TECHNICAL REPORT

Effective Convergence Type Problem-solving Technique for North
American Automotive Products and Manufacturing Processes
M. MCKEEL

Automotive systems and components have shown a pattern toward increasing complexity, stricter tolerances, andshorter product cycles. To meet market requirements, excellence in automotive design and manufacturing practices
must rise accordingly to ensure the reliability of such evolved mechanical systems. Product failure investigations
must be fast, efficient, and exact. The increasing complexity of mechanical systems can create an attribute failure
problem (i.e., damage) that is often harder to solve than a failure that comes from a continuous feature such as
dimensional variation. Consequently, convergent problem-solving methods for finding the source of various failure
types have been utilized effectively in North America.
Introduced in this paper will be problem solving techniques that specifically address attribute failures.
Key Words: contrast, energy, strength, Concentration Chart, destructive events, malfunctions

1. Introduction
Automotive components that fail in the application
can potentially compromise operator safety as well as
comfort. It is therefore critical that problem solvers
tasked with improving the reliability of automotive
components have quick and exact methods that canreveal and eliminate the precise source of the failures.
The techniques being used in North America that arepresented in this paper will show investigators how todiagnose multiple failure modes accurately and by orderof influence.
The first step in an efficient investigation is to choosea strategy for how the problem will be solved and thenimplement that strategy using techniques that will makethe task easier. Effective strategies begin with a precise
1)
categorization of the response under study :1) Feature (a continuous response: typically dimensions
referenced against a datum)2) Defect (an attribute response: scratches, chips, cracks,etc.)3) Event (an attribute response: field failures, Durability
test etc.)
4) Property (a continuous response: decibels, density,
tensile strength, etc.)
The second step is to choose a technique that is
appropriate for that category. If, for example, Y is aproperty or a feature, the X-Y relationship should be
evaluated by plotting Y against several potential variables(X) on either a Multi-Vari chart or a Scatterplot. TheseJTEKT ENGINEERING JOURNAL English Edition No. 1014E (2017)

graphs will expose the family of variables that harbor theroot cause.
At this point, that family is split into two or moregroups of variables, followed by another test to ascertain
which of those groups harbors the root cause. Thissequence of splits continues progressively until the2)
offending variable (X) is found.
If the failure is an event and the product is an
assembled unit, then a typical next step might be to use
3)
a technique known as Component Search . This method
will show if the assembly process caused the failure
or if it was one of the components, and if one of thecomponents, which one.
If the response is a defect such as a scratch, inclusion,crack, etc., then the starting point is to map the defects
4)
with a Concentration Chart to see if the defects areconcentrated. If they are, then either a strength problem
exists at that location or the concentration correlateswith some position in the manufacturing process (orto a position within the application). If the defects are
distributed randomly, then they were probably caused byan energy spike from the manufacturing process or theapplication itself. Concentrated defects tend to result fromthe way the part is made; randomly distributed defects
tend to result from the way the part is used.

79−Effective Convergence Type Problem-solving Technique for North American Automotive Products and Manufacturing Processes−

2. Converging Onto Important Factors:
the Attribute-to-Continuous TransformWhen defects such as cracks or scratches are found on
tested parts, it is prudent to transform those defects andevents (attribute responses) into continuous responses
early in the problem-solving investigation (Fig. 1).

AttributeＹ

Continuous
X

Fig. 1 Attribute-to-Continuous transform

Attribute-to-continuous transformations come in many
forms: Defect to Feature, Defect to Property, Event to
Feature, etc. Below is an example of an attribute-tocontinuous transform.
The raceway of a transmission thrust bearing was
spalling at the inner diameter where the edge curls
downward. The spall was categorized as a defect (anattribute response) based on actual tested parts. Since
the response is a defect, the investigation began with themapping of spall locations onto a Concentration Chart(Fig. 2).

