ニアネット成形部品の仕上げ加工のための熱モニタリングに関する研究
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言語: English
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公開日: 2018-06-06
キーワード (Ja):
キーワード (En):作成者: 呉, 東津
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https://doi.org/10.15118/00009631

Doctoral Dissertation:

Research on Thermal Monitoring for Finish
Machining of Near Net Shape Parts

Dongjin Wu

Muroran Institute of Technology

March.2018

Title:Research on Thermal Monitoring for Finish Machiningof Near Net Shape Parts

Name: Dongjin Wu
Supervisor: Professor Koji Teramoto
Associate supervisor: Professor Yoshihisa Aizu&
Professor Toshiharu Kazama
School: Muroran Institute of Technology
Date:23/03/2018

Abstract
The objective of this research is to investigate an appropriate monitoring method tosecure the accuracy of finish machining.
The development of complex 3-dimensional shape products for industrial applications
has been one main goal for manufacturing industries over the last few decades.
Conventional processes such as injection molding, forging, and casting and assembly
technologies are typical processes that have contributed to achieve mass production.
Recently, various small-lot near-net shape production methods such as additive
manufacturing, thin-wall casting, and incremental forming are proposed and becomecommon in industry. Because these near-net shape parts are often complex shape, theseparts are finished by manual operation. Therefore, a systematic finish machining method
for complex 3-dimentional parts for small-lot production is eagerly desired.
Because end-milling has advantages like efficiency, accuracy, and applicability to smalllot production, this technology is very widespread application in aerospace, automobile andother manufacturers. Therefore, end-milling is expected as a promising machining method
for finish machining of near net shape parts. However, accuracy assurance of end-milling
has not achieved. Especially, thermal monitoring in process has not been caught easily bytraditional detect method, such as thermocouple measurement system or wirelessthermographic technology.
Heat generation and heat transfer characteristics in the cutting process are strongly
affected from the cutting process. As a modern technology, finite element simulation canbe used to reproduce the machining process. It can be used to study the stress and strain,Itemperature distribution, chip breakage and friction and heat generation characteristics ofthe cutting area which are difficult to be observed in processing. In this thesis, thecombination method of local temperature measurement and finite element simulation isused to investigate the estimation of temperature distribution in machining process. The
main contents are as follows.
Firstly, trends of recent production are summarized as a background of this research.
Roles of monitoring in information driven and connected production are explained.
Moreover, related researches about thermal analysis in end-milling, measurement
technology development, FEM analysis, and combination of measurement and simulation
are surveyed.
Secondly, a framework for temperature measurement is proposed. Based on
considerations of process variety, a concept of measurement point evaluation is explained.
This research presents a method to evaluate an effectiveness of each measuring points from
aspects of stability of measurement and sensitivity to the process variation.
Thirdly, developed systems to evaluate the proposed framework are explained. A thermal
analysis method of end-milling is explained, and details of developed simulation system
are illustrated. Developed 3D finite element (FE) models are constructed based oncommercial FE software to simulate the macroscopic workpiece behavior. After discussing
a representation method of process variation, evaluation procedures of measurable points
are illustrated. The proposed evaluation method of measuring points is evaluated based on
the case studies.
Lastly, conclusion of dissertation is described.
Key words: near-net shape parts, small-lot manufacture, end milling temperature, thermal
expansion, monitoring method
IIAcknowledgments
I would like to express my sincere gratitude to my advisor, Dr. Teramoto, for his guidance,encouragement and support during my studies at Muroran Institute of Technology. Thanks to the faculty
and students of the Manufacturing Engineering Laboratory for their help and support to this research. I
appreciate the partial financial support from the KAKENHI Grant Number 26420038.

Most of my daily life was support by Japan government scholarship, which I had this opportunity tocontrate my focus on my research, A lot of learning gradually about end milling process with many help
from my professor, he continuing gives me lectures once and once, and also I had a luck opportunity to
Makino machining company for internship, I broaden my eyes to see what is happened in thermal
expansion when we deal with accurate machining center, and what the bad influence to operated
machines, tools and finished parts, for me, it was really good experience for my research step. I fell myburden, as researcher, do research is the backup of technology company, and also will changing ourlives.

Three years passed quickly and slowly, sometimes, I felt lonely after I am leaving motherland, but theirsupport and understanding push me walking further and further; from many good friends’ encouraging
around me, I known that I am a fighter. I cannot finish doctoral course without the great support ofteachers, family and friends.

With thousands of thanks to everyone who helped me.

IIIOutline of contents
Abstract .......................................................................................................................... I
Acknowledgments........................................................................................................ III
Outline of contents ...................................................................................................... IV
1 Introduction ................................................................................................................ 1
1.1 Background .......................................................................................................... 3
1.2 Small-lot manufacturing...................................................................................... 7
1.3 Thermal simulations applied in machining process ......................................... 10
1.4 Machining process monitoring .......................................................................... 15
1.6 Summary ............................................................................................................ 16
2 Survey of related research ....................................................................................... 18
2.1 Research status .................................................................................................. 18
2.1.1 International and domestic research on thermal issue .............................. 18
2.2 Measurement technology ................................................................................... 24
2.2.1 Thermal-couple and Infrared thermal-couple ............................................ 27
2.3 FEM.................................................................................................................... 30
2.3.1 FEM used in manufacturing ....................................................................... 30
2.3.2 The basic equation of heat conduction in cutting progress ........................ 32
2.3.3 2-D heat conduction ..................................................................................... 34
2.3.4 Determination of heat proportion of heat source in flying face wear ......... 36
2.3.5 3-D heat conduction ..................................................................................... 38
2.3.6 Generated heat in end milling .................................................................... 38
2.3.7 The thermogenesis of plastic strain ............................................................ 39IV

2.3.8 The thermogenesis of tool-chip friction ....................................................... 39
2.4 ANSYS APDL..................................................................................................... 40
2.5 Combine measurement and simulation ............................................................ 43
2.6 Summary ............................................................................................................ 45
3.Article Propose ......................................................................................................... 46
3.1 The proposal of this subject ............................................................................... 46
3.2 The main contents of this thesis ........................................................................ 46
4 Method and Principles ............................................................................................. 48
4.1 Mobile heat source method based on Fourier’ law heat conduction ................. 48
4.2 Modeling and solving of workpiece temperature field ...................................... 52
4.2.1 Approximated simulation model ................................................................. 53
4.2.2 Simulation process ...................................................................................... 54
4. 3 Summary ........................................................................................................... 61
5 Thermal estimation .................................................................................................. 63
5.1 Principle to select the nodes .............................................................................. 63
5.2 Evaluation of sensitivity against process variation .......................................... 64
5.3 Case of different machining situation (half cutting paths) ............................... 65
5.4 Simulation results for temperature................................................................... 67
5.5 Effectiveness of measurable points ................................................................... 85
5.5.1 Evaluation method for measuring points ................................................... 85
5.5.2 EoP formula ................................................................................................. 86
5.5.3 Result of one measurable point evaluation ................................................. 87
5.5.4 Results of measurable points evaluation .................................................... 89
5.6 Summary ............................................................................................................ 93
6 Applications .............................................................................................................. 94
6.1 Simulation models ............................................................................................. 95V6.2 EoP application models ...................................................................................... 96
6.3 Orthogonal tests................................................................................................. 97
6.4 Square model 1 ................................................................................................ 102
6.5 Square model 2 ................................................................................................ 107
6.6 Square model 3 ................................................................................................ 109
6.7 Calculated EoP value ....................................................................................... 111
6.8 Summary .......................................................................................................... 112
7 Conclusion .............................................................................................................. 114
Reference ................................................................................................................... 116

VI

1 Introduction
Near net-shape (NNS) are used in many manufacturing, the desired outcomes ofmanufacturability analysis for near-net-shape processes are cost and time reduction throughminimization of process steps (in particular cutting and finishing operations) and raw
material saving. For example, near net forming technology has advantages like higheffective and low cost, this technology is very widespread application in aerospace,automobile and model manufacturers. Moreover, it is well known that additive
manufacturing is widely interested and investigated as a next generation production
technology. Besides the various NNS technologies are constantly improving and evolving,
there still is challenge in accurately reflecting their requirements and capabilities. Therefore,a finish machining for NNS parts is still indispensable process.
Machining still remains a major industrial activity despite recent significant
developments in near-net shape forming techniques[1]. We are chasing much accuracy,
agile production and monitor-able in small lot manufacturing, as the manufacturing
development cannot leave the advanced manufacturing technology, and advanced
manufacturing technology is the core and basis of high quality, high efficiency, lowconsumption, clean and pollution-free technique.
Conventional workpiece has large or medium batch, the simple shape and focus oncommon material. Conventional requirement of machining is high quality, low cost anddue date (accuracy, zero defect, agility). End-milling is satisfied with the requirement ofhigh quality and agile production. However, when we meet the emergent workpiece, likethe small batch, complex shape, hard and new material, the additional requirements areappeared. Moreover, new requirements such as traceability, environmental issues and1workers’ safety are also needed to adapt..
Recently, network connected and information driven manufacturing based on modern
information technologies such as IoT (Internet of Things), Cloud computing technology
and CPS (Cyber-Physical System) become a realistic solution[2]. In such an emergent
manufacturing system, agile and smart adaptation to changeable demands must be
accomplished[3]. It is reported that a closed loop machining using on-machine shapemeasurement can achieve an accuracy assured machining [4-6].
With the development of new machine tools and related equipment, advanced cutting
tool material development, the cutting technology towards a more efficient, practicaldirection. A growing concern in the aerospace, automotive and biomedical industrial
segments of the manufacturing industry is to build in absolute reliability with maximum
safety and predictability of the performance of all machined components [7], as we humans
can pursue high precision and most perfect prediction to the machined parts.
As the finish machining methods, end-milling is a promising method because of its
versatility and effectivity. Because thermal behavior influenced the accuracy of finishedparts, it is important to understand and control the thermal behavior during finish process.
When the thermal situation in finish machining is well controlled, components can bereliably fabricated with desired material properties.
Thermal issues in end-milling have been investigated for many years. For example, asimulation research about heat generated in end milling are reported. In the research,evaluation of a dimensional error of finish workpiece depending on experiment andanalysis has been investigated[8]. After many years studying, influence of tool thermal
expansion has been also investigated and summarized in many published papers. However,monitoring temperature in process is not easily caught by traditional detect method, such2

as thermocouple measurement system or wireless thermographic technology.

1.1 Background
Smart manufacturing will integrate these parts of automation, enabling data sharing
throughout the simulation. The convergence between machine-gathered data and human
intelligence will advance manufacturing engineering, including substantial increases in
economic performance, worker safety and environmental sustainability. Smart
manufacturing marries information, technology and human ingenuity to bring about a rapid
revolution in the development and application of manufacturing intelligence to every
aspect of business. It will fundamentally change how products are invented, manufactured,
shipped and sold. It will improve worker safety and protect the environment by making
zero- emissions, zero-incident manufacturing possible. It will help keep jobs in this country
by keeping manufacturers competitive in the global marketplace despite the substantiallyhigher cost of doing business in the world.
Near net forming technology is through the precise mold design, strict control of hotprocessing technology parameters, and a reasonable forming process to produce without
processing or only a small amount of processed parts of the process. It does not need or
only a small amount of follow-up processing, in reducing the production cycle and save
raw materials but also can greatly reducing production costs at the same time, it will
effectively promote the application of small lot production. With the development ofaviation and aerospace industry, the precision forming of complex shape components has
replaced the traditional "multi-piece connection" structure and "forging-cutting"production mode has become an inevitable trend. New net forming technology is one of
the future developments of manufacturing industry trend.
The application of near net forming will play an important role in the future3manufacturing industry. The use of near net forming technology to solve the difficult
problem of alloying will be an efficient and fast way. Such as in the field of aerospace,titanium and titanium alloy because of its high strength, light density, plasticity and othercharacteristics, is good to meet the new materials, new technical conditions on the material
requirements. However, there also has three factors of near net shape parts are looking
forward to greater progress，(1)

Laser engineered net shaping-LENS is this century in many parts of the world havedeveloped an advanced manufacturing technology, this technology send synchronous
powder laser multi-layer welding compound technology with rapid prototyping technology,
can achieve high performance, compact, rapid prototyping manufacturing of complex parts.
But, In the process of HIP, the powder can be compressed, deformed and the boundary isvariable, and the precise simulation of complex structure is needed in the constitutive
model, material parameter, and finite element solution and so on
The numerical simulation of the process temperature field is used to further
understand the variation law of the forming degree field and provide the theoretical basis
for the forming experiment. The study of the temperature field of the laser solvatedtemperature field is also helpful to establish the temperature field model of the lens process.
In recent years, there are many researches on the numerical simulation of the laser coatingprocess and the LENS process temperature field. The study of the temperature field
simulation of laser salvations also helped to establish the temperature field model of the
LENS process.
Research status:
In 1992，A. F. Hoadley comes from Swiss Federal Institute of Technology [9] ,who
proposed the two-dimensional finite element model of powder-coated laser cladding and4the quasi-steady-state temperature field of the longitudinal section of the coating was
obtained. The results show that the laser power and scanning speed are linearly related tothe height of the cladding coating
In 2003，American corning Lasertron company’s M Labudovice et al. [10], used thecapabilities of ANSYS parametric design language and calculates transient temperature
profiles, dimensions of the fusion zone and residual stresses, experiments showed good
agreement with the modeling.
In 2004, the Italian University of Science and Technology, Palumbo, G., S et al.[11]used software to establish a three-dimensional finite element model of laser single-channel
over-alloying on alloy ring parts. Calculated to solve the problem. The distribution of stressand strain in the process of the coating process was evaluated when the dilution rate andthe change of the bath morphology were analyzed. Since 2004, the University of Canada,
such as the University of Waterloo [12] using the laser column and the process of
simulation and experimental study, pray for the pulse laser shaping process on the impact.
In 2006, University of California R. Q. Ye. et al..[13], A three-dimensional finite
element model for the preparation of non-induced steel slabs was established by ABAQUS.
The variation of temperature and temperature gradients near the pool was analyzed. The
two-dimensional finite element model of stainless steel sheet was fabricated by Mississippi
State University, and the distribution of temperature field in forming process was obtained.
The influence of laser power on temperature field was analyzed. The experimental results
were in accordance with the simulation results.
In 2009，Y.H. Xiong et al. of University of California, USA[14], the three-dimensional
finite element model of Wc-Co ceramic metal thin-walled parts was established. The sizeof the solution was calculated by high-speed camera, the correctness of the model was
5verified, and the temperature, cooling rate and temperature gradient near the molten pool
were further studied. The simulation results explain the variation of microstructure of thinwalled parts.
In 2010, J.H. Yang et al. From Swinburne University of Science and Technology, whoestablished a three-dimensional finite element model of laser heating alloy, analyzed theinfluence of different process parameters and material properties on heat influence size.
The results show that with the increase of laser power, the depth and width of the heataffected zone will increase. With the increase of the spot diameter and the scanning speed,
the depth and width of the heat affected zone will decrease, and the process parameters of
the heat control zone size will be found by simulation.
In 2011, the United States Utah State University C Zhang and others used software toestablish a laser multi-channel solution super alloy dimensional finite element die spray, topredict the multi-channel lamination process towel temperature field and stress field,
pointed out that the hot crack generated. The results are consistent with the simulation
results[15]. A 3-D transient finite element (FE) model has been developed and athermomechanical analysis is performed. The developed thermal model has been applied
to predict and analyze the distribution of thermal stress concentration and the tendency ofstray grain formation.
(2) Additive Manufacturing (AM) is an appropriate name to describe the technologies
that build 3D objects by adding layer-upon-layer of material, whether the material is plastic,
metal, concrete or one-day human tissue. There are several solid freeform fabrication (SFF)
techniques [16]including 3D printing, ink-jet printing, fuse deposition modeling of
ceramics based on layer by layer deposition using 3D model image.
Thin-walled composite components, also better known as die casting, creates parts
6

that are stronger and lighter than those of other casting methods.
(3) The key to the precision forming of thin-walled composite components is that there
are also a lot of hard-to-handle control of microstructure and metal flow control.
These three factors meet the high demand of produce a broad range of parts withperfect finish and astonishing detail. Many key components used in the small lot production
are made of difficult machining processing and diffident materials, Alloy in the near net
forming technology like the application will be greater development. Above three near net
shape applications meet some problems, such as small batch, complex shape and hardmaterial, etc. at these atmosphere, thermal issues cannot be solved by simulation only.
Finished parts from near net manufacturing need to face some problems, like complex
shape from servo press, hard material from laser machining and thin wall from thin casting ,and so on. In conventional approaches to solve the post processing, employing the end
milling process is wildly accepted in manufactures. In the end-milling, the thermal
phenomena is the common problem in finished parts. Therefore, we are considering toestimate temperature distribution in end milling is to support finish machining of near net
shape parts.

