the  two  products  and  the  dimensions  of  the 
corresponding inclusion  

129 
 
 
 
 
 
 
 
 
 
ANNEX 6: VALIDATION FATIGUE TESTS ON 100CR6 (HEAT 54062, TASK 4.3.2) 

For the final validation tests two billets from heat 54062 were rolled to D-100 mm and D-62 mm round bars. A 
set  of  tubular  samples  was  manufactured  from  each  of  the  two  products  rolled.  In  the  following,  the  results 
obtained  from  this  test  on  the  two  products  are  presented  and  compared  to  the  results  obtained  on  similar 
products discussed in the previous Task 4.1. 

According to the previous trials, the tests conditions were fixed to a maximum compression load of 22KN. In 
fact, previous tests have shown that at this loading level, the main failure initiation reason was originated in long 
inclusions in the subsurface of the regions of the specimen under tensile stresses. 

Thus, a set of 12 specimens were manufactured from each of the products and tested in the same conditions than 
previously. This helped in the evaluation of the fatigue behaviour concerning the new heat, which has different 
inclusion  content.  The  runnout  criterion  was  fixed  to  6.5x106  cycles  according  to  the  results  obtained  from 
previous tests. At the usual resonance frequency of the system with this type of specimen (about 77.4 Hz), every 
individual “runnout” test (i.e. not failed specimen) spends one single day.  

Diameter 62 mm 

Ten samples were tested, and the corresponding results are summarised in the Table A6-1. 

N 

1 

2 

3 

4 

5 

6 

7 

8 

9 

LOAD (KN) 

Fatigue Life 

Max 

(Cycles) 

Failure
/ Runnout

(F/R) 

Angular 
Position (º)

Failure Mode 

Inc/Matrix 

-22,0 

336.463 

-22,0 

1.066.300 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

6.502.600 

6.550.400 

6.570.500 

6.584.637 

7.325.900 

7.500.100 

8.450.030 

180 

0 

Al2O3 
(L=1600 mm, W=16mm) 

SMn 
(L=358 mm, W=5 mm) 

F 

F 

R 

R 

R 

R 

R 

R 

R 

R 

10 

-22,0 

8.455.800 

Table A6-1: Resonant Fatigue test on Cylinders from 62 mm bars from heat 54062. 

130 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Only two failures occurred in this product (20% of the population) in contrast to the behaviour of the equivalent 
diameter,  for  the  heat  47484,  previously  tested  (24  failures  in  a  population  of  25).  The  Electron  Microscopy 
inspection of the fractures revealed that the two fractures were initiated in inclusions. In one of the specimens, 
the inclusion has been revealed to be a sulphide, as shown in the Figure A6-1 to Figure A6-4 below. 

Figure  A6-2  :  Long  Alumina  inclusion  in  the  origin  of  the  failure  of  the  specimen  with  the  shorter  life 
(336.463 cycles). 

131 
 
 
 
 
 
 
 
 
 
 
 
 
Spectrum 

O 

Mg 

Al 

Total 

S1 

S2 

S3 

S4 

S5 

58.02 

0.70 

41.28 

100.00 

61.13 

0.62 

38.25 

100.00 

55.83 

44.17 

100.00 

57.86 

1.88 

40.26 

100.00 

58.44 

2.64 

38.92 

100.00 

FigA6-2: EDX Analysis of the inclusion in the origin of the failure shown in Figure A6-1. 

Figure A6-3: Defect in the origin of the fatigue failure (specimen life 1.066.300 cycles) 

Spectrum 

S 

Mn 

Total 

S1 

S2 

34.86 

65.14 

100.00 

35.45 

64.55 

100.00 

132 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A6-4: EDX analysis of the inclusion shown in Figure A6-3. 

iameter 100 mm 

A  set  of  ten  specimens  has  been  used.  Tests  were  conducted  according  to  the  improved  performance  of  the 
62mm bar material of the same heat and in comparison to the previously tested specimens from heat 47484. In 
spite of the expected behaviour almost all the specimens failed due to the matrix collapse (Table A6-2).  