x

x

xx

xx
x
x
x
x

xx
x
x
x
x

x
xx
x
x
x

x
x

Spall on innerdiameter

xx x

x
xx

x：Spall
Spall on innerdiameterThrust Raceway Spall

Rationale
Concentrated

Random・Concentration Chart

Fig. 3 Search tree

The fact that the defects are concentrated suggests thematerial is too weak to withstand system energy. The
reason why the material is too weak can be answeredby leveraging the contrast between spalled and nonspalled raceways. Twelve raceways were sampled, sixwith spall at the inner diameter (the WOW, or worst-ofworst location) and six with no spall at the inner diameter(BOB, or best-of-best, parts). Taper and surface finish
features were measured on the WOW parts near the spall
indications. Table 1 shows the original, unsorted data
(BOBs in green, WOWs in red). Table 2 shows eachcolumn sorted independently from low to high in a Group
5)
Comparison analysis . The data in the interaction column
is the product of the taper and surface finish values.

Table 1 Group comparison (unsorted)

Taper

Surface Finish

Taper×Surface Finish
Interaction−0.0220.136−0.0030−0.0260.111−0.0029−0.0280.119−0.0034−0.0310.115−0.0035−0.0290.114−0.0033−0.0270.112−0.0030−0.0350.133−0.0046−0.0280.158−0.0044−0.0280.15−0.0043−0.0320.141−0.0045−0.0290.145−0.0042−0.0340.132−0.0045

Original column order（BOBs in green, WOWs in red）

Fig. 2 Concentration chart of spall on a thrust bearing raceway

The progressive search pattern is shown on a searchtree (Fig. 3). The concentration chart answered the firstquestion: are the defects concentrated or distributed
randomly?

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Table 2 Group comparison (sorted)−0.022

OK

Surface Finish−0.026−0.028Marginal−0.0301=No
Spalling,
2=
Spalling
<1
1−2
>2

OK

−0.0350.111−0.0046−0.0340.112−0.0045−0.0320.114−0.0045−0.0310.115−0.0044

0.
11
5
0.
12
0
0.
12
5
0.
13
0
0.
13
5
0.
14
0
0.
14
5
0.
15
0
0.
15
5

Taper

Taper×Surface Finish
Interaction

Taper, mm−0.024−0.0290.119−0.0043

Surface Finish (Ra), µm−0.0290.132−0.0042

Fig. 4 Contour plot of taper, surface finish−0.0280.133−0.0035−0.0280.136−0.0034−0.0280.141−0.0033−0.0270.145−0.0030−0.0260.150−0.0030−0.0220.158−0.0029

End-count = 6

End-count = 9End-count = 12

90% Conﬁdence

95% Conﬁdence

99% Conﬁdence（BOBs in green, WOWs in red）

The sum of each column’s end-counts determinessignificance. Data are analyzed by ranks, not values,
which makes the technique suitable for both variables andattributes data. Sample sizes can be as small as six parts(three BOBs, three WOWs) for finding significance andconfirming effects.
Larger end-counts provide higher significance.
End-counts of seven to nine are significant with 95%
confidence; from ten to twelve: 99% confidence; andgreater than twelve: 99.9% confidence.
Based on the end-counts in Table 2, it is clear that taperand surface finishes are significant. Surface finish was thestronger of the two effects. However, the strongest effect
was the taper-by-surface finish interaction. Figure 4
shows a contour plot of the taper and surface finish
variables against the response (no spall = 1, spall = 2).
Diverging contour lines indicate the presence of the taperby-surface finish interaction.

No Good−0.034Thrust Raceway Spall

Rationale
Concentrated

Random・Concentration Chart

Taper xSurface Finish

Other

・Group Comparison

Fig. 5 Search tree (updated)

The Search Tree (Fig. 5) is updated per the new
information. The attribute response Y (spall) has now
been transformed into a continuous response X (taper,
surface finish).
Now that the attribute response Y has been transformed
into a continuous response X, the next step in the
progressive search is to apply Multi-Vari and Scatterplot
techniques to taper, surface finish, and their interaction.
Starting with the taper variable, the question was asked
if Heat Treat was causing its variation or some process
before Heat Treat. Taper was measured before Heat Treat
and then re-measured after Heat Treat. The data wasplotted on a Scatterplot (Fig. 6).
It was clear from the correlation (Fig. 6) that taper
variation after Heat Treat depended on the variation theparts had before Heat Treat. A similar analysis was donewith surface finish data as well (for this response, parts
were measured before and after the tumble process).
Taper After Heat Treat, mm