1.2 Small-lot manufacturing
Quiet a large proportion of industrial products are manufactured by processes thatshear and form standard shapes, largely sheet metal, into finished parts. In general, shearing
and forming are all that are required some secondary operations, such as cutting, drilling,tapping and milling, or even welding, to appropriately prepare it for assembly in to wanted
product. However, the near-net-shape characteristics of the results of these processes makethem an extremely cost-effective alternative for producing a product or component[17].
Small-lot production is a transitional form between unit production and the7manufacture of products in moderate-sized lots. The sizes of the lots vary. The scale of the
product is limited to existing orders or contracts. For this reason, the manufacture of aparticular product ends comparatively quickly, and a new product is then introduced into
manufacture. Small-lot production is used, for example, in the manufacture of somespecial-purpose alloys, rolled products, the production of additive manufacturing, thin-wall
casting, and incremental forming in the manufacture of small lots of articles or machines
intended for experimentation under various conditions. Small-lot production differs fromexperimental production, which is generally limited to the manufacture of a single sample.
During the cold war, the nuclear weapons complex produced thousands ofcomponents each year to support the stockpile. The manufacturing process stream had highproduction capacity but not always high yields[18]. As seen in Figure 1 A qualified process
and what can go wrong(b), problems that can arise when one tries to develop qualified

process must be compensated. In order solving a series of blind points in dynamics process
which are poorly understood and not monitored, in-process monitoring and control became
essential to small-lot manufacturing. To achieve in-process monitoring and control, we
must employ the data reduction methods to find the key signatures that might identify thepresence of specific faults. Therefore, we should introduce some signatures to develop
learning algorithms that only identify the faults but also classify their causes.

8

(a)

(b)
Figure 1 A qualified process and what can go wrong

Figure 2 In process monitoring and control

9In the recent years, there are increasing demands for small lot production technologies
to deal with diversifying needs and rapid forming tool production technologies to survive
in global competition on the development of new products. Improvements of machining
technologies are necessary. For example, the sheet metal forming technique, which istypical mass-production method requires the rapid production [19].

1.3 Thermal simulations applied in machining process
Finite element (FE) simulation of machining can be used as a replacement or a
supplementary to the conventional experiment allowing an analysis to be performed at alower cost. With the rapid development of computer technology and the theory ofnumerical analysis, the finite element simulation is more and more widely used inmachining process. Many commercial finite element software such as ABAQUS,
Advantage, Deform, LS-DYNA and so on have become a powerful tool for metal cutting
simulation. Thermal simulation in machining process is an active research topic in recent
years, as the heat generated in the machining process has vital effect on both the quality of
the workpiece and the life of the cutting tool. It also can shorten the time-to-market andreduce the cost of the machining process. However, the simulated thermal results inmachining process are often unreliable due to the lack of quantitative material data, theparameters used in boundary conditions and other parameters used in the simulation
process.
Maurel-pantel[20] uses DYNA software to perform 3D simulation of 304L stainlesssteel square shoulder milling. Man[21] proposed a three-dimensional explicit dynamic
Lagrangian finite element model for predicting the overall force. Saffar[22] considered theeffect of tool deformation on the cutting force in the model. The results of many researchers,
such as stress, strain, temperature distribution and residual stress, can be obtained by using
10

finite element method in one cutting simulation, but the operation time of this method islong, especially for cutting and orthogonal milling class of the three-dimensional cutting
process. This shortcoming is particularly evident. Due to the limitation of the simulation
period, the finite element technology is not yet suitable for studying the influence of manydifferent working conditions on the cutting force of 3D complex process. Cutting
temperature field analysis modeling one of the pioneers Jeager[23] proposed the mobile
heat source method in 1942.
The finite element method (FEM) has been employed to solve end milling dynamic
models, experimental and analytical methods have been developed to estimate the valuesof fatigue and creak in end milling. It can reduce energy consumption for both cost saving
and environmental friendliness[24]. As the rapid use of internet and simulation software,it
is significately known that the simulation has played a much importment role in industry.
From literatures,there are many research paper worte about the application of simulationin machining process,not only the cutter wore but also the application of workpiece thermal
expansion.
Analytical modelling as opposed to the widely used FEM based modelling is much
faster in computation time[25],in literautres there are provided several analytical andsimulation approachs for temperature modelling in metal cutting[25], An analytical model
for prediction of tool temperature fields in metal cutting processes has been developed[26].
In the field of manufacturing engineering, the computer simulation process is one of
the important results, it uses the mathematical model for the required power, cutting force
and chip formation. These computational models are of great value in reducing or eveneliminating the number of iterations in tool design, process selection, workability
estimation and chip breaking studies. In the past ten years, many studies of finite element
11

methods have been spent on modeling the performance of different types of diamondcutting problems. The earliest analytical model was made by Mer Chant Piispanen [27, 28],and Lee Shaffer [29]. The reason why the chip is crimped due to the contact between thetool nose and the chip is introduced[30]. Palmer proposed a model including the effect ofcomponent hardening and strain rate on the machining process [31, 32]. Doyle built a new
viscoelastic model, which included the friction between the tool's rake face and the chips
formed during the machining and the friction between the tool and the surface formed by
the tool[33]. Trigger and Chao, the problem of heat caused by plastic deformation andfriction is considered in the process in the analysis model [34]. Usui use energy methods
to establish a model, this model takes into account the three-dimensional geometricconditions in the process of the impact[35].
In manufacturing engineering, using computer to simulate the manufacturing process
is an important achievement, which calculates required power, cutting force and theforming of cutting by means of mathematical models. These calculated models canobviously reduce or even eliminate the repeated experiments in the design of tools,
choosing of process, estimation of machinability and chip breaking research. In the past
decades, the study on FEM are most about the numerical modeling of machinability ofdifferent cutting problems. The advantage of FEM is that it can be simulated the wholecomlicated process automaticly. In 1973, B. E. Klameck[36] in Illinois University firstly
studied the formation mechenism of chip in cutting process systematically. In 1980, M. R.
Lajczok[37] in North Carolian University using FEM to analyze the major problems inmaching procees in his doctoral dissertation. Usiui and Shirakashi[38], Iwata et al.[39],Strenkowsk and Carroll [40] was the prior researchers who used FEM to simulate thecutting process.
12

Usui and Shirakasih1[38] wanted to establish a steady-state orthogonal cutting model,
the first proposed blade angle, chip geometry and streamline, etc., to predict the stress andstrain and temperature of these parameters. Iwata et al. established a rigid body[39], plastic
finite element model. The thickness of the chip, the shape of the crimp and the internal
stress and strain distribution were calculated, and the effects of the material properties andthe friction on the internal stress and strain were discussed. At the same time, the cutting
test was carried out, and the test results also verified the simulation results. However, they
did not consider the elastic deformation, so there is no calculation of residual stress.
In 1982, in order to build Orthogonal cutting model of the steady state, Usui and
Shirakashi [38] firstly proposed cutting angle, chip geometry, flow line and et al., they
predicted the parameters of stress-strain and temperature. Iwata[39], Streknows and Carroll[40] built new finite element model, which includes a chip separation criterion based on
effective plastic strain. Some of the previously ignored cutting parameters are included inthis finite element model, for example,they considered friction between the tool and thechip,and the tool is elastoplastic. The results show that the application of the chip separation
criterion is very important and effective in the processing of finite element simulation
components. In the future research, various chip separation criteria, such as K. Komvopoul
and following, there were many criterion has being estimated. For example, S.
A.Erpenbeck[41] “Distance tolerance” criterion, Z. E. Lln, S. Y. Lin set strain energy
density criterion [42]. J. Hashemi, A. A.Tseng [43]built Separation criteria based on
fracture mechanics. Huang and Black evaluated some of these criteria[44], they found that
if the cutting process was stable, these criteria would not have a significant effect on the
shape of the chip and the stress and strain distribution within the component. Komvopou1os
and Erpenbeck used the Coulomb friction law to obtain the normal force and friction
13

between the tool and the chip by means of orthogonal cutting analysis. Komvopoulos andErpenbeck used the Coulomb friction law to obtain the normal force and friction between
the tool and the chip by means of orthogonal cutting analysis.
The elastic-plastic finite element model was used to study the effects of the side wearon the tool, the residual stress in the chip and the workpiece, and so on[41]. Furukawa andMoronuik studied The influence of finish on the surface quality of the workpiece in ultra precision cutting of aluminum alloy by experimental method. The analysis shows that when
the depth of cut is about 10-6m, the minimum cutting force is in the range of about10-1N[45].
Noyo Ikawa wrote the interaction between the chip formation and the depth of cutting of
the red copper material was measured by a precision cutting machine. The depth of the
cutting used in the experiment was 10-9m[46]. Toshimichi Moriwaki et al. Used the steelplastic finite element model to simulate the above experiments [47]. They simulated thedepth of the diamond in the range of millimeters to nanometers in the process of orthogonal
cutting. The effect of cutting depth and cutting speed on residual stress in orthogonal ultraprecision cutting of NiP alloy was studied by Zone Chinglin et al. of Taiwan University of
Science and Technology. The model of flow stress of the material was obtained byregression analysis of the unidirectional tensile test data before the simulation, they built
the thermoelastic and plastic finite element model considering the thermal effect in the
cutting process[48].
But the above studies are mainly considered a number of aspects of the process, more
importantly, it is based on their own research on the preparation of a model for the finite
element program. And they did not form a professional business process for cutting.
Because the preparation of a finite element program is a very heavy task, so the effective

14

application of existing finite element business software for cutting numerical simulation isparticularly important.

1.4 Machining process monitoring
There are many reasons for installation of monitoring system in a manufacturing
process. A machine tool operator performs routine monitoring tasks, for example, visuallydetects missing and broken tools as well as its sound characteristic. Monitoring and controlof manufacturing process is becoming nowadays a driver for development andsustainability of manufacturing industries[49].The cutting force signal is considered toprovide rich information for tool failure detection in end milling and drilling operations[50]
An intelligent monitoring and control of manufacturing processes is becoming
nowadays a driver for development and sustainability of manufacturing industries. Processmonitoring is the manipulation of sensor measurements in determining the state of theprocess[51]. There is an example in monitor application is Komatsu construction machines,
which Komatsu not only is a maker to make construction and mining equipment but also
simulation predict monitoring system to watch out fatigue or creak happened. This
company focused on the monitoring during the constriction process, also inventors’
development of prediction and collection information from crack date and deformation datefrom machine monitors, the feedback from machine to operators is a directly process that
can help reducing the accident rate, monitor widely used in industries is an inevitable trend.
In the automobile industry and aerospace industry, driver monitoring system on auto
driving has rapid developed in recent years, especially the monitoring has become the mostvalidate method to improve get around easily and safely drive, the world’s first drivermonitoring system features a camera, using near infra-red technology, mounted on top of
the steering column cover. This camera monitors the exact position and angle of the driver’s
15

head while the cars are in motion. In 2008, the Toyota Crown system went further and can
detect if the driver is becoming sleepy by monitoring the eyelids, and also there are many
examples in airplane with an aircraft monitor ,such as structural health monitoring system
for an airplane to determine the origin of any damage in a particular structure [52].
Monitoring cutting force in end milling is an active research, the selection of an
appropriate signal and signal processing algorithm is very important. Several signals in a
milling operation have been considered to monitor tool failure, for example, cutting force,
torque, vibration, acoustic emission, and spindle motor current[53]. This scientific work
has so far been focused mainly on the prediction of workpiece thermal damage. it was
shown that thermally induced changes of workpiece surface and subsurface properties
usually occurred in connection in significant changes of process quantities, especiallycontact zone[54].
According to the literature, a generic methodology for developing an intelligent
monitoring system for machining is composed of six key issues: 1. Sensors; 2. Signal
processing;3. Feature generation; 4. Feature selection/extraction; 5. Process knowledge
model (a) Design of experiments and (b) AI technique[55].

1.6 Summary
1. This chapter states that end-milling is a promising machining method in the finish
machining of near net shape parts.
2. The application of thermal expansion in end milling directly reflects the heat
distribution in near net parts, so that the prediction of end milling thermal study can achieve
the thermal prediction in near net.
3. Modern industrial monitoring system has been shipped a very wide range, thetechnology is more mature, and monitoring temperature in the end-milling process will be
16filed in this article.
4. The technical route of this dissertation was proposed.

