Improvement strategy by using ML and TM. A case study for solving cracking in sand casting

Improving productivity and yield by
using combination of Taguchi method
and Machine learning techniques
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission is strictly prohibited
1
Presentationby JagadishC.A.
jagadish.chandra@qmaxim.com
20th of Feb., 2015, v1.5
Illustrated in a case study related to manufacture of casting by sand casting
process

CASE STUDY PRESENTED HERE ILLUSTRATES A NEW
APPROACH FOR SOLVING THIS KIND OF PROBLEM.
KNOWLEDGE OF METALLURGY, PROCESS & CASTING IS
COMBINED WITH ADVANCED STATISTICAL OPTIMIZATION
TECHNIQUES OF MACHINE LEARNING AND TAGUCHI
METHOD IS USED. PROBLEMS CAN SOLVED MORE
QUICKLY, ECONOMICALLY & WITH GREATER CERTAINITY.
PROBLEMS LIKE CRACKING IN CASTINGS IS
TRADITIONALLY SOLVED BY TRIAL AND ERROR
METHOD USING KNOWLEDGE OF METALLURGY,
PROCESS & CASTING. THIS IS A HIT & MISS
PROCESS.
2

BY READING THIS PRESENTATION
ONE CAN GET SOME INTUITION
ABOUT THE APPROACH & HOW ONE
CAN APPLY IT IN THEIR OWN WORK
3

content
4
•Case study phase #2 – DOE by Taguchi Method & advanced
data analysis
•Appendix-1: Primer on Machine learning & Taguchi Method
•Case study phase #1 –exploratory analysis
Setting up context-Setting up context-
economicreality, challenges, opportunities
•Phase #2– confirmation
•Findings, Conclusions, summary
•Questions,background, Contacts
•Overview of new approach, case study background
•Appendix-2: Sand casting some resources

The manufacturing reality
manufacturing sector-many challenges
• Still low growth rates in manufacturing
• Customer requirements getting tougher
• Severe competition and pressure on margins
• Continuouspressure to reduce costs
• Increasing complexity, automation,increasing
need for new skills
• Younger inexperienced but ambitious workforce
• Continuouspressure for rapid & big
improvements

The manufacturing issues
data analysis & continuous improvement challenges
• Data deluge –vast amounts of data collected but not much
analysis. Many sources of data – SCADA, CRM,ERP, customer
data, handwritten notes, Excel., social networks....
• Data in electronic or paper form, many formats – quite often
not analysed
• Internet of things becoming reality – more data, more
communication- increasing complexity
• too many variables – how to reduce the number to do
analysis?
• variables are not just quantitative but categorical or ordered
categorical .
• Simple data analysis tools less and less useful e.g. Simple
Regression
• Continuous improvement based on Kaizen, 7 basic tools for
quality improvement, Shainin DOE are useful but not that
effective for big improvements

The manufacturing reality
many challenges but also opportunities...
• Classical vary one factor at time approach has severe
limitations
• Choosing operating conditions to balance productivity
& yield is not easy
• Traditional six sigma does not incorporate many of the
new tools and methodologies which are now available
• But, this is also an opportunity......
– Unheard of granularity & richness of data
nowadays
– Falling cost of computation, storage
– Powerful machine learning tools are available for
data analysis & optimization (many of them open
source & free)

8
content
8
data analysis
•Case study phase #1 –exploratory analysis
•Setting up context-
••Overview of new approach, case study background

Outline of the new methodology is as follows
• New methodology
– Uses combination of machine learning & Taguchi
method
– Can find best operating conditions rapidly without
compromising productivity
• Illustrated with a case study related to sand
casting, but can be applied to any manufacturing
situation
(Why sand casting chosen?
Sand casting is very noisy process with many variables, uses
highly variable natural substances & is manual process – if it can
work in sand casting it can work in any manufacturing situation )
– see next 3 slides for overview of methodology
– case study in the subsequent slides
9

manufacturingimprovementstrategy outline
main objectives and major issues manufacturing faces
1. Improveyield by reducing
defectssuch as cracking, pin
holes, bad surface......
Mainobjectives of
manufacturing
2. Improveproductivity & cost
by reducing time taken by
operating at highest possible
speed and reducing inputs as
much as possible
How to set process parameters?
e.g. Temperature, degassing rate, speed – start
and steady, time- start, ramp up...
Make any major changes?
e.g. Equipment change, mould design, invest?
Majorissues in manufacturing