Each column sorted independently−0.032−0.0250−0.0275−0.0300−0.0325−0.0350−0.034 −0.033 −0.032 −0.031 −0.030 −0.029 −0.028 −0.027 −0.026 −0.025
Taper Before Heat Treat, mm

Fig. 6 Scatterplot

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−Effective Convergence Type Problem-solving Technique for North American Automotive Products and Manufacturing Processes−

Note that if the spall had been distributed randomly
over the surface of the raceway, the problem solver should
consider using a Weibull analysis to understand the natureof the failure.
A Weibull analysis of miles-to-failure data (fromvehicles) or hours-to-failure data (from bench tests) will
generate a slope (b) that will show if the spall resulted
from part weakness (b＜1), energy spikes (b＝1), or
premature wear-out ( b ＞ 1). If the wear-out time issignificantly less than what was intended by design, theneither the part was designed incorrectly or the applicationenergy was too high for the part’s design.
In another example, the outer diameter of a thrustbearing raceway was spalling. Specifically, the spall
resulted when the rollers repeatedly hit the high edge ofthe dip in the raceway (Fig. 7). An attribute-to-continuoustransform connected the spall to raceway thickness
variation.
Spall

For the next split, raceways were sampled from allseven cavities of the stamping die. The thickness variation
was the same for each part, up to and including the firstcavity (the piercing step), suggesting the variation existed
in the incoming strip material. Additionally, the thin areas
on the raceways were concentrated on the same side withrespect to the strip feed direction (Fig. 10).

Thin side of
raceway

XX
X
XX
XX
Spall

Strip Feed
Direction

Fig. 10 Concentration chart

Fig. 7 Spall location on raceway

The updated search tree is shown in Fig. 8.
Thrust Raceway Spall

Rationale
Concentrated

Random・Concentration Chart

Raceway
Thickness

Other

・Multi-VariFig. 8 Search tree

The next split asked if raceway thickness variation was
originating at or before the stamping operation or after
the stamping operation. The raceway thicknesses of asample of parts were measured after they were stamped
and then they were re-measured after Heat Treat. The
data correlated (Scatterplot), identifying Stamping as thesource of thickness variation (Fig. 9).

To investigate the strip material influence, a ten-footstrip was cut, reversed in direction, and fed back into
the stamping machine and die. Raceways stamped fromthat strip showed that the thin area of the raceway had
shifted to the opposite side (Fig. 11). This reversal ofconcentration location proved that the thickness variation
was coming entirely from the strip material and not thestamping process. (A common technique in convergent
searches is to “move” a family of variables similar towhat was done in this example to see if it will move thelocation of the concentration.)Thin side of
racewayXX
XX
XXX

Thrust Raceway Spall

Rationale
Concentrated

Random・Concentration Chart

Raceway
Thickness

Other

・Multi-Vari

Stamping or
beforeHeat Treat or
after

・Scatterplot

Spall

Strip Feed
Direction

Fig. 11 Thin side shifts from left to rightFig. 9 Search tree (updated)

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Thin areas in the strip correlated with thin areas in
the raceway. Hence, it was found that the root cause of
raceway spall (attribute response Y) was strip thickness
variation (taper, continuous response X). See Fig. 12.
Thrust Raceway Spall

Rationale
Concentrated

Random・Concentration Chart

Raceway
Thickness

Other

・Multi-Vari

Stamping or
beforeHeat Treat or
after

・Scatterplot

Strip Material

Stamping
Parameters / Die・Concentration Reversal

Taper

Other
Dimensions

・Multi-Vari

To understand how much energy it took to crack awasher, several washers were broken with a tensile
machine and the average strain energy density (area5)
beneath the stress-strain curve was calculated. It wasseen that typical washer strength was greater than all
sources of energy produced by the value stream.
The investigators first approach was to reduce washer
strength. An additional process step was added to weaken
the washers (i.e., change the correct relationship between
system energy and part strength) so they would breakeffortlessly and increase the number of failures. This
result would make it easier to find out where in theprocess the washers were breaking. Specifically, a notch
was cut into six hundred washers taken from the stamping
machine (Fig. 14) and then re-inserted into the remaining
process.