17

2 Survey of related research
2.1 Research status
2.1.1 International and domestic research on thermal issue
The development of near net shape 3-Dimensional products for industrial applications
has been one main goal for manufacturing industries over the last few decades. Processes
such as polymer blow molding and its various stages of development, glass forming,
extrusion, forging, centrifugal and sand casting, bulge forming, and vacuum forming are
typical processes that have contributed to this development. Current practices center on
surface coatings, rapid prototyping, laser forming and nanotechnology manufacture ofcomplex 3-D shapes and assemblies[56].
The pioneering study of the field of cutting temperature analysis was initiated by
Benjamin Thomason, who studied the heat generation of the cannon bore in 1798 andpresented the concept of thermal equivalent[57]，or use a thermocouple to measure thetemperature at a different position in the tool or workpiece[58, 59].The exact relationshipequation was established by Joule in 1850[60],and found that heat had an important effecton tool wear and suggested that the cutting speed had an important relationship with tool
life [61, 62], Taylor's research results have been used until now. The earliest cutting
temperature analysis model was presented in the 1950s. Use a moving heat source and aheat source to approximate the temperature distribution on the shear plane and the sliding
friction surface[63-68].
Rosenthat[64] and Trigger[63] made a pioneering contribution to the research ofmobile heat sources, and carried out the analysis and research on the heat generation inmost mechanical processes. The groundbreaking contribution of these processes to heat18

production is the basis for many problems in the temperature of the machining process.
Boothroyd uses the semi-analytical method to calculate the maximum temperature in
the cutting process and compare the results of the analysis with the experimental
temperature [69]. In order to study the temperature distribution of chips, two different heatsource application methods are designed: rectangular and triangular regions. An evenly
distributed heat source is defined in the rectangular area where the frictional heat source isdistributed along the knife-chip surface in the triangular area. The maximum temperature
value of the cutting zone is obtained at the blade edge and decreases linearly from the tool
nose to the knife-chip separation point along the tool nose surface temperature.
Radulescu and Kapoor have made significant breakthroughs in the use of 3D energy
formulas in mathematical analysis models [26], the predicted temperature on the tool tocontact surface in continuous cutting is consistent with the previous experimental data in
the literature. Studies have shown that the temperature in the cutting process increases as
the cutting speed increases. Maekawa uses finite element modulus materials for properties
and thermal conductivity[70]. Ostafiev obtains the temperature distribution of the tool bystudying the heat flow on the tool surface [71]. The temperature of the tool was measured
using a thermo-sensitive coating in the experiment, and the conditions of heat transfer werestudied using the measured temperature. Huang's cutting force and thermal analysis modelstudied the effects of cutting forces on the temperature in the process by correcting thestrain, strain rate and temperature structure [72]. A uniformly moving oblique heat source
simulates the heat generation in the main cutting zone, and a uniform movement zone heatsource in the semi-infinite medium to simulate the heat generation in the second
deformation zone.
Komanduri and Hou made a general review and summary of the previous
19mathematical analysis model [73-76]. Evaluated the previous analytical work andpresented an analytical model to eliminate the problems posed by simplicity assumptions
in early work. These problems involve the heat transfer of materials and the flow of heat
between different materials. In the model, the conditions of different temperature
distribution of the first deformation zone, the second deformation zone and the heat source
application area are superimposed, and the temperature field distribution of the wholecutting zone under different cutting parameters is obtained.
There are many researches are placed on quality and productivity in manufacturing, it
becomes necessary to develop models that accurately describe the performance ofmachining processes[77], which shows the enhanced chip load model gives predictions ofboth cutting force signatures and surface error profiles that are significantly better than therigid system chip load model developed previously.
In the end milling process, the cutting force during machining produce selection of
cutter and workpiece which result in dimensional inaccuracies or surface error on finishedcomponent[78]. End milling process is one of the most important and common metal
cutting operation encountered for machining parts in manufacturing industry. By utilizing
this process, the material can be easily removed from the surfaces of molds and dies. End
milling is an efficient operation for making mold part (articles) with Aluminum 7075-T6
that has some advantages such as small and medium series production in plastic injection
molding and aircraft industry. Since end milling process is the final stage in manufacturing
a product, it is important to control the performance of this process[79]. Surface roughness
plays a significant role in determining and evaluating the surface quality of a product.
Because surface roughness affects the functional characteristics of products such as
resisting fatigue, friction, wearing, light reflection, heat transmission, and lubrication, the20

product quality is required to be at the high level. While surface roughness also decreases,
the product quality increases[80, 81].
Thermal generation and cutting temperatures in end milling have been long studied
by many researchers. They have applied multiple analytical and numerical methods tocalculate the temperature on the tool rake face, and determine the distribution oftemperature on the tool rake face and workpiece. Overall, there are two methods, analyticalmodels and numerical models.
Trigger and Chao[34] presented a steady state two dimensional analytical model topredict the average temperature in machining. They presumed that it existed two heatsources and one on the shear plane, the other one on the tool-chip interface, and theycalculated the average temperature rise of the chip when it leaves the shear plane result
from the average interface temperature of tool-chip in orthogonal cutting. They madeassumptions that ninety percent of the heat flow into chip and ten percent flow into theworkpiece, the shear plane contains heat source, the energy of friction and shear are
distributed consistently, no thermal shear energy by redistributing enter the chip during thecontact of a very short time. The Blok’s[82] partition principle was used to calculate the
distribution of heat at the shear plane. Under the same assumptions, Loewen and Shaw[66]used a similar method to compute the average temperature rise at the interface of tool-chip.
They considered that the chip and the workpiece keep a relative motion at the shear plane,
and the shear plane was presumed as stationary body. They also applied in Blok’s heatpartition principle in their analysis. And they obtained two temperature solutions for eachside heat source. They also made presumption that the interface of tool-chip is adiabatic,
and hence that the temperature rises in the chip only on the chip side but not on the tool.
Although they used Blok’s partition principle to develop a relatively straightforward
21

solution to predict the average temperature of the interface of tool-chip and the shear plane,
but it was impossible to match the two sides’ temperatures, and the two temperature
distribution curves they got are nearly consistent. Another analytical model developed by
Weiner[83] obtained the average temperature at the tool-chip interface. The author
considered that the moving speed of heat source is equal to the cutting speed on an inclined
plane, he also assumed that the chip has a normal flow to a negligible tool motion with thethermal conduction in the direction of tool motion, and the intersection between the shear
plane and workpiece stayed at ambient temperature, it was no heat loss from surface toworkpiece. In these consumptions, the temperature distribution in the workpiece, chip andtool could be calculated, and the temperature at any distances from the shear plane could
also be calculated. Others like, analytical model of three-dimensional in continuous andinterrupted machining process[26], the calculation of tool temperature in transientconditions,[84] the analytical model for combined effect of heat source of friction and shear
to temperature rise distribution[75] and etc. were developed.
In order to reduce and simplify the assumptions of analytical models, there are many
researches about modeling orthogonal metal cutting processes in finite element simulations.
But these kinds of simulations require massive input parameters which always get frompractical experimental work and mechanical property tests. Generally speaking, there are
two types formulations to implement the finite element modelling in cutting processes.
Tay et al. [85]were able to obtain the temperature distribution of two-dimensional,
which requires the strain-rate field. However, the input parameters should be obtained from
specific combination of the tool and workpiece from machining test. Muraka et al.
[86]simulated the process parameters such as cutting speed, coolant water and etc. by using
the finite element method. The model needs the experimental parameters. While Dawson
22and Malkin[87] assumed that the band heat source did not move with the shear to theworkpiece, instead, it move with the cutting velocity. A thermos-viscoplastic machining
model was developed by Kim and Sin[88] for the mechanics of steady-state orthogonalmachining process. They assumed that the workpiece is viscoplastic and the flow stress isfunction of strain, temperature and strain rate, and their results matched the experiments inaccordance with the workpiece and tool temperature. Moriwaki et al.[47]presented a FEM
model to the analysis of the orthogonal cutting process and the role of the tool to the micro
machining. And this model was just well meet low cutting speeds. More recently, Mamalis
et al.[89] used the commercial implicit finite element code MARC to present a coupled
thermal-mechanical model of plane-strain orthogonal metal cutting, they determined thetemperature fields in workpiece, chip and tool and other parameters. Tuğrul Özel andTaylan Altan[90] developed a method to predict temperature, chip flow, tool stresses andetc. in cutting process by using finite element analysis. Regardless of cutting conditions,the highest temperatures were predicted at the primary cutting edge of the flat end milling.
W. Grzesik [91]used an integrated thermal analytical and simulation models to determine
the temperature distribution in the cutting zone. Their results showed that the distributionof temperature changes with heat flux transfer conditions, and they compared the simulated
results with the experimental results. D. Umbrello et al.[92] used five different sets work
material constants to implement the behavior of AlSi316L steel in a numerical model byusing the Jahnson-Cook’s(J-C) constitutive equation. They obtained temperature
distribution in finite element modeling of orthogonal cutting of AlSi316L. E. G. Ng etal.[93] resented a FE model by using FORGE 2® to simulate the distribution of temperatureand cutting forces when orthogonal turning a hardened hot work die steel, and the obtained
a good correlation between experimental and simulated results for temperature. S. Keith
23

Hargrove and Duowen Ding[94] using a finite element method to predict the temperature
distribution in the workpiece under different cutting parameters, and they determined a setof cutting process parameters through minimizing the thickness of the temperature
affections. Bo Song et al.[95] simulated the temperature distribution on a three-dimensionalmodel, they found that it exists a great temperature gradient from platform surface, and the
maximum depth of molten powder layer, and the experiment date meets the simulation
well. Yourong Liu et al.[96] simulated the temperature distribution of surface of Si3N4 andtitanium ceramic tools for turning different metallic materials based on a mathematic modelof heat source, and they used thermal video system to check the computed temperature
distribution, the results showed a good agreement.

2.2 Measurement technology
There are many ways to measure the cutting temperature during the cutting
experiment. The thermocouple method is a commonly used cutting temperature
measurement method. Thermocouple temperature measurement method of the device
structure is simple. According to the measurement principles and the use of different
methods, temperature measurement methods can be divided into following threethermocouple methods [97].
1) Natural thermocouple method is used to measure the average temperature of the
cutting area, natural thermocouple method to measure the cutting temperature is simple andreliable. The disadvantage is that the temperature of the designated point of the cutting area
can not be obtained, the tool or workpiece material changes, the thermocouple calibration
curve must also be re-obtained.
2) The artificial thermocouple method can be used to measure the temperature of thetool and the workpiece, and the specific artificial thermocouple material is calibrated only24

once, and the thermocouple material can be selected flexibly. However, artificial
thermocouple buried very difficult to embed superhard tool materials (such as PCD,
ceramic), limiting the application of artificial thermocouple method.
3) The semi-artificial thermocouple method is a combination of natural thermocouple
method and artificial thermocouple method. The working principle of measuring the
cutting temperature is the same as that of the natural thermocouple method and the artificial
thermocouple method. As a result of semi-artificial thermocouple temperature
measurement using a single wire connection, do not have to consider the insulation problem.
As we known that cutting temperature measurement is an important method, metal
cutting humidity research and theory predicts the results of heavy to approach. But theparticularity of the metal cutting process the cutting temperature measurement is difficult,such as small cutting area, uneven temperature distribution and large temperature gradient,chip and tool contact, multiple parts in high speed moving. Temperature measurement
method is commonly used in metal cutting process as shown in Figure 3, mainly includes:
thermocouple method, optical radiation method, thermal radiation, metallographic
structure, etc.[98]25Figure 3 Method of metal cutting temperature measurement[98]4. Sensors and sensing devices[51].
The techniques for the monitoring of machining have been traditionally categorized
into two methods: Direct and indirect. The direct monitoring methods can achieve a highdegree of accuracy, but due to numerous practical limitations, they are characterized aslaboratory-oriented techniques. On the other hand, the indirect monitoring methods are lessaccurate but more suitable for practical applications, at machine shop level. Auxiliary
quantities are measured and empirically correlated with machining phenomena.
Direct methods require the use of visual systems, lighting, cutting fluids, etc., which
may interfere with the monitoring system and machine tools, resulting in unstableproduction environments. An unmeasured system has been developed for assessing tool
wear using a laser sensor that measures displacement and light intensity. These methods
are promising because they can measure flank tool wear greater than 40 microns [99].
Moreover, the combination of CCD cameras can be used simultaneously for the acquisition26

of the tool images during machining[100]. Based on image acquisition and the creation of
an image database, containing tools stressed under extreme conditions, an algorithm forfurther data processing can be created. Due to the stability issues of the direct measurement
methods, other systems, using signals such as force, torque, acoustic emission, acceleration
have been developed. An important factor of a monitoring system’s design is thephenomenon, which data will be acquired from, and the decision of the signal characteristic
to be correlated with the phenomenon. Not all the types of sensors can measure the same
phenomenon, with the same accuracy. Load cells are often attached to the machine structure
for the measurement of cutting forces. Expensive dynamometers are often used inlaboratory settings for precise measurements; however, they are impractical for industrial
applications. In [101] forces in milling operations were predicted from the current of thefeed axis drive.
2.2.1 Thermal-couple and Infrared thermal-couple
Concerning measuring methods of workpiece temperature, there are two populardevices: thermocouples and infrared thermography. Thermocouples are attached on theworkpiece surface. Therefore, it can measure only non-machining surface and the numberof measurable points is limited. On the other hand, the infrared thermography can measuretemperature distribution of surface. However, the infrared thermography is disturbed by
cutting chips and coolant in the practical production environment. Therefore,
thermocouples system must be employed in actual machining situation.
1) Natural thermocouple
As known dynamic thermocouple, is a widely used cutting temperature measurement
method. As shown in Table 1 the natural thermocouple uses the relatively moving cutting
tool and the workpiece as the poles of the thermocouple. When the insulation is insulated27

from other parts of the machine, connect the two poles to form a closed loop and measure
the thermoelectric potential using the millivolt meter. The use of the Tool-workpiecethermocouple needs to be calibrated to obtain the correspondence between thethermoelectric power and the humidity. During the metal cutting process, the cutting heatcauses the temperature of the contact between the tool and the workpiece to rise, and thethermocouple is generated at the cold end of the thermocouple. The cutting temperature
can be obtained by the thermoelectric potential measured by the millivolt meter.
Natural thermocouple method is a simple structure, it suitable for cutting theexperimental device does not change, such as cutting speed on the cutting temperature.
However, the application and accuracy of the natural thermocouple method are limited by
the conductivity of the tool and the workpiece. The cutting fluid cannot be used during the
cutting process. It is also controversial whether the measured temperature is the lowest
temperature or the average temperature of the contact surface.
2) Built-in thermocouple
Another widely used thermocouple is a built-in thermocouple, also known as an
artificial thermocouple, for measuring processing and friction temperatures. The built-in
thermocouple uses two calibrated materials to form a thermocouple, place it in a turning
tool or cut the workpiece to obtain the corresponding point temperature. Built-in
thermocouple in the turning tool or wash the workpiece within the pre-perforated
thermocouple, the size and location of the bar has strict requirements. By changing thedepth of the hole, the built-in thermocouple can get the temperature of the tool or thedifferent parts of the workpiece but cannot directly measure the tool and the workpiecesurface temperature. For the tool surface and the workpiece has been processed surface
cutting temperature, through the establishment of tool and workpiece heat conduction
28

model, based on the thermocouple measured tool and the workpiece temperature using theheat conduction reverse method to obtain.
Built-in thermocouple easy to use and good reliability. It should be noted that the useof built-in thermocouple requires pre-punching a hole, which may damage the internal
structure of the tool or workpiece, affecting the tool or workpiece strength and otherproperties, while the hole in the tool or workpiece damage the original temperature field
distribution, the temperature measurement results deviate from the original temperature.
Near knife-chip contact area is generally considered not suitable for the use of built-in
thermocouple, the thermocouple size requirements are more stringent.
3)Infrared thermal-couple
Infrared theory, the use of infrared cameras and lines of micro-technology to promote
the use of infrared thermal imaging technology. The use of infrared thermal imager canquickly change the position of the temperature measurement point, and make timely
response, electronic video signal temperature measurement results are conducive to followup processing analysis. It should be noted that the infrared thermal imager as a non-contact
temperature measurement technology, can only get the surface temperature of the object,
the cutting of metal parts and the internal temperature of the tool cannot do anything. The
correct setting of the emissivity of the thermal imager is an important part of the infrared
thermometer temperature measurement. Since the reading of the measured temperature is
proportional to the emissivity, the irrational setting of the emissivity will lead to themeasurement error.