manufacturingimprovementstrategy outline
Classical approach to tackle this problem which most companies follow
Mainobjectives of
manufacturing
much as possible
e.g. Equipment change, mould design ..
at a time holding others constant
validity
Do trials by varying one factor
at a time holding others constant
Problem:
Many parameters – too many
trials. Results have no statistical
validity
Quite often investments are madeQuite often investments are made
without utilising potential of existing
equipment
ClassicalapproachClassicalapproach

manufacturing improvement strategy outline
Classical approach which most companies follow
new approach has three phases as outlined
Mainobjectives of
manufacturing
much as possible
e.g. Equipment change, mould design,
modernization ..
Phase #1Phase #1
Do exploratory data analysis existing data
using linear regression & machine
learning. Gain insights & short list
important variables
Do sophisticated data analysis and find best operating
Phase #2
1. Do trials by Statistical experimentation by making
changes to process parameters and system.
2. Do sophisticated data analysis and find best operating
conditions
New approachNew approach

manufacturing improvement strategy outline
Classical approach which most companies follow
new approach has three phases as outlined
Mainobjectives of
manufacturing
much as possible
e.g. Equipment change, mould design,
modernization ..
Phase #1Phase #1
Do exploratory data analysis existing data
using linear regression & machine
learning. Gain insights & short list
important variables
Do sophisticated data analysis and find best operating
Phase #2
1. Do trials by Statistical experimentation by making
changes to process parameters and system.
2. Do sophisticated data analysis and find best operating
conditions
Phase #3
Decide whether to make major investments
New approachNew approach
Phase #1,#2 covered
in in the following
slides

Outline of the new methodology, it has following steps
1. Do exploratory data analysis by using many
modelling algorithms - linear regression, cluster
analysis & LASSO (or other machine learning
algorithms) and gain insights and shortlist
variables of importance (Phase #1)
2. Use Taguchi Method (TM) to find important
factors & their best levels (DOE) by doing
planned manufacturing runs & analysing data
(Phase #2)
( what is machine learning & TM? see appendix-1)
Continued...
17

Outline of the new methodology, it has following steps
3. Confirm findings by doing actual
manufacturing runs using optimum
conditions found in #2 (Phase #2)
4. Use entire data and build models by using
linear regression & LASSO (or other machine
learning algorithms) – for further insights and
verify efficacy of findings (Phase #2)
5. Operate under optimum operating conditions
found in steps #1to #5 & monitor
performance on a long term basis
Note : these steps marked as: on the slides
18
1

Illustrative case study details
• Objective :
– Reduce cracking significantly
in a mild steel part of a
particular design
manufactured by sand
casting
– Also, significantly reduce
overallconsumption of
welding electrode for repair
(additional details next slide)
(what is sand casting? See Appendix -2)
19
0

Illustrative case study details
• Manufacturing Process : Sand casting
• Operating mode: completely manual
• Design : pipe joint, mild steel
• Complicated production process (2 box, cored)
• Project relates to cracking seen in a certain design
during 1st stage inspection after pouring, shake out,
fettling, heat treatment, shot blasting (see schematic of
process & some pictures in following slides)
• Magnetic particle inspection (MPI) required as crack is not
visible to naked eye. Defectiveportions have to be
repaired
• Almost all castings crack
• Cracking has severe cost implications –repair, welding
consumables,heat treatment, lost time....
20
0

Case study: Reducing cracking in pipe joint cast by sand casting
Schematic process flow rework by welding at 1st stage inspection,
Amplification effect- for each 1kg of casting got to cast 1.9 of metal
21
1st stage inspection,1st stage inspection,
defectdetection&
repair
0

Illustrative case study details –some pictures
Some Process steps –2 piece box bottom with chills
23
0
Bottomhalf of mould
with chillsseen
Core coated and ready
for insertion
Metalpoured into
mould

Illustrativecase study details
cracks almost always occur at approximately the same place
24
0
Cracks marked after inspection.
Cracks occur mostly at the same
location
Crack runs throughout
the section of pipe

25
content
25
data analysis
Case study phase #1Case study phase #1 – exploratory analysis

Phase #1 overview
varioussteps, sourceof knowledge
following slides havedetails
Furnacecontent, operating conditions, chemistry
records
14 months,100 furnaceloads, 4-6 designs / cast
=6500 records
Rework records
Technical
information
Cleanup, combine, createnew
variables, separate information
aboutcrack pronedesign of
interest
Exploratory graphs. Advanced
statistical analysis – regression,
machinelearning -model
building
Interpretations, Findings,
presentations
Get insights, shortlist
importantvariables & decide
on next steps
Metallurgy
casting
Machine
learning
1