LOAD (KN) 

Fatigue Life 

Failure
/ Runnout

Failure Mode 

Max 

(Cycles) 

(F/R) 

Angular 
Position (º)

Inc/Matrix 

N 

1 

2 

3 

4 

5 

6 

7 

8 

9 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

-22,0 

F 

F 

F 

F 

F 

F 

0 

0 

0 

0 

0 

80 

M 

M 

SMn/M 
(L=110 mm, W=7 mm) 

M 

SMn/M 
(L 128=mm, W=10 mm) 

Al2O3 
(L=870 mm, = 15 mm) 

Failed before frequency 
stabilization 

4.627 

7.958 

8.720 

15.471 

61.047 

94.822 

*** 

*** 

*** 

*** 

10 

-22,0 

Table A6-2: Fatigue results on 100 mm bar material from heat 54062 

133 
 
 
 
 
 
 
 
 
 
 
 
 
 
This event has been confirmed by SFEG images, in which appears that no inclusion is in the origin of the fatigue 
failure. The fracture is inter-granular from the initial stages of the failure, while ion the fractures initiated in an 
inclusion, the fatigue crack propagation is mainly intra-granular. 

The  cause  of  such  a  poor  fatigue  behaviour  may  be  due  to  surface  defects  caused  during  the  specimens 
machining.  

Figure A6-5: Fracture initiated in the surface (Specimen life: 15.471 cycles). No inclusion present. 

The corresponding Critical index L10 allow quantifying the observed improvement (Table A6-3). 

Diam (mm) 

100 

62 

62 vs 100: % 
Improvement 

L10 
(Pulses) 

2.786 

917.188 

32824% 

Table A6-3 Critical indexes for products from heat 54062 

Data Treatment as a function of the Local Stress at the fracture initiation Point. 

In the following graphs it is shown the fatigue life of the 4 products as a function of the local stress at the crack 
initiation,  and  the  area  of  the  corresponding  data  points  are  proportional  to  the  inclusion  dimension  (length, 
width or equivalent diameter). Discussion is included in the main text of the report. 

134 
 
 
 
 
 
 
 
Fatige Life vs Rupture Stress & Inc Equiv Diam

C47484-D60

C47484-D100

C50624-D62

C54062-D100

700

600

500

400

300

200

100

)
a
P
M

i

(
t
n
o
P
n
o

i
t
a

i
t
i

n

I
e
r
u
t
c
a
r
f
e
h
t

t
a
s
s
e
r
t
S

0
1,E+02

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Figure A6-6 

Fatigue life as a function of the Local Stress at the failure origin. The Area of each data 
“disc”  is  proportional  to  the  Inclusion  EQUIVALENT  DIAMETER  (Sqr  Root  of  the 
total inclusion area) 

Fatige Life vs Rupture Stress & Inc Length

700

600

500

400

300

C47484-D60

C47484-D100

C54062-D62

C54062-D100

)
a
P
M

i

(
t
n
o
P
n
o

i
t
a

i
t
i
n

I
e
r
u
t
c
a
r
f
e
h
t

t
a
s
s
e
r
t
S

200

100

0
1,E+02

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Figure A6-7 

Fatigue life as a function of the Local Stress at the failure origin. The Area of each data 
“disc” is proportional to the Inclusion LENGTH 

135 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fatige Life vs Rupture Stress & Inc Width

C47484-D60

C47484-D100

C50624-D62

C54062-D100

700

600

500

400

300

200

100

)
a
P
M

i

(
t
n
o
P
n
o

i
t
a

i
t
i

n

I
e
r
u
t
c
a
r
f
e
h
t

t
a
s
s
e
r
t
S

0
1,E+02

1,E+03

1,E+04

1,E+05
Fatigue Life (Cycles)

1,E+06

1,E+07

1,E+08

Figure A6-8 

Fatigue life as a function of the Local Stress at the failure origin. The Area of each data 
“disc” is proportional to the Inclusion WIDTH 

136 
 
 
 
 
 
 
 
 
European Commission

EUR 26175 —  Innovative methodology for through process inclusion level forecasting of engineering 

steel (INCLUSION)

Luxembourg: Publications Office of the European Union

2013 — 136 pp. — 21 × 29.7 cm

ISBN 978-92-79-33088-9
doi:10.2777/41933

137