29

Table 1 Measuring methods of workpiece temperature

Technology

Category

Thermo-paint
Thermocouple

Toolworkpiece
CrossedMain Advantages

Main Disadvantages

Evaluate

Cheapest
Simple build

Inaccurate,
Cannot change locationof measurement point

Proposed calibration
Calibration difficult,limit on response
time

Temperature ofdifferent positions

Cannot be used formilling, drilling,obtained withoutchanging theexperimental device

grinding

Built in

Pyrometer

Infraredradiation

Quick response,
Suitable for measurin
g surface temperature

Melting point
powderInfraredthermal imager

Quick response
Opticalinfrared

Cheap

Cannot directly
measure the surface
temperature, damage tothe thermocouple
Emissivity changes of t
he measured object willaffect the measurement
results
Need to warm up,
expensive
Inaccurate, the result is
an approximate
temperature range

More sensitive to theenvironment temper
ature
Cannot extract data
directly
No calibration
required

Thermocouples have advantages and disadvantages, we cannot completely measure
every point in the actual production, so in the following chapters, we propose a combinationof simulation and production methods to predict the temperature

2.3 FEM2.3.1 FEM used in manufacturing
In order to maximize the limited measuring points, this paper presents a method toevaluate an effectiveness of each measuring point from aspects of stability of measurement
and sensitivity to the process variation. As a quantification method of an effectiveness of30

the measuring point, a finite element method (FEM)–based thermal simulation is utilized.
In the 1940s, the rapid development of aviation industry for area plane machine
structure is put forward more and more high demand, the engineers had to in precise designand computing, at the same time plan for the development of computer technology makes
use of numerical computing method to study the cutting process is possible. In 50s ~ 60swith the finite element method (FEM) in the rapid development, limited to the early stage
when the level of computer hardware and software and theory, the finite element numericalcalculation method was not popular in the engineering. But at 60s~70s, there was a large
general-purpose finite element program, they were good at doing calculation precisely andreliability.in the following 40 years, large number of scholar’s studies the basis of FEM.
At an early stage when the level of computer hardware and software and theory, thefinite element numerical calculation method is not in the engineering. They gained
popularity in 60 ~ 70s a large general-purpose finite element program, and they calculated
precision and reliability. Efficient computational efficiency became the analysis of theengineering structure calculation tools. In the later 40 years, scholars in a large amount ofbasic research. In 1973, Lee and Kobayashi of rigid-plastic finite element method (fem) is
put forward for the first time Matrix column type, the extremely promoted the limited
element numerical value simulation technology in the application in the process of metal
volume into shape [102]. In 1974, Tay. Stevenson and Davis for the first time by finite
element method to calculate the orthogonal cutting tool, cutting, work pieces thetemperature distribution. Year of 1979, Mróz, Norris and Zienkiewicz Put forward the
sticky plastic material such as finite element formulations and viscoplasticity is deduced
Finite element of penalty function method, the high temperature molding the analysis of
the problem is resolved [103]. In June 2006, The United States Net shape manufacturing31

engineering research center, Ohio state university Professor Altan and others at CIRP highspeed cutting meeting, at this stage of finite element simulation of high speed cutting work
made a detailed report, and puts forward the future research.[104]
The FEM is an appropriate tool to gain the necessary knowledge of manufacturing
technology, the quantitative results can be obtained from a numerical analysis are importantto the optimization as well. At the same time, taking all the necessary boundary conditions
into consideration by the mathematical equations which describe the process is socomplicated, which we can decrease some conditions make the simulation much simple todescribe questions. FEM thermal analysis’ target is to investigate effects in metal cutting
which are not directly measurable.
2.3.2 The basic equation of heat conduction in cutting progress
As shown in Figure 4, the one-dimensional heat conduction process, the upper andlower edge of the adiabatic boundary, the left and right sides of the temperature were T1
and T0 (T1> T0), then the heat flow through the cross-sectional area A is Q, the heat flux
is defined as heat flux density (Eq. 2-1)

Q
q
A

2-1

Based on a large amount of heat transfer experimental data, Fourier concluded that in thesolid thermal conduction process, the heat flux and the temperature gradient is proportionalto, see equation (2-2).

q=  kdTdx

2-2

Where K is the thermal conductivity of the material. After substituting equation (2-2) into
equation (2-1), we get the general formula (2-3) for the one-dimensional Fourier thermallaw.
32Q =  kA

dT
dx

2-3

For H-dimensional heat conduction problems, Fourier's heat conduction law can beextended to (2-4).
q=  kT

2-4

Figure4 One-dimensional heat conduction process

The production and transformation of energy are always accompanied with the cutting
process, the vast majority of plastic deformation work (90%) and the friction between
workpiece and the tool contacted surface is converted into heat, only a small part of themis stored in workpiece in the form of elastic deformation energy, so the plastic deformationand thermal effect of friction must be fully taken into account in the thermal analysis in
cutting process. According to the law of conservation of energy, the heat balance equationof heat transfer in solid is shown in Eq. 2-1.
(qx q y qz T) +q   c x
x
y
z
t

2-5

In Eq. 2-5. q x , q y , q z is the heat flux in the direction of x, y, z, respectively, that is

the energy flowing in unit time and unit area. q is the heat conducted by internal heat33

source in unit time and unit area.  is the density (kg/m3), c is the specific heat (J/kg·K),
T is the temperature, t is time.

In the research process of metal plastic deformation, workpiece and tool are normallyregraded as a whole piece, and the tool is regarded as rigidity which has no deformation inmachining and exists no internal heat source, furthermore, the internal plastic deformation
work is considered into internal heat source, as shown in follows.

q workpiece    q workpiece  0 

2-6

In the Eq. 2-6.  ,  is equivalent stress and equivalent strain rate, respectively. η is
Heat generation efficiency that represents the transformation ratio from mechanical energy
to heat (η=0.8~0.9).
In the heat conduction of solid, it is usually assumed that heat flux is proportional to
the temperature gradient, as shown in Eq2-3.
qxi  xi

T
xi

2-7

In Eq. 2-7, xi is the heat conductivity coefficient in different direction, as the heatflow from high temperature to low temperature, so the left of Eq. 2-4. is minus. Consider
that the analyzed material is isotropous, so x  y  z  0 , that is

c

T 
T

T

T 
 (
)  ()  (
)+ q
t x
x
y
y
z
z

2-8

Eq. 2-8 is the heat balance differential equations in the solid heat conduction process.
2.3.3 2-D heat conduction
The temperature distribution on the tool-chip interface for sharp tool edges in dry
machining can be calculated with an analytical model, as proposed by Komanduri et.
34al[105]. It is believed that the temperature rise in dry machining is caused by the primaryheat source at the shear plane and the secondary heat source at the tool-chip interface, threeheat sources/losses are considered: the primary heat source due to shear deformation, thesecondary heat source due to friction, and the heat loss due to air-oil mixture cooling, asshown in Figure ,The following sections describe how the heat loss due to convection on
the tool flank face and workpiece is calculated based on a stationary heat source model (forthe tool) and a moving-band heat source model (for the workpiece). The temperature
distribution on the tool-chip interface is then estimated by the superposition of temperaturechanges due to different heat sources and heat losses.
Temperature fields and subsequent heat flow into the components chip,tool andworkpieceare of major significance for metal cutting.the temperature field in the tool
influence for example the type of wear and the tool life and hence the temperatures evolving
in the workpiece are responsible for alter product quality, and heat during metal cutting
process is assumed to be generated in three areas within cutting zone.

Figure 5 Heat sources and heat losses for the 2-D model

In this paper, the cutting edge is cut along with the edge of the scrubbing force, andthe friction heat source of the shear zone and the flank wear zone of the main cutting zone
35

are equidistant along the milling cutter into several rectangular heat sources. The calculated
thickness and cutting force of each element of the cutting heat source can be used to solve
the problem that the cutting geometry is complicated, and the cutting thickness caused by
the helix angle of the cutting edge is changed along the axial direction of the cutter. The
influence of the instantaneous cutting heat source on the required temperature field isanalyzed by dividing the machining time course, and thus the thickness of the cutting is
calculated. The above Figuretakes into account the main shear zone heat source, theflank face and the workpiece Frictional heat source and heat transfer model of mirror heatsource. According to the above ideas, each cutting heat source is discretized into
rectangular heat source element, and the angle between the heat source micro-element andthe flank face wear zone heat source micro-element is 180°-  n . The shear surface heatsource element and its mirror heat source of the angle for the flank wear with heat source
element and its mirror heat source coincidence. For the cutting shear surface heat source
microelement in each time element, it’s heat flux density,q , j ( , z ) Vs dF , j ( , z )csc n h j ( , z )sec  dz

2-9Instantaneous flank wear Heat flux density is，
qf , j ( , z ) VdFtf , j ( , z )VB sec  dz

2-10

2.3.4 Determination of heat proportion of heat source in flying face wear
The derivation process of the heat source heat distribution ratio of the flank wear zone
is similar to that of the shear surface heat source. The average temperature rises around theflank wear zone is calculated from the workpiece side and the tool end, respectively, andthen the two are equalized to obtain their analytical expression formula. The same reason,
36

from the workpiece side of the analysis, in a time interval, the flank wear with heat source
micro-element can be seen as the workpiece has been processed on the surface of the bevel
movement, so the use of the upper section of the ramp movement of the rectangular heatsource model can also predict the average temperature rise of the micro-element  f ,w .

f ,w Bf , j ( , z )qf , j ( , z ) w
2 wVƒf

2-11

In the equation, Bf , j ( , z ) shows the ratio of the micro heat powder to the amount ofheat transferred to the workpiece in the element time， ƒ f is based on the geometric andkinematic relationship of the flank face heat source is based on the geometric and kinematic
relationship of the flank face heat source, the following equation is used to convert:

0 = , V0  V , W0 

VB
V sec  dz
, L0 
4 w
4 w

2-12

From the tool end, the flank wear zone heat source is a rectangular heat source on theflank face, and the relative position of the tool does not change during the cutting process.
Therefore, the temperature rise solution of the static rectangular heat source is calculated
the average temperature rise  f ,t of static rectangular heat source in the flank wear proposedby using Trigger and Loewen et al. mentioned method.

f ,t (1  Bf , j ( , z ))VBqf , j ( , z )

t

A

2-13



In the equation 2-13, the thermal conductivity of the tool material t ,and A is thedimension less area factor associated with the aspect ratio of the heat source area, given by:

37

2
2
2 
1  sec  dz  1  sec  dz
VB    sec  dz   sec  dz   sec  dz 
1  sec  dz  1  sec  dz A  sinh 1  sinh 
 
  
 
 
 1  
sec  dz    VB  
 VB   VB  VB  3  VB  3  VB  3  VB


2-14
also：

f ,w  f ,t

2-15

Hence, the simultaneous equations 2-13, 2-14, 2-15, considering the meshingdiscrimination coefficient, can be obtained:

Bf , j ( , z )   j ( z )1t w f f
12 VBw AV

2-16

It is worth notice that the flank wear with a mirror heat source and the flank wear withheat is exactly same, the spatial position is completely coincident, it is equivalent to theeffect of the original flank wear with heat source heat doubled, in the discussion below will
only consider the flank wear with heat source, the mirror heat source of the calculation
process is exactly the same, do not have to repeat.
2.3.5 3-D heat conduction
Internal ration at a rate q per unit volume causes an accumulation=qdxdydzdt,
It is understood that the mechanical behaviors of workpiece are sensitive to environmental
conditions, such as heat conductivity, heat transfer coefficient or temperature.
2.3.6 Generated heat in end milling
Compared with turning, end milling is a discontinuous machining operation, with two
totally opposite phases. In the temperature rising phase, the insert performs a cutting action
and is heated by the heat source from the secondary and the tertiary deformation zone,
causing its temperature gradually reach the top temperature[106].

38

0.5






2.3.7 The thermogenesis of plastic strain
The analysis of heat-mechanical coupling is assumed that the contribution ofplastic strain in unit volume to heat flux as pl

 pl   /  1
 pl n / (   t )
2t

2-17

In the Eq. 2-17 , η is the non-plastic heat generation rate, t is the time interval of
an incremental step,  is the stress, n is the flow direction matrix. Substitute thefollowing equation into the Eq.

   el   pl   th
 pl

q
  D( 0  1)



2-18

In the Eq. 2-18, W is strain energy density which is related to elastic strain andtemperature,  is total strain,  el is the elastic strain,  th is the strain induced by hot
ductility, q is equivalent stress of Mises or Hill. D( ) and n( ) are the parameters
corresponding to temperature. So, relations of plastic strain to produce heat after
incremental step is finished.

 2W
 2W
 th






/
(



   pl ) el  el  el


r
r
r
 pl   pl nr   pl ( n   n  pl  n  )


 pl



2-19

2.3.8 The thermogenesis of tool-chip friction
In the analysis of heat-mechanical coupling process,  is defined as heat generation
rate which determines the ratio of the energy loss of sliding friction transfer to heat. The
heat conducts to two contact surface is f1 and f 2 , respectively, and f1 +f 2 =1 . The
setting of these two parameters is based on the assumption that the contacted surface has
no heat capacity and the heat must be diffused by heat conduction or heat radiation. The

39

energy loss of friction is:

q A  f1   Pfr
qB  f 2   Pfr

2-20

In Eq. 2-20, q A and qB is the heat flux from major face to subordinative face. As therising temperature of tool-chip by heat friction, the conduction and radiation of heat flux
exists in the contacted surface of tool-chip. Hence, supposed the heat transfer from surface
A to surface B，the two surface energy transfer to their inner, q1 and q2 are as follows.

q1 =q A  qk  qr
q2 =qB  qk  qr
qk and qr

2-21

is the heat generated by conduction and radiation, respectively, theirformulation are as follows.

qk  k (h, p, )(1   2 )

z 4
z 4
qr  C[(1   )  ( 2   ) ]2-22

k (h, p, ) is a coefficient of heat conduction which is a function about the averagetemperature of contact point, contacted interference and pressure. C is a parameter ofradiation which is related to the radiation capacity of contacted surface and parameters ofmutual view, and  z is the absolute zero value of used temperature unit.