Fig. 12 Final search tree

3. Part Strength vs. System Energy
Components fail when system energy overwhelms partstrength. Investigators must decide if a part’s failure is due
3)
to excessive system energy or insufficient part strength.
The answer can be found by either understanding orchanging both system energy and part strength in thevarious steps of the manufacturing process.
When defects are distributed randomly over a part,one should ask how much energy is needed to make that
defect and which step of the process produces that muchenergy. This approach can lead the investigator to thesource of the problem.
Consider the cracked washer example. This low failure
rate problem (19 ppm) was solved by understanding how
part strength and process energy (and their relationship)changed throughout the value stream.
It was noted that rectangular and uniformly wide
indentations were made at the base of the cracks(Fig. 13). This characteristic was an important clue as towhat was cracking the washers. Any suspected root cause
would have to be capable of making a dent of the same
dimensions.

Crack

Dent

Fig. 13 Characteristic indentation

JTEKT ENGINEERING JOURNAL English Edition No. 1014E (2017)

Notch

Fig. 14 Weakened washer

Most of them cracked, albeit in the tumble process
where the tumbling energy was too low to crack a fullstrength washer, but high enough to crack a weakened
washer. None of the cracks had the characteristic
dents that were seen on the original cracked washers.
Consequently, a new problem-solving approach wastaken.
Since energy leaves impressions that have specifictiming, identity, location, and magnitude, investigators
asked what exists in the process that can make thatspecific dent at that location to that extent. They also
asked if part strength changes throughout the process, andif so, where?
By using a tensile machine to crack washers after
every process step, it was found that washers were theweakest after quenching. When the investigators checked
the oil quench tank, they found a clearance at the rotation
position of the conveyor belt (Fig. 15) with a section
shape that was the same as the dents seen on cracked
washers. It was suspected that the sidewall plates werepinching the washers. To confirm this suspicion, quenchedwashers were dropped between the converging side plates
during belt rotation. A crack with the characteristic dent
was made. It was concluded that pinching sidewall plates
was the root cause of washer cracks.

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−Effective Convergence Type Problem-solving Technique for North American Automotive Products and Manufacturing Processes−

Dropped washer
was pinchedbetween side plates

Plate

96.0

Side Plates

93.0
Gap

90.0
dB

Dropped Washer

87.0
84.0

Conveyor
Oil

Fig. 15 Quench tank with conveyor

81.0
78.0

1st
1st Re- 2st ReAssembly assembly assemblyWOW's 90.5
90.7
90.1
BOB's
83.8
83.1
82.0

FA

JK

83.40
89.90

89.80
83.30

FA
Confirm
90.30 83.3
82.90 89.8
JF

Fig. 16 Component searchThis effect explained all the contrasts: the brittle
cracks, their random locations (implying energy spikes),
the low failure rate, and the size and shape of the dents.
By examining energy impressions and the relationshipbetween the component strength and system energy, theroot cause of defects can be found quickly and effectively.

4. Catastrophic Failures vs. Malfunctions
All examples discussed thus far have dealt withcatastrophic failures: defects that remain after energy
is removed. Another kind of failure is the malfunction:
defects that disappear when energy is removed.
Malfunctions can occur intermittently (unpredictably)or repeat under specific operating conditions. Sometimes
a malfunction can be continual, presenting symptoms
such as excessive noise, heat, and vibration, and yet create
no physical damage.
Solving malfunction problems can be approached byleveraging the first law of thermodynamics (conservationof energy): supplied energy (E1) = work done (E2) +energy loss (E3). Determine which of these three forms ofenergy is causing the malfunction event.
If the malfunction is continual while energy is being
applied, the root cause can be found using the Component
Search technique. The response is decibels, which is ameasure of energy loss (E3). Figure 16 shows an exampleof part swapping between a noisy and a quiet thrustbearing. Two assembled units are used in this study: aBOB, and a WOW, unit.