2.4 ANSYS APDLThe basic idea of the finite element method is to separate the continuous structure into
finite elements and set a finite number of nodes in each element. The continuum works area set of elements that are only connected at the node. The node value of the field function
is taken as the basic unknown quantity and an approximate interpolation function isassumed in each unit to represent the distribution rule of the field function in the unit andthen some of the principles in the mechanics are used to establish the finite element40Equation, which makes the infinite degree of freedom problem in a continuous domain intoa finite degree of freedom problem in the discrete domain. Once solved, we can use thenode value of the solution and the set interpolation function to determine the field function
on the unit as well as the whole assembly[107, 108]. The internal process of the finite
element solution can be seen from the Figure 5.

Figure 5 Flow chart of finite element

With the improvement of computer graphics and simulation technology, thesimulation and optimization technique based on cutting process gradually substitute themethod that obtain the parameters in cutting process by cutting experiments and empiricalvalue in traditionally. The simulation of cutting process exists in the whole process ofrelative motion between workpiece with tool and display the distribution of heat flux,
temperature, phase transformation and stress dynamically, which overcome the defects intraditional experimental methods and become an efficient approach to study the mechanismof cutting process. Compared to experimental methods, simulation, as an efficient assistant,41

implement a function of temperature, strain and strain rate to material properties of
workpiece, and fully take account into the interaction between different parameters inmachining, and give a guidance to research the mechanism and optimizing of technicalparameters in cutting process.
ANSYS software not only has powerful function, good generality, but also has good
openness; users can accord their needs in the standard on the ANSYS version function
expansion and system integration, which can be generated in accordance with the needs ofuser version of ANSYS program. Developmental function mainly includes three parts:
ANSYS Parametric Design Language, (APDL), User Interface Design Language(UIDL)
and the User program features (UPFs), among them APDL and most UIDL are popular.
Generally speaking, the ANSYS software provides two kinds of working modes, namely
the man-machine interactive way (Graphical User Interface, GUI) and to command flow
input (BATCH) as the main form of APDL. APDL method is suitable for large, complex
finite element analysis, but the other languages will not be used APDL file line by line towrite, but it makes full use of GUI analysis for the first time to generate the LOG file,modification will be needed in the design of the command stream file, and then the APDL
file can be got from the increasing of the APDL command control.
We introduce end milling workpiece as the research object, using the finite element
model parametric language APDL programming techniques and EXCEL data visualization
technology, comparing with the involved temperature parameters related temperature
distribution analysis, and the contrast analysis was carried out on the simulated data andexperimental data, and the characteristics and laws of S45C cube workpiece with thechange of the parameter settings were explored and the visual curve were shown by
EXCEL.
42

2.5 Combine measurement and simulation
End milling is a strong thermal coupling of the plastic deformation process, the tooland the workpiece to withstand periodic mechanical load and thermal load, the cutting
process of cutting temperature research also has the following problems:
1) Cutting more continuous cutting in the cutting force and cutting heat and otheraspects of the existence of significant cyclical intermittent cutting process of cutting theheat source of the cyclical changes on the tool, the workpiece temperature needs further
study.
2) The traditional cutting theory is difficult to solve the problem of high-speed cutting
temperature, high-speed cutting process should be for the heat convection, heat distribution
ratio and heat conduction theory to study.
3) High-speed cutting process cutting tool temperature measurement technology is not
yet mature, need further study.
Our laboratory has been studying monitoring temperature while end milling process
for a long time. Mr.Tanaka gave an introduction about use thermal-couple to detect
temperature in end milling both by experiment and computer simulation[109], and fromthe previous research of our group, we already had a clear illustration by combining
experimental results and the simulation, the temperature distribution on workpiece could
be monitored by confirmed parameters. Last year, Mr. Azhad, who was in our groupproposed response surface by ANSYS workbench, which illustrated the accurate results
did by Mr.Tanaka and based on all work did in these years, but the variations of machining
process are generated by using the different combination of model parameters in end
milling process, so I am still looking for propose a new method, which can summarize thisthermal expansion based on the method to select appropriate temperature measurement
43positions based on a variation conscious machining evaluation.
No matter in industries or automotive adopted Computer Aided Engineering (CAE)
to decrease the number of trials of actual experiments, effective experimental mechanicaltests are really need the validate CAE results, shown in Figure 6. In the previous research,
the experimental setup and some of the experimental results are based on the work of
Tanaka[110] . In this section, only the main points of the experiment described in detail inthe mentioned research was outlined. Mr. Tanaka proposed an estimated method to monitor
temperature and relative parameters by combining experimental results and simulation (seein Figure 7).

Figure 6 Comparison of conventional simulation and proposed Sensor-configured

simulation

44

Figure 7 Combination of measurement and simulation

2.6 Summary
1. This chapter summarize conventional thermal analysis in end milling process.
2. Some theoretical issues such as the heat loss calculation formula, the results of various
heat consumption are explained.
3. We consider the combination of simulations and experiments to complement each other's
weaknesses.
4. Combined with the work of predecessors, we learned that simulation can predict theexperimental process when the model parameters are appropriately identified.

45

3.Article Propose
3.1 The proposal of this subject
Advanced manufacturing technology is rapidly transforming the global competitive
landscape. The companies — and nations — that act now to seize its promise will thrive inthe 21st century.
We proposed a systematic monitoring method for finish machining of complex 3dimentional parts for small-lot production. The method is to evaluate an effectiveness ofeach measuring points from aspects of stability of measurement and sensitivity to theprocess variation. As a quantification method of effectiveness of the measuring points, afinite element method (FEM)–based on thermal simulation is employed. From the resultsof the nominal simulation and sensitivity analysis, each measurable point is evaluated.
Evaluation procedures are explained in Chapter 5. By using some case studies, feasibilityof the proposed method is also confirmed in Chapter 6.

3.2 The main contents of this thesis
Small-batch, high quality and agile production methods have been required by mostmanufacturers. End-milling is a promising method to satisfy the requirements. In order toachieve accurate and reliable end-milling, it is necessary to understand thermal behavior inmachining process. As a result of rigidity improvements of modern machine tools, athermal expansion of workpiece becomes an important phenomenon in the precision
machining [5]. Many investigations have been reported to predict thermal influence in endmilling [8, 110-112]. Furthermore, methods to identify process parameter for thermal
simulation are also discussed[113, 114]. On the other hand, a concept of model based46

monitoring has been expected as an important technology for advanced machining, because
detailed understanding of workpiece state is considered as an effective method formachining process control[115]. Based on these technological backgrounds, a method toestimate temperature distribution of workpiece by combining workpiece measurement andthermal analysis has been proposed[110] .
----------------------------------------------------------------------------------------------------------Introduction and Survey(Chap.1 & 2)
Measurement technology
Monitor technology
Thermal conduction
Simulation process
Article Propose(Chap.3)
Combine measurement and simulation
Method and Principles(Chap.4)
Temperature field
Thermal estimation(Chap.5)
Orthogonal tests
EoP formula
Applications (Chap.6)
Case1
Case2
Case3
-----------------------------------------------------------------------------------------------------------Conclusion and future works(Chap.7)
Figure 8 Organization of this thesis47

4 Method and Principles
Based on the moving heat source method, this chapter establishes the cutting
temperature model of the workpiece in continuous machining. The continuous heat sourceis substituted into the established cutting temperature model, and the influence of thetemperature distribution on the cutting surface after moving the heat source is analyzed. In
the case of cutting without the use of coolant, the surface of the workpiece can beconsidered an adiabatic boundary, that is also to say, there is no heat exchange between theworkpiece and the outside, and the heat generated by the cutting heat source reaches thesurface of the workpiece and is no longer conveyed.

Fourier heat transfer theory

Move the heat source model

Workpiece temperature model

Effect of thermal parameters on cutting

Temperature Distribution
Figure 9 Flowsheet of method

4.1 Mobile heat source method based on Fourier’ law heat
conduction
Heat conduction in solids is the result of vibrations of the solid lattice and of the48

motion of free electrons in the material. In metals, where free electrons are plentiful,thermal energy transport by electrons predominates. Thus, good electrical conductors, suchas copper and aluminum, are also good conductors of heat. Metal alloys, however,generally have lower (often much lower) thermal and electrical conductivities than thecorresponding pure metals due to disruption of free electron movement by the alloying
atoms, which act as impurities.
Metal cutting process in accordance with the metal deformation cutting heat generated
by different positions are mainly divided into shear zone heat, tool-chip contact surface
heat and flank heat source, the three cutting heat relative to the workpiece are in the moving
state, so the workpiece processing moving heat source method is widely used in theanalysis of temperature [116].
To solve the three-dimensional thermal conductor in the position P( xl , yl , zl ) of theinstantaneous heat caused Q by any moment  , the temperature rise T at any locationA( x, y, z ) based on Fourier heat conduction of non-steady-state heat conduction

differential equation is Eq.4-1.
  2T  2T  2T T a 2  2  2 

y
z 
 x

4-1

F
 a  a 2  b 2  c 2  F

4-2

Fourier transform get Eq. 4-2.

After the phase shift and integration to get the following formula
F ( K , )  Ae ak 
2

4-3

Using the Fourier transform table in the math manual, an inverse Fourier transform isapplied to Eq. 4-3 using the Fourier transform of the three-dimensional Gaussian49

distribution shown in Eq. 4-4. According to the law of conservation of energy, get thetemperature rise T at any position at any time as shown in Eq. 4-5.
 f ( R)  B e b1 R
1

B1 3/2  k 2 /4b2e
F (K ) 
b13

2 2

Q
T
e
c  (4 ar )3/2

4-4

( x  x12 )  ( y  y12 )  ( z  z12 )
4 a

4-5

Q means the Instantaneous heat transfer energy released by the heat source, c is the specificheat capacity,  is density and a is thermal diffusivity.
Based on the instantaneous point heat source temperature solution, by integrating thetime and space coordinates, we can deduce the temperature solution caused by the infinite
heat source in an infinite heat transfer medium Eq. 4-6.
 

Q   x    
T  1 e  2a 
K0 
2
 2a

 x    y 2 
2



4-6

Which,  is heat source moving speed and K 0 is zeroth order modified Bessel functionof the second kind.
Of course, in order to avoid mass transfer heat transfer compensation issues,
Hahn's[117] proposed a heat source analysis method of cutting heat source moving along
the shear plane, see Figure 10, and based on the chip deformation process to establish amoving belt heat source model to solve the metal cutting process workpiece cutting
temperature. During the cutting process, the material of the cut workpiece moves along the
cutting plane and then generate plastic deformation to form chips, so the heat source of the
cutting plane can be regarded as moving along the cutting plane inside the workpiecematerial.
50

Figure 10 Hahn's moving model

From the moving heat source by pioneering works of Rosenthal on thedetermination of temperature distribution in a solid resulting from arc
welding[118]. We got the idea if the solid is long enough in the direction of motion ascompared to the penetration depth of heat transfer field, in quasi-stationary condition, themathematically defined by setting

T 0 [64].
t

An observer stationed at the point source fails to notice any change in the temperaturearound him as the source moves on, this state of heat flow is called quasi-stationary. This
theory of quasi-stationary sates simplifies many practical problems to the point where
calculation is possible and provides a method of attack on other problems which have notyet been put on a mathematical basis.

51

4.2 Modeling and solving of workpiece temperature field
In this paper, in order to ignore the geometric complexity of the tool, the thickness ofthe cutting edge along the cutting edge of the problem，the third deformable zone heatsources are separated into several heat source micro-units. In estimating the cutting
temperature with tool flank wear in dry machining, Huang and Liang [119] developed a
new model to predict the heat generation due to the tool flank wear. A rubbing heat source,
because of the presence of tool wear land, was added to the heat sources methods under
dry machining condition. The cutting temperature distribution was thus calculated with thetool flank wear effect.
Temperature rise of tool-workpiece contact area forming in the milling process ismuch higher than the final machined surface owing to thicker instantaneous uncut chip
thickness when the cutting tool passes by[120]. Cutting process presents periodic and nonstable, tool and workpiece to withstand periodic mechanical load and thermal load impact.
This series of characteristics makes the intermittent cutting temperature theoretical model
much difficult to analyze, and the cutting process of the tool and the workpiece temperaturefield of the unsteady state also makes it difficult to measure. Compared with the theoreticalanalysis and experimental study on the cutting temperature of the cutting tool and theworkpiece, the finite element simulation can effectively deal with the highly nonlinear andunsteady problem of intermittent cutting. The simulation analysis method of theintermittent cutting process combines the finite element analysis theory with the metalelastic-plastic deformation and other cutting theories. Through Lagrangian and Eulerian,
the differential equations of motion and the heat conduction differential equation must be
solved, in order to obtain the cutting force, cutting temperature and metal cutting
deformation.
52

The effects of heat and thermal management of structures is more and more criticalfor performance limits which are pushed further by the need to have lighter, smaller andmore efficient designs. Convection, radiation and conduction loads are obvious to parts
accuracy, but it needs including the effect of power losses and thermal energy from frictionand external sources.

Simulation
theory analysis
Heat
conductivity
Processing
model

Heat flux

Workpiece
temp.

Heat transfer
coefficient

Figure 11 Plot of this chapter

4.2.1 Approximated simulation model
Following steps are based on this state steady thermal end-milling simulation.
Firstly, using mechanical-APDL programming method to establish model ofworkpiece under end milling process is not have an extremely influence to propose the new
method about predict the sensitive points during cutting process.
In this study, the temperature of the workpiece at any time is obtained by integrating
the instantaneous heat source element along the time axis to the temperature rise of thetime required, and then the change of the temperature field of the workpiece can be
described. In addition, in order to reveal the periodic variation of the temperature rise of53

the workpiece during steady state, the temperature data acquisition involved in this paper
is not required for the specific type of cutting. We approximate the entire workpiece cutting
process as an ultra-thin cutting, so that the whole cutting process is similar in the welding
process, the heat source instead of the tool after the whole process can ignore the impact ofchips.
This thesis initial set is about from the heat source temperature distribution in theinitial temperature atmosphere and add the same as the actual operation of simulated heat
flux and constraint condition, then we calculate the temperature distribution in theworkpiece.
Secondly, based on the transient thermal analysis, add heat flux in selected element,
and then we use the loop orders to push the heat source moving on paths.
Lastly, from postprocessor all data can be picked out, we chose all the date which also
can be detected followed easier picked criterion in actual experiment, and then export datefiles.
4.2.2 Simulation process
4.2.2.1 Pre-process
We take advantage of the classic ANSYS® parametric design language (APDL) to
develop the simulation process. Pre-processing task is to establish various parameters under
the condition of heat conduction current testing geometric model, an ultra-thin cutting
setting and the transient thermal analysis estimated completed.
4.2.2.2 Modeling and meshingThis step we complete until to mesh generation, including: define material properties,
modeling and mesh steps. As the properties of the workpiece and the tool material, theprocessing method and the processing parameters and other factors determine the heat54

generation during the cutting process. In order to verify the effectiveness of the model, theS45C steel (body cubic lattice BCC) workpiece material was selected as an example forintermittent cutting.
Because the price is not too high, and it is a carbon steel which is well circulated in
the market, it may be that the S45C is used for the time being in the case where the generalversatility is high, and the special property is not necessary for the time being. Dimensionsof the S45C workpiece (length (L), width (W) and height (H)) are illustrated in Figure 12.
Top surface of the workpiece is machined by end-mill with feed rate of 600mm/min. A tool
path of reference machining case is illustrated in Figure 14. The initial temperature isassumed as 20 °C (i.e. ambient temperature), Chemical Composition shows as Table 2.

Figure 12 Dimensions of workpiece
Table 2 S45C Chemical Composition (%)

Material number C
S45C

Si

Mn

P

S

0.42～0.48 0.15～0.35 0.60～0.90 ≤0.030 ≤0.035

4.2.3.4 Boundary conditions and loading
Because this simulation is focused on finish machining, this case study ignores thechange of workpiece shape during machining. Therefore, movement of end-mill is modeled55

as a moving heat source on the top surface. Surrounding temperature and initial temperatureof workpiece is assumed 20°C. Heat transfer from workpiece to the environment isassumed in side surfaces and top surface except the end-mill position. Heat transfer atbottom surface is ignored in this case study. Reported workpiece parameters are utilized inthis case study. Details of the parameters are described in Table 3. The entire model ismeshed using Solid 70, which has eight nodes with a single degree of freedom.
In this paper, the influence of thermal fuels is neglected, and the physical propertiesof the tool material are not changed as a function of temperature. It should be noted thatthe establishment of the temperature model of the physical properties of the material model,
the nonlinear control equation will make the calculation is extremely complex, see Figure
13. Jen [121] et al. proposed and established a nonlinear solution of the physical propertiesof the material with the tool’s temperature field. Their results show that for the cemented
carbide tool, the physical properties of the tool material are not considered to be simplified
with temperature cause a large error. The properties of the cemented carbide tool material
used in this paper are less affected by the temperature, so the simplified assumptions used
for the nature of the tool material are acceptable. Based on the application of multiple linear
regression analysis together with assumptions of moving heat source theory[122],
workpiece models was defined by using finite element meshes containing linear tetrahedral
elements, readily available in the software.

56

Figure 13 Cutting tool boundary condition diagram

Table 3 S45C Physics parameters

Workpiece material

Carbon steel S45C (JIS)

Density

7.85×10-6 kg/m3

Specific heat

480 J/(kg•K )Heat conductivity

48 W/(m•K)

Surrounding temperature

20 °C

Initial temperature

20 °C

Top surface of the workpiece is machined by end-mill with feed rate of 600mm/min. A
tool path of reference machining case is illustrated in Figure 14.

57

Figure 14 Tool paths of full machining

4.2.3.5 Add heat source
In machining, grinding, cutting and sliding of surfaces, the energy generated as friction
heating and can be modelled as moving heat source, discussed by many researchers[123].the welding process, we consumed the heat source moving one element to anotherelement until to the end. The computer iteratively calculates the vector temperature valueof each node according to the divided cell grids, Figure 15.

Figure 15 Cell grids

58

4.2.3.6 Solve and Post-process
ANSYS reprocessing module includes two parts: general post processor POST1 andtime course post processor POST26. The general-purpose postprocessor is used to view theresults of the analysis of the temperature field at a certain moment or the average of theparameters over time, and the postprocessor of the time history can view the results at
different times.
(1) Extraction of data results: ANSYS saves each calculation result in the file with
suffix rmg, which the user can extract for later analysis and modification. The analysis
mainly refers to the temperature distribution of the temperature change curve according toseveral temperature change parameters. The modification of the APDL language is basedon the experimental measurement values，through the program to save the above data into
a text file in order to facilitate subsequent data visualization call data in EXCEL.
(2) Color cloud graph display: The cloud graph display can generate continuous
contour lines and discontinuous contour lines along element boundaries for node results
and element results. According to the needs of the analysis, you can also select a part ofthe model area to display the cloud image, which shows the value distribution of therelevant parameters in space. Figure 16 shows the cloud image of the temperature fieldon the top surface of workpiece, which also includes the type of solution (vector, node
solution, etc.), excitation frequency, parameter type (H, HSUM), cell grid number, themaximum and minimum values of the parameters sought in the selected area, Color andnumerical calibration scale. It also shows that the specific date saved in the cloud isavailable for log archiving.
Calculation of the temperature field is through the initial boundary conditions imposed,
load conditions, etc., by the computer iteratively iterative calculation based on the cell grid,59get the solution of the vector temperature value of each node

Time=0.4s

Time=30s

Time=60s

Time=100s

Figure 16 Temperature distribution of workpiece

4.2.3.7 Data processing and graphical visualization
In the temperature field analysis, through the APDL cycle program, the relative
position of the heat source and the workpiece is changed by a small variable, and the dataof the workpiece temperature is saved and stored in the text file at each position. Thenchange the material properties of the workpiece parameters, and then according to theabove steps for analysis and data records. Finite element simulation takes a parameter as avariable, fixed other parameters to calculate.

60Through the simulation of computer software, we can obtain the curve of temperaturechanging with time. Figure 17 illustrates temperature changes of all points include insurface I at the full machining case. From the graph, it can be seen that to complete theentire surface of cutting within 100 s, including a total of four peaks, and the highest
temperature is around 65°C at 62 s. Temperatures in surface I are affected by the distancefrom the heat source. Points’ temperatures are increasing when the end-mill is near surface
I; on the other hand, the temperature is decreasing with the end-mill away. This trend isconsistent to our natural understanding of the actual end-milling process. We can represent
temperature’s changing of points in surface I by selecting the highest temperature for eachpoint.

Full I 1
Full I 2
Full I 3
Full I 4
Full I 5
Full I 6
Full I 7
Full I 8
Full I 9
Full I 10
Full I 11

70

Temperature (oC)

60

50

40

30

20
0

20

40

60

80

100

Time(s)Figure 17 Temperature change of full machining

4. 3 Summary
1. The process includes five parts, modeling and meshing, boundary and conditions andloading, add heat source, solve and post-process and then data processing and graphical61

visualization.
2. Within the allowable error, finite element analysis using ANSYS can further detect
the temperature distribution.
3. There are also some shortcomings，the simulated induction heating process requires
reliable and complete thermal properties of the material properties data.

62

5 Thermal estimation
The effects of tool wear, cutting forces and process temperature are superposed and leadto displacement between cutting edge and surface of the workpiece, thus resulting in formand dimension errors in final products[124]. To eliminate the costs associated withadditional finishing processes in conventional machining, soft computing and othermathematical approaches used in CBN turning are presented[125].
As mentioned in the introduction, there have been many researches which investigate
temperature distribution of workpiece. End-milling process is a thermo-mechanicalcoupling process in principle, nevertheless influence of elastic deformation are enoughsmall in finish machining. Therefore, workpiece thermal analysis can be calculated without
considering thermal-stress just similar to laser heating. As a numerical simulation method,most of the researches are based on the heat conduction analysis with the FEM method. To
simulate the end-milling process from the thermal aspect, the process can be modeled asheat conduction problem with heat transfer surfaces and a moving heat source. To solve
the problem, thermal properties such as amount of heat flux, heat transfer coefficients
between the workpiece surface and environment and so on must be determined in advance.

5.1 Principle to select the nodes
Concerning the measurable points, the measurable areas of workpiece surface are
selected according to the following principles.
Firstly, the hold areas cannot be selected owing to the interference with fixtures which
fix the workpiece on machine tools.
Secondly, areas directly touched by coolant cannot be selected, because coolant plays
a significant influence of temperature measurement.
63Thirdly, working areas of end-milling are not allowed to be selected.
Finally, the temperatures of positioning reference are not preferable to measure, areasof that cannot be selected. The measurable areas are selected by above principles, and it
can be simplified that selected nodes can represent surface temperatures. In order to selectthe nodes, it must be considered the balance, convenience of measurement, sensor
availability. The data obtained from the quantification method are compared with eachother, the effectiveness of the points is evaluated based on the quantified value of eachpoint.

5.2 Evaluation of sensitivity against process variation
Sensitivity analysis is a primary approach used in mathematical modeling to identify
important factors that control the response temperature in a model. As we know that there
is a variety of sensitivity depends on locations of sensors, some node’s temperature changes
sensitively by variation of machining process, while there are stable nodes against thevariation of machining process.
In order to evaluate the sensitivity to the machining process variation, it is necessary
to compare several simulation trials with different thermal parameters such as the heat flux
and heat transfer coefficients. Variations of the parameters are summarized in Table 4. In
this study, a data set B corresponds to a nominal machining situation. A heat source isinfluenced by the cutting tool wear conditions and local workpiece material property. A
heat transfer coefficient is also changeable depend on the air flow, surface conditions andunexpected chip accumulation. Temperature difference between the certain simulation caseand the B case can indicate sensitivity to the process variation.
Theoretical basis of thermal expansion mentioned in chapter 2, in-process thermal
estimation is employed in order to achieve two functions. One is evaluation of thermal
64expansion; the other is monitoring of machining process. Thermal expansion can beevaluated based on thermal temperature analysis of nominal machining situation.
Deviations of machining process such as excessive tool wear, undesirable chipaccumulation and so on can be detected based on inverse analysis of heat conduction
analysis[110, 113]. Based on these functional viewpoints, effectiveness of measuring
points should be considered both of thermal expansion and detection of process deviations.
It is well-known that thermal expansion of workpiece is proportional to temperature risewhen other thermal properties are same. Therefore, comparison of temperature rise
between the measurable points can be an index. Concerning deviation of machining process,it is assumed that temperature of effective points varies large according to the change ofmachining process.
Table 4 Analysis cases for temperature analysis

Case

A

B

C

D

E

F

G

Heat flux (×1×105 W)

0.5 1.0 1.5 0.5 1.5 1.0 1.0

Heat-transfer coef. (×20 W/m2•K) 0.5 1.0 1.5 1.0 1.0 0.5 1.5

5.3 Case of different machining situation (half cutting paths)
In order to inspect the ability of proposed method for selecting suitable measuring
points according to the change of machining situation, a different machining situation is
also investigated. A half machining process is evaluated to compare the result of the fullmachining process. The half machining paths for same workpiece of full machining process
are shown in Figure 18. In the machining situation, heat source only moves half of top
surface. Temperatures can be detected by thermo-couple in uncut region on the rest of top
surface. Therefore, we can evaluate more points’ temperature in top surface of workpiece.
As example cases to evaluate the effectiveness of measurable points, we select measurable
65surfaces. Measurable points are selected from the selected surface. In this research, 11
points are contained each surface. As the results, we have candidate points from surface I
to surface IV for the full machining, from surface I to surface V for the half machining.
They are illustrated in Figure 19 and Figure 20.

Figure 18 Tool paths in half machining

Figure 19 Selected measurable surface in full machining

66

Figure 20 Additional measurable surface in half machining

5.4 Simulation results for temperature
After Figure 17 illustrates temperature changes of all points include on surface I. thedifferences of maximum temperature of each point are calculated, we use node 1 on surface
I as example, shows in Figure 21.
The bar chart of Figure 22 illustrates the evolution of the resulting of point 1’s
temperature variance according the parameter set A, C, D, E, F and G in surface I.
Maximum temperature difference between results of each parameter set from the result of
the set B; they are |TB-TA|, |TB-TC|, |TB-TD|, |TB-TE|, |TB-TF| and |TB-TG|. By using the
maximum temperature with nominal parameter set and the maximum temperature
difference among the parameter sets for measurable points, Calculate the EoP values ofeach point seen in the Figure 10, which illustrates most effective points in surface I is point
4.

67

Figure 21 Node 1 on surface I of full machining

Figure 22 Variance of point 1 in 6 sets’ parameters

After simulation and calculation, we known that time has distribution on each surface,
we keep eyes on 11 nodes with every surface, and calculate the variance between nominal
set to other sets, shows below, and discussed by five small parts.
1.) Surface 1

68Figure 23 Nodes on surface 1

Temperature changes and nodes’ temperature variances from surface I are shown in
Figure 24. Figure 25 is the variance value between parameter B to parameter A, C, D, E, F andG.

70
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60

50

40

30

20
0

20

40

60

80

100

Time(s)Figure 24 Temperature change of full machining on surface I

As shown in Figure 24, there are four obvious peak temperatures corresponding to its
time ranges for each node. To a certain node, the value of peak temperature increases with69

the prolonging of time, because in a same cutting rate, the cutting times increase with the
cutting time, and the cutting times determines the generated heat for each node. Node 1, 2
and 4 get their maximum value at the time range of 80~100s, node 3, 5, 6 and 7 get theirmaximum value at the time range of 60~80s, node 8, 9 and 10 get their maximum value at
40~60s, but node 11 get its maximum value at 20~40s, and the 11 nodes have their own
seasonal change due to the reciprocating movement of the tool, when the tool gets close to
the nodes side, the surface temperature will be raised, while the tool is far from the nodesside, the surface temperature will be reduced.

7
6

Variance(oC)

5
4

B-A
B-C
B-D
B-E
B-F
B-G

3
2
1
0

0

2

4

6

8

10

12

Node' number
Figure 25 Variance of full machining on surface I

As seen from Figure 25, the variance value of B-A, B-C, B-D and B-E for each node
has slight fluctuation, while their values are kept in the range of 5~6. However, the variancevalue of B-F and B-G for each node kept almost the same, while their value are kept
approximately 0, except for node 1, its value is about 0.5.

70Figure 26 is the temperature change of half machining on surface I, and Figure 27 is thevariance of half machining on surface I.

70
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60

50

40

30

20
0

10

20

30

40

50

Time(s)Figure 26 Temperature change of half machining on surface I

As seen from Figure 26, in the time range of 0~50s, node 1, 2, 3 and 4 do not get theirpeak temperature, the temperature value of each node increase with the prolonging ofmachining time. Node 5, 6, and 7 has one obvious temperature peak at the machining time
range of 40~50s, respectively. While node 8, 9, 10 and 11 has two obvious temperaturepeaks at the machining time range of 20~30s and 40~50s, and node 8 and 9 get its
maximum value at 40~50s, but node 10 and 11 get its maximum value at 20~30s. Overall,node 8 has the maximum temperature value of 65°C in the time range of 40~50s.

71

7
6

B-A
B-C
B-D
B-E
B-F
B-G

Variance (oC)

5
4
3
2
1
0
0

2

4

6

8

10

12

Node's number
Figure 27 Variance of half machining on surface I

Figure 27, the variance value of B-A, B-C, B-D, and B-E for each node is undulate,and reach their maximum at node 8, respectively. However, the variance value of B-F and
B-G stays almost the same (approximately 0) for each node, except node 6 which value
reaches 0.5.
2.) Surface 272

Figure 28 Nodes on surface 2

Temperature changes and nodes’ temperature variances from surface II are shown
from Figure 29 to Figure 30. We can notice that temperatures do not have too huge swings
either full machining or half machining on surface II.