The three stages of the graph show:st
1 Stage: the normal variation for each of the BOB and
WOW units when they were disassembled andre-assemblednd
2 Stage: how the causal component(s) were foundthrough part swappingrd
3 Stage: how reversals that occurred during the partswapping were confirmed
In the first stage, the noise (dB) of the BOB unit wasmeasured. The unit was disassembled, re-assembled,
and re-measured. This step was repeated, yielding threemeasurements for the BOB unit. The same procedure wasapplied to the WOW unit, producing six measurements inall. The BOB unit remained good (low dB) and the WOW
unit remained bad (high dB). The conclusion drawn fromthe first stage was that assembly variation was not causing
the noise; it was coming from one of the components.
The data for the first stage were used to establish
decision limits, lines meant to show what is “normal” foreach unit based on assembly variation.
In the second stage, the investigator swapped
Components 2 and 3 between the BOB and the WOW
assemblies. The trend line reversed for Component 1
only, which showed that the root cause was coming fromthat component alone (Fig. 16).
In the third stage, the investigator swapped Component
1 again to confirm the original reversal. The result
confirmed the correlation between noise and Component
1.
Without a detailed investigation of all components,
Component 1 was taken from eight BOB assemblies andeight WOW assemblies. An inspection of Component 1
found scratches on all of the WOW parts but none on the
BOB parts.
Below is the final Search Tree for the noisy bearing
malfunction problem (Fig. 17):84

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−Effective Convergence Type Problem-solving Technique for North American Automotive Products and Manufacturing Processes−

References

Thrust Bearing Noise (dB)

Rationale
Product
Variation

Measurement
Variation

・Measurement process passes

Component

Assembly
Variation

・Stage 1 Component SearchComponent 1

Components
2 and 3・Stages 2 and 3 Component SearchComponent 1
Scratches

Other
Characteristics

・Concentration Chart

Fig. 17 Thrust bearing noise search tree

Malfunctions are easier to solve when the contrast is
known between what a good unit is supposed to do andwhat a defective unit is actually doing. Malfunctioning
units will usually emit some kind of energy loss suchas heat, noise, vibration, etc., which can be used as a
response at the beginning of the convergent search tree.
If product failures are catastrophic such that nofeatures can be measured on the damaged parts, the root
cause can be found by pre-measuring features prior to
testing the parts. The data from parts that failed the test
are then compared with the data of parts that passed thetest (a Group Comparison analysis − see examples inTables 1 and 2).
Another method for dealing with catastrophic failures
is to monitor a variable that changes with time thatusually reaches a predictable level prior to a productfailing. BOBs and WOWs can then be chosen based on
the length of time that passed before the indicator variable
has reached its failure limit.

1)

2)

3)
4)
5)

N. Reddy KM, Y. Varadarajan, R. Prsad: Quality
Improvement during Camshaft Keyway Tightening Using
the Shainin Approach, International Journal of Scientificand Research Publications: Vol. 4, No. 7 (2014) 1-5.
P. Shainin, R. Shainin, T. Nelson: Managing StatisticalEngineering, Transactions of 51 st Annual Quality
Congress, (1997) 6.
K. Bhote: World Class Quality, 2 nd ed., AMACOM,(2000) 147-156, 157-193, 194-216.
M. Hicks: Problem Solving and Decision Making, 2nded., Thomson, (2004) 93-94.
D. Roylance: Stress-Strain Curves, Department ofMaterials Science and Engineering, MIT, (2001) 9.

5. Conclusion
In recent years, convergent search techniques have beenused in North America for solving complex problems
in automotive products and processes. A few of thosemethods were introduced in this paper. These were:
1) Categorization of the response of failed parts todetermine how an investigation should begin
2) Transformation from an attribute response to acontinuous response and then convergence to a root
cause3) Convergence to a root cause based on the relationshipbetween system energy and part strength4) Convergence to a defective component within a
product assembly by swapping BOB and WOW
components
By using these techniques, complex problems can besolved fast and effectively.
M. MCKEEL**JTEKT ENGINEERING JOURNAL English Edition No. 1014E (2017)

KOYO BEARINGS NORTH AMERICA LLC

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