7065

TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60
55
50
45
40
35
30
25
20
0

20

40

60

80

100

Time(s)Figure 29 Temperature change of full machining on surface II

As seen in Figure 29, the temperature of each node has similar variation tendency withthe extend of time, they have two obvious temperature peaks in the time range of 0~30s,73and the first temperature peak are their maximum of approximately 50°C. The detected
nodes on surface II have similarity that the distance between the node with the tool in X
direction is equal, hence the difference of their temperature change is just depended on the
time.

7
6

Variance(oC)

5
4
B-A
B-C
B-D
B-E
B-F
B-G

3
2
1
0
0

2

4

6

8

10

12

Node' number
Figure 30 Variance of full machining on surface II

It can be seen from Figure 30, the variance of B-A, B-C, B-D and B-E has similar
trend to each node, their variance value stays at 5~6. While the variance of B-F and B-G
have similar trends, and get value of approximately 0, except node 2 to B-F.

74

70
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60

50

40

30

20
0

10

20

30

40

50

Time(s)Figure 31 Temperature change of half machining on surface II

As seen from Figure 31, the temperature change of half machining is just as the first
50s of full machining on surface II, their variations are the same as the first 50s of fullmachining process.
7
6

Variance (oC)

5
B-A
B-C
B-D
B-E
B-F
B-G

4
3
2
1
0
0

2

4

6

8

10

Node's number
Figure 32 Variance of half machining on surface II

75

12As seen from Figure 32 the variance values of all parameters have similar trend,almost stays the same, while B-A, B-C, B-D and B-E have the variance values ofapproximately 6, but B-F and B-G have the variance values of approximately 0.
3.) Surface 3

Figure 33 Nodes on surface 3

Figure 33 shows the schematic diagram of surface 3, the temperature change of fullmachining on surface III and the variance of parameters are shown in Figure 34 and Figure
35, respectively.

76

70
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60

50

40

30

20
0

20

40

60

80

100

Time(s)Figure 34 Temperature change of full machining on surface III

As shown in Figure 35, the variances of full machining on surface III for B-A, B-C,
B-D and B-E are fluctuating as the different nodes in the value range of 5.5~6.5. However,the variances of B-F and B-G change a little almost staying at approximately 0.

7
6

Variance(oC)

5
4
B-A
B-C
B-D
B-E
B-F
B-G

3
2
1
0
0

2

4

6

8

10

Node' number
Figure 35 Variance of full machining on surface III

77

12

Figure 36 and Figure 37 are the temperature change of half machining and the varianceof half machining on surface III, respectively.
70
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60

50

40

30

20
0

10

20

30

40

50

Time(s)Figure 36 Temperature change of half machining on surface III

As shown in Figure 36, in the time range of 0~50s, node 1 to node 4 have no obvious
temperature peak, and node 5 to node 8 have one obvious temperature in the time rang of
40~50s, while node 9 to node 11 have two obvious temperature peaks in the time rang of
20~30s and 40~50s. Overall, node 4 reaches its maximum temperature of 65°C in time
rang of 40~50s.

78

7
6

Variance (oC)

5
B-A
B-C
B-D
B-E
B-F
B-G

4
3
2
1
0
0

2

4

6

8

10

12

Node's number
Figure 37 Variance of half machining on surface III

As shown in Figure 37, the variance value of B-A, B-C, B-D and B-E decrease with
the increase of node’s number, from 6.7 of node 1 to 0.4 of node 11. However, the variancevalue of B-F and B-G stay almost the same at 0, except node 1. Overall, parameter A, C,
D and E have significant effect on the variance value, but parameter F and G have weakeffect on the variance value.
4.) Surface 4
Figure 38 is the schematic diagram of surface 4. Figure 39 and Figure 40 are thetemperature change of half machining and the variance of half machining on surface 4,
respectively.

79

Figure 38 Nodes on surface 470
TEMP-1
TEMP-2
TEMP-3
TEMP-4
TEMP-5
TEMP-6
TEMP-7
TEMP-8
TEMP-9TEMP-10
TEMP-11

Temperature(oC)

60

50

40

30

20
0

20

40

60

80

100

Time(s)Figure 39 Temperature change of full machining on surface IV

As shown in Figure 39, there are two obvious temperature peaks on the curves foreach node in the time range of 0~100s. In the first 40s, the temperature of the detected 11
nodes changes a little and their values are lower than 20°C as is the small amount of heatgenerated in the early cutting stage. Prolonging the cutting time, when the cutting timeexceed 80s, the surface temperature increase rapidly, and then reaches their corresponding80

maximum temperature in the time range of 90~100s. Furthermore, node 1 has the highest
temperature of 67°C because of the shortest distance between node 1 with the initial cutting
position.
7
6

Variance(oC)

5
4
B-A
B-C
B-D
B-E
B-F
B-G

3
2
1
0
0

2

4

6

8

10

12

Node' number
Figure 40 Variance of full machining on surface IV

As shown in Figure 40, the parameter effect of B-A, B-C, B-D and B-E have similar
trend for each node, and their values are stay in the range of 5.5~6.0, except for node 11
which has a largest variance of 6.0. However, parameters of B-F and B-G have similar
trend and stays almost the same at variance value of approximately 0.

81

21.0
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

20.8

20.6

20.4

20.2

20.0
0

10

20

30

40

50

Time(s)Figure 41 Temperature change of half machining on surface IV

As seen in Figure 41, in the first 30s, the surface temperature is almost the same ofroom temperature. After that, the increasing rate of surface temperature gets rapidly in themachining time range of 30~50s.

82

7
6

Variance (oC)

5
4

B-A
B-C
B-D
B-E
B-F
B-G

3
2
1
0
0

2

4

6

8

10

12

Node's number
Figure 42 Variance of half machining on surface IV

As shown in Figure 42, the parameters effect of B-A, B-C, B-D and B-E have similar
trend and their values increase with the increase number of detected node, while theirvalues are stays in the range of 6.0~7.5. However, the parameters effect of B-F and B-G
also have similar trend at the variance value range of approximately 0, except node 11 for
B-F.
5.) Surface 5
Figure 43 is the schematic diagram of 11 nodes on surface 5. Figure 44 and Figure 45
are the temperature change of half machining and the variance of half machining on surface
4, respectively.

83

Figure 43 Nodes on surface 5

70
TEMP_1
TEMP_2
TEMP_3
TEMP_4
TEMP_5
TEMP_6
TEMP_7
TEMP_8
TEMP_9
TEMP_10
TEMP_11

Temperature(oC)

60

50

40

30

20
0

10

20

30

40

50

Time(s)Figure 44 Temperature change of half machining on surface V

As shown in Figure 44, all nodes have similar trend with the prolonging of machining
time. They have just one obvious peak temperature in the machining time range of 0~50s.
And in the first 20s, their surface temperature almost stays the same, but after 30s, theincreasing rate of their surface temperature get rapidly, and reaches their corresponding
maximum in the machining temperature range of 40~50s. Furthermore, nodes one has the84largest temperature of 44°C due to the shortest distance between the node’s position andthe initial machining position.

7
6

B-A
B-C
B-D
B-E
B-F
B-G

Variance(oC)

5
4
3
2
1
0
0

2

4

6

8

10

12

Node's number
Figure 45 Variance of half machining on surface V

As seen in Figure 45, parameters B-A and B-E, B-F and B-G have same trend for 11
nodes, respectively. However, parameters B-A and B-D have different effect for 11 nodes.
And the variance value of B-A is in the range of 2.5~3.2, B-D is in the range of 1.8~2.2,
B-C and B-E is in the range of 1.3~1.7, B-F and B-G is in the range of 0~0.3.

5.5 Effectiveness of measurable points
5.5.1 Evaluation method for measuring points
In-process thermal estimation is employed in order to achieve two functions. One isan evaluation of thermal expansion; the other is a sensitive monitoring of machining
process. Thermal expansion can be evaluated based on thermal temperature analysis of
nominal machining situation. Deviations of machining process such as excessive tool wear,
undesirable chip accumulation and so on can be detected based on inverse analysis of heat85

conduction analysis [18-20]. In the proposed process monitoring method [18], processparameters are adjust based on the measured temperatures. Based on these functional
viewpoints, effectiveness of measuring points should be considered both of thermal
expansion and detection of process deviations. It is well known that thermal expansion of
workpiece is proportional to temperature rise when other thermal properties are same.
Therefore, comparison of temperature rise between the measurable points can be anevaluation index. Concerning deviation of machining process, it is assumed thattemperature of effective points varies large when the machining process change. Figure
46 shows relations between two functions and effectiveness of the measurable point.

Figure 46 Constitute of EoP process

5.5.2 EoP formula
In this research, increases of heat flux caused by excessive tool wear and changes ofheat transfer coefficients by chip accumulation are considered as dominant process
deviation phenomena. Therefore, temperature sensitivity to the process parameters such as
86amount of heat flux and heat transfer coefficients is introduced as a second index. Therefore,
effectiveness of measurable points (EoP) is formalized as follows.

EoPi 

Ti ,nmax (T j ,n )j{1, N }Timax (T j )5-1

j{1, N }

Ti  max (Ti ,k  Ti ,n )k{1,M }5-2

In Eq. 5-1, where i represents nodal number of measurable point (maximum number is N),
k represents number of a thermal properties set (maximum number is M). Ti,n indicatesmaximum temperature rise of i the node with nominal thermal properties set; Eq. 5-2 shows
Ti indicates variance of maximum temperature rise against the change of processparameters.
5.5.3 Result of one measurable point evaluation
Based on the results of simulation and proposed formulation, effectiveness ofmeasurable points (EoP) for candidate nodes is calculated. Results of calculation for thefull machining and the half machining are listed in Table 3 and Table 4 respectively.
This variance is a measure of how large the temperatures are affected from the processchange. It can be an index of the effectiveness of measurable point.

87)
Difference value of temperature (¡ã C

80
70
60
50
40
30
20
10
0
|TB-TA||TB-TC||TB-TD||TB-TE||TB-TF||TB-TG|

Parameters' sets

Figure 47 Temperature difference of point 1 on surface I in full machining

Figure 46 illustrates the change of temperature in machining process, and Figure
47 shows the numerator of second part of the EoP formula. In the first part, we can evaluate
the effectiveness of each point based on the temperature rise. In the second part, we canevaluate the sensitivity against the process change based on the difference of temperaturerise. For example, variance of point 1 in 6 sets show sensitivity against the change of thesimulation parameters. The combination of the Figure 47 and Figure 48 gives enoughinformation to calculate EoP formula. Finally, we can calculate the EoP values of eachpoint seen in the Figure 49, which illustrates most effective points in surface I is point 4.

88

1.80
1.75

Value of EoP

1.70
1.65
1.60
1.55
1.50
1.45
0

1

2

3

4

5

6

7

8

9

10

11

Node' s number
Figure 48 EoP of full machining on surface I

By the same way, we can list other surfaces’ effectiveness of measurable points. By
using the method mentioned above, other sensitive points can be selected in two machiningcases.
5.5.4 Results of measurable points evaluation
Based on the results of simulation and proposed formulation, effectiveness ofmeasurable points (EoP) for candidate points are calculated. Results of calculation for thefull machining and the half machining are listed in Figure 49 and Figure 50 respectively.
We can know where the temperature changes hugely and fluctuates with parameters
by using the EoP values. It is a quantification method of effectiveness. Figure 49
illustrates the score of point 1 of surface IV from full machining have the largest score(1.80) in all surfaces of full machining process. The score of point 2 to point 5 of surface
IV scored 1.79 -1.76. At the same time, point 6 and point 10 of surface III and point 4 ofsurface I scored 1.76, 1.77 and 1.77, respectively.
89

2.0
1.8
1.6

ch1
ch2
ch3
ch4
ch5
ch6
ch7
ch8
ch9
ch10
ch11

Value of Eop

1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
surface I

surface II

surface III

surface IV--

Four surface' s number
Figure 49 EoP of temperature in half machining
2.0
1.8
1.6

Value of EoP

1.4

ch1
ch2
ch3
ch4
ch5
ch6
ch7
ch8
ch9
ch10
ch11

1.2
1.0
0.8
0.6
0.4
0.2
0.0
surface I

surface II

surface III

surface IV

surface V

Five surface' s number
Figure 50 EoP of temperature in full machining90--

As shown in the flowing Figure 50, we can see that 11 points on either surface IV
and surface V of EoP have same temperature score (1.28 and 1.40). Point 8 on surface I
and point 4 on surface III have largest score (1.87) of EoP value in half machining.
Furthermore, point 7 on surface I and point 5 on surface III have second largest score (1.79).
Using this method, we can pick the effective points from 44 points in full machining
and 55 points in half machining. In order to determine the effective measuring points, we
also consider convenience of measurement. After the EoP analysis of two machining cases,
selected measuring points based on the effectiveness of measurable points are shown in
Figure 51 and Figure 52 respectively.
In the full machining case, point 4 on surface I, point 6 and 10 on surface III, andpoints from 1 to 5 on surface IV are selected. On the other hand, point 7, point 8, point 9
and point 11 on surface I, point 1 and point 3 to point 5 on surface III in half machining
process are selected as effective points. Considering the possibility of measurement about
workpiece by thermocouple in production process, we selected these surfaces and chose 8
points based on the value of EoP evaluation.
Table 5 EoP of temperature for full machining

Node

1

2

3

4

5

6

7

8

9

10

11

Full I
Full II

0.84 0.89 0.86 0.86 0.90 0.95 0.91 0.90 0.94 1.00 0.89
1.02 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 0.62

Full III
Full IV

1.15 1.27 1.21 1.19 1.30 1.44 1.34 1.30 1.42 1.57 1.37
1.03 1.03 1.03 1.03 1.02 1.03 1.03 1.03 1.03 1.03 0.99

We know where the temperature change hugely is the amounts of EoP largely by
quantification method. The score of node 10 of surface III from full machining have thelargest score in all surfaces of case of full machining process. We can see that node 1 tonode 11 from half surface II of EoP have same largest temperature score (1.07) as well.
Using this method, we can pick all the sensitivity nodes from 99 nodes.
91

Table 6 EoP of temperature for half machining

Node

1

2

3

4

5

6

7

8

9

10

11

Half I
Half II
Half III
Half IV
Half IV

0.82
1.07
0.96
0.80
0.73

0.83
1.07
0.98
0.80
0.65

0.85
1.07
0.91
0.81
0.65

0.88
1.07
0.85
0.81
0.64

0.73
1.07
0.76
0.81
0.69

0.89
1.07
0.84
0.73
0.63

0.82
1.07
0.89
0.81
0.63

0.84
1.07
0.89
0.81
0.63

0.90
1.07
0.86
0.81
0.63

0.97
1.07
0.84
0.81
0.73

0.94
1.07
0.82
0.82
0.66

Let us assume the case of available number of thermocouples are four. We select theeffective measuring points from the evaluation of measurable points in full machining andhalf machining and convenience of measurement. Based on the principle described in
section 2, four effective points are selected for both cases. They are illustrated in Figure
51and Figure 52 respectively. In the full machining situation, points III6, III9, III10 and
III11 are selected. On the other hand, points form II1, II4, II8 and II11 are selected in half
walking process. Although we got same score in half surface II, we only chose four nodes
within same scores of EoP score, because we consider balance measurement about
workpiece by sensors in production process, and also it is much convenient to measure byusing the separate measurements.

Figure 51 Effective nodes on the full machining

92

Figure 52 Effective nodes on the half machining

5.6 Summary
1. By using FEM-based thermal simulation, analysis of temperature distribution andsensitivity analysis are carried out.
2. Based on the results of calculation, measurable points for different machining
situations are evaluated.
3. As the results, different measuring points are selected according to the change ofmachining situation. Furthermore, evaluations of points including sensitivity analysis canbe obtained stable results at a reasonable computational cost.

93

6 Applications
In order to evaluate the heat flux transferred into the workpiece, some theoreticalmodels based on FEM analysis have been investigated in the literature. For more accurate
parameters by orthogonal test, we present a method to evaluate an effectiveness of eachmeasuring points from aspects of stability of measurement and sensitivity to the process
variation, based on these analysis effectiveness of measurable points (EoP) can becalculated by using thermal simulation and variation modelling.
Every machining thermal analysis is based on previous calculation or determinationof temperature field during machining process, which constitutes the thermal distribution
load to be applied to the overall workpiece.

Figure 53 Detailed information flow94

6.1 Simulation models
As described in chapter 4, numerical simulation of machining process has begun to be
successfully simulated by FEM codes, the results of simulations can be used to explain thephysical essence of some complex phenomena in various machining process, we mentionedthat simulation can predict thermal distribution when cutting process exchanged, see
Figure 54, by simulation software we mentioned in chapter, see the temperature
distribution in Figure 55.

Figure 54 Parameters in end milling process

95

Time=0s

Time=400s

Time=800s

Time=1200s

Figure 55 Temperature distribution of workpiece

6.2 EoP application models
We consider same thermal couple as before models. There are 10 thermocouples
named ch1 to ch10 shown in Figure 56, and we talk the variance by two thermocouples,name ch6 and ch8 shown in Figure 57.

96

Figure 56 workpiece shape and thermocouples

Figure 57 Parameters in end milling process

6.3 Orthogonal tests
From chapter 5, the results show the two different paths go through the surface ofworkpiece and have a certification to EoP, but the thermal parameters are changing varies.
Orthogonal test method can reduce test times, shorten test cycles and find multifactors optimization scheme quickly. As we all known that heat flux, heat coefficient andthermal conductivity make different contributions to thermal environment in machining
process, and from orthogonal tests, we can make it clearly that which factor make97

sensitivity effects to temperature changes. Task significant illustrated in Figure 58andorthogonal experimental design shown as Table 7.

Figure 58 Orthogonal tests

Table 7 Orthogonal parameters

Set

A-Heat conductivity(W/(m•K))

B-Heat coefficient(W/m2•K)

C-Heat flux
(w/m2)

1

15

194

24

2
3

22
26

278
361

35
45

In mathematics, in the area of combinatorial designs, an orthogonal array is a "table"(array) whose entries come from a fixed finite set of symbols, A variation simulation basedon orthogonal array has three factors, they are thermal conductivity (HC), heat transfer
coefficient (HTC) and heat flux (HF), and we named them as A, B and C, respectively.
From the orthogonal arrays, we listed 10 sets by Table 8; variation simulations which are98

a set of various simulations is carried out and compared with experimental results.
Calculated results as a set can be considered to represent a simulated result with uncertainty.
The fifth set of data (Set 5) A=22w/(m•k), B=278w/m2•k and C=35w/m2 as the nominal
set, which is the middle set in the orthogonal arrays.
Table 8 Orthogonal arrays

Set

A

B

C

1
2
3
4
5
6
7
8
9
10

1
1
1
2
2
2
2
3
3
3

1
2
3
1
2
2
3
1
2
3

3
2
1
2
2
1
3
1
3
2

In this article, we consider 10 points of workpiece evaluated by thermocouples [10],as shown in Figure 56. By 9 sets from orthogonal arrays, we distinguished 9 sets of 10
points’ temperature change with heat moving process in simulation tests and actual
experiments. Use ch6 as an example, which we can see from Figure 57 that simulation
test and actual test looks much similar by the date of run 3: A1B3C1.
Orthogonal test method can reduce test times, shorten test cycles and find multifactors optimization scheme quickly. As we all known that heat flux heat coefficient andconductivity make different contribution to thermal environment by machining process,and from orthogonal test, we can understand which factor make sensitivity effects totemperature change.
1) Average temperature to test results.
We can see that value R is A>C>B, which means heat flux make biggest contribution toaverage temperature changing.

99

Table 9 Range analysis of experimental results

A

B

C

variance

1
2
3
4
5
6

1
1
1
2
2
2

1
2
3
1
2
3

3
2
1
2
1
3

1.211
1.523
1.860
1.788
2.246
1.794

7
8
9
K1
K2
K3
k1
k2
k3

3
3
3
4.594
5.827
7.209
1.531
1.942
2.403

1
2
3
5.601
5.836
6.193
1.867
1.945
2.064

1
3
2
6.709
5.850
5.072
2.236
1.950
1.691

2.603
2.067
2.539

R

0.872

0.197

0.546

Temperature (°C)

20

Simulation
Actual Experiment

18

16

14

12

10
0

200

600

400

800

1000

Time (s)Figure 59 Evaluation the accuracy process of ch6

100

1200

2) Area difference to test results.
Temperature distribution from actual test and FEM simulation test, we can define theArea difference value under orthogonal tests, Figure 60, Which A (Heat flux), B (Heatcoefficient) and C (Heat conductivity) in the form, k1, k2, k3, R. We can see that value R
is C<B<A, which means thermal conductivity make biggest influence on D-value of area.
The deviation of analytical solutions as compared to the nearly constant FEM
simulation results could be caused by the thermal conductivity which subsequently
influences the difference area value. Simulation compares experiment，Mr. Tanaka did
experiment in (22-278-35)
Table 10 Range analysis of experimental results

Case number

A

B

C

ABS area

1

A1B1C3

1

1

3

1859.6

2
3
4
5
6
7
8
9
K1
K2

A1B2C2
A1B3C1A2B1C2A2B2C1A2B3C3A3B1C1A2B2C3A3B3C2

1
1
2
2
2
3
3
3
4188.66674.1

2
3
1
2
3
1
2
3
8043.26629.8

2
1
2
1
3
1
3
2
6573.86637.91387.3941.6
2690.82139.41843.93492.83103.12559.89155.71396.22224.73051.91655.75345.32681.12209.91781.8899.3

6806.62191.32212.62268.9
77.6

K3
k1
k2
k3
R

101

Temperature (°C)

20

TEMP_6
actu

18

16

14

12

10
0

200

400

600

800

1000

1200

Time (s)Figure 60. Area difference

From these two orthogonal tests, we can define the value of parameters in thissimulation that help us to do most similar simulation with experiment. Temperature
distribution changing with thermal parameters by FEM, and then we do patterns ofmeasurement points, which shows evaluation points are changed to the predicated points,
the influence of measurement points will be different. In this article, we mentioned threecases of machining process by different paths.

6.4 Square model 1
Following two figures estimated temperatures and actual temperatures at two points,
Figure 62 and Figure 63 illustrate comparisons of measured temperature and thevariational simulations at point 6 and 8 in Figure 57. From the Figure 62, we can see thetwo points have temperature fluctuation in machining process in 1200s, and the nominal
set curve (Set 5) is in the middle of 10 simulation lines, 6 raise temperatures to peaks
quicker than 8, because they are in different location of workpiece, and heat source walking
102through the surface as shown in Figure 54. As shown in the Figure 64, measured results
can be contained within the range of the variational simulations. This indicates if we canadjust an appropriate parameter set, actual machining situation can be represented
accurately.

Figure 61 One-way parallel paths

103

35

Experiment
Set1
Set2
Set3
Set4
Set5
Set6
Set7
Set8
Set9
Set10

Temperature (°C)

30

25

20

15

10
0

200

400

600

800

1000

1200

1000

1200

Time(s)Figure 62 Temperature at point 6 in Fig.2

Experiment
Set1
Set2
Set3
Set4
Set5
Set6
Set7
Set8
Set9
Set10

Temperature (°C)

35

30

25

20

15

10
0

200

600

400

800

Time(s)Figure 63 Temperature at point 8 in Fig.2

104

Use ch1 in one way moving path as an example, we calculate temperatures under
different experimental parameters of heat flux, heat conductivity and heat transfer
coefficient shown in Table 7 Then, the differences of maximum temperature of eachpoint are calculated. As we mentioned before that Ti indicates variance of
maximum temperature rise against the change of process parameters. In Figure 64 (b)shows the values of TTn temperature difference of point 1, |T3-T5| represents
between the No.3 parameter set and nominal parameter set (No.5 set). In this case, |T3T5| gets the highest variance (0.647℃) in one way moving path machining. Therefore,
this value becomes T1. At the same time, T2toT10 corresponding to ch1 to ch10 are
also calculated based on the 9 values of |T1-T5|, |T2-T5|, |T3-T5|, |T4-T5|,|T6-T5|, |T7-T5|, |T8-T5|, |T9-T5| and |T10-T5| for each point.

(a) One-way parallel paths in 1/4 time

105

2.00
1.80

Eop values

1.60
1.40
1.20
1.00
0.80
0.60

0.40
0.20
0.00
Ch1

Ch2

Ch3

Ch4

Ch5

Ch6

Node's number

Ch7

Ch8

Ch9

Ch10

(b) EoP of one-way parallel paths
Figure 64 One-way parallel paths and EoP value

As shown in Figure 64, Ch3 is the most sensitive point by one-way movement paths.
From this EoP formula, we can easily understand that some points show large variancenumber as sensitive parameters’ change or/and thermal expansion.
To evaluate the feasibility of the EoP value, results of inverse analysis which are useddifferent measuring points are investigated. In the reported paper [18], a relation between
the measuring points and estimation error has been reported. In the research, we had a clear
identification that Ch3 is the good point but not the Ch1, which illustrated in this last sectionof chapter 6, see from figure 64.

106Figure 65 Sensitive point in one-way parallel path

6.5 Square model 2
From round-way parallel paths walking of Figure 66 and Figure 67 in a quartertime show the different case study, and we still use the same method the results from EoP
value shows in Figure 68, we noted that EoP value of Ch3 is equal 2 ,it is still the mostsensitive point not only in one-way parallel paths but also in round-way parallel paths.
Then, Ch6 has larger value of EoP than other points in round-way parallel paths. We candraw Figure 69 with the sensitive point in round-way parallel paths by upper description.

Figure 66 Round-way parallel paths

107Eop values

Figure 67 Round-way parallel paths in 1/4 time

2.00
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Ch1

Ch2

Ch3

Ch4

Ch5

Ch6

Ch7

Ch8

Node's number
Figure 68 EoP of round-way parallel paths

108

Ch9

Ch10

Figure 69 Sensitive point in round-way parallel paths

6.6 Square model 3
As the results shown in one-way and round-way parallel paths, the most sensitivity
points found with same points, and then we proposed following walking paths in Figure
70. It is spiral moving paths from middle to side of workpiece; the dotted line means
connection between two spiral paths. From the following walking paths showed in Figure
71. It is spiral moving paths from middle to side of workpiece. The dotted line shows theboundary between two spiral paths.

109

Figure 70 Spiral way Paths

Figure 71 Spiral paths in 1/4 time

110

2.00
1.80

Eop values

1.60
1.40

1.20
1.00
0.80
0.60
0.40
0.20
0.00
Ch1

Ch2

Ch3

Ch4

Ch5

Ch6

Node's number

Ch7

Ch8

Ch9

Ch10

Figure 72 Maximum EoP value of spiral paths

As shown in Figure 72, Ch4 is the most sensitive point in spiral walking paths.
Furthermore, Ch2 has a larger value than others in spiral paths. The sensitive point in spiralwalking paths in Figure 73 is painted red solid according to the EoP results.

Figure 73 Sensitive point in spiral path

6.7 Calculated EoP value
To evaluate the feasibility of the EoP value, results of inverse analysis which are useddifferent measuring points are investigated. In the reported paper[109] ,a relation between
the measuring points and estimation error has been reported. In the research, we had a clear
111

identification that Ch3 is more sensitive than Ch1 as illustrated in Figure 74 and Figure 75.
The estimated error of Ch2 and Ch7 are estimated by Ch6 and Ch3 data, which are smaller
than the error of estimated by Ch6 and Ch1 data. Calculated EoP values are consistent to
the relation between the measuring points and estimation error which was reported in theprevious research. This implies the feasibility of the EoP value.

Figure 74 Ch1 and Ch6 as estimation measuring points, Ch2 and Ch7 as evaluation measuring
pointsFigure 75 Ch3 and Ch6 as estimation measuring points, Ch2 and Ch7 as evaluation measuring
points

6.8 Summary
1. Orthogonal table like parameter set generation was introduced as a simple
representation method for process variation.
112

2. From the comparison of three EoP results of these three paths, three paths of thegraph show that the three paths do not have the same results.
3. Results of inverse analysis which used different measuring points were
investigated, this implies the feasibility of the EoP value.

113

7 Conclusion
In this thesis, a systematic approach to monitor the finish machining of NNS parts was
discussed. The results are as follows:
1. From the survey of thermal issues in machining research, it became clear the sensorconfigured simulation method which combine FE simulation and local measurement
combination method is a promising solution to the process monitoring in end-milling. .
2. Thermal process simulation for workpiece temperature calculation is formalized
and a FE based simulation system was implemented. Within the allowable error, developed
simulation can calculate the temperature distribution of workpiece.
3. Sensitive parameters in process simulation are selected and maximum variation of
those parameters are estimated. Moreover, orthogonal table like parameter set generation
was introduced as a simple representation method for process variation.
4. By using FEM-based thermal simulation and process variation model, analysis oftemperature distribution and sensitivity analysis were carried out. Based on the results ofcalculation, measurable points for different machining situations are evaluated. As theresults, different measuring points were selected according to the change of machining
situation. Furthermore, evaluations of points including sensitivity analysis could obtainstable results at a reasonable computational cost.
5. To evaluate the feasibility of the EoP value, results of inverse analysis which useddifferent measuring points were investigated. Calculated EoP values are consistent to therelation between the measuring points and estimation error which was reported.
6. The three EoP results correspond to the three different tool paths were compared.
114

The distribution of EoP values for each path showed that appropriate measuring points
were different. This implies the proposed method can quantify the appropriateness of themeasuring points.

115

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