Effect of Variables on response variable studied
Companycollects vast amountsof data
furnace content, schedule, chemistry, operating conditions....studied (~27 variables)
• PartName
• Fur No
• Furnace content
• Week day
• shift
• grade
• Pour Time Slot
• Tap temp
• Mould Hardness
• coating mould
• Core Hardness
• coating core
• Piece weight to total
Liquid ratio
• Ladle temp.
• C Mn Si S P
• Cr Ni Mo Cu V
• Carbon equivalent –
Conventional
• Carbon equivalent –
Japan
1

Some Exploratory graphs- histograms
tap temp, core hardness, mould hardness, ladle temperature have 2 distinct groups
1

Some Exploratory graphs of chemistry -histograms
Mostly near normal distribution, some large values seen 1

Exploratory graphs - histograms
Ni,Mo,Cu, skewed with some large values
1

Exploratory ECDF (empirical cumulative distribution function) graphs of variables
2 furnaces show different behaviours w.r.t. tap and ladle temperature this is probably the reason
for grouping seen
Furnace B
Furnace A
1

ExploratoryECDF graphs –mould/ core hardness
different mould coatings show highly different hardness- this is reason for groups seen
cotpol
Ceramol
930
1

Some of the Exploratorybox plot graphs of operatingconditions on cracking tendency
higher tap temperature, higher mould hardness, higher ladle temperature are better
1

cluster dendrogram done to group similar variables in entire dataset
Relationship ofcracking with various variablesmarked – zoomed view next slide 1

Cluster dendrogram , area of interest zoomed in - relationshipof cracking with various
variables
cracking tendency groups with shift, tap (melt) temperature,ladletemp, furnace No
1

Linear Regression analysis of data set between cracking built
ANOVA table created the table by introducing each of the terms in the model one at time
Variables of importance highlighted
Analysis of Variance Table
Response: CracksP
Df Sum Sq Mean Sq F value Pr(>F)
Fur_No 1 11.073 11.0733 18.0554 4.657e-05 ***
Paint_mld 1 3.529 3.5291 5.7544 0.0182110 *
Paint_core 3 2.266 0.7552 1.2314 0.3020912
Tap_temp 1 17.235 17.2349 28.1022 6.408e-07 ***
Mld_Hardness 1 0.525 0.5252 0.8564 0.3568672
Core_Hardness 1 0.126 0.1258 0.2052 0.6515131
Ladle_temp 1 0.949 0.9493 1.5479 0.2162225
C 1 8.978 8.9779 14.6388 0.0002214 ***
Mn 1 0.204 0.2045 0.3334 0.5649174
Si 1 0.122 0.1219 0.1987 0.6566789
S 1 2.260 2.2598 3.6846 0.0576309 .
P 1 1.143 1.1432 1.8640 0.1750845
Cr 1 0.254 0.2536 0.4135 0.5215845
Ni 1 3.314 3.3135 5.4028 0.0220296 *
Mo 1 0.005 0.0048 0.0078 0.9297360
Cu 1 4.506 4.5063 7.3477 0.0078453 **
V 1 0.116 0.1155 0.1883 0.6652000
P2_L_ratio 1 4.742 4.7424 7.7327 0.0064290 **
C:Ni 1 1.200 1.1995 1.9559 0.1648990
Cr:Ni 1 1.871 1.8714 3.0514 0.0835905 .
Residuals 105 64.396 0.6133
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1

Model built using LASSO (generalized linear model via penalized maximum likelihood ) regression
output shown
The algorithm keeps coefficients of only important variables
(Intercept)Fur_NoB Paint_mldCotpol Tap_temp Ladle_temp C
-0.4368 .4353 0.3358 -0.4066 -0.2325 0.1821
S Ni Cu P2_L_ratio
0.1313 0.2184 -0.1775 -0.1882
> coef(cvfit, s = "lambda.min")
30 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) 7.888494721
no_pcs -0.002647573
Tap_temp -0.003587278
Mld_Hardness .
Core_Hardness .
Ladle_temp -0.001463345
C 1.240515245
Mn .
Si .
S 1.225540417
P 1.746826872
Cr -0.100157651
Ni 0.595015164
Mo 0.270251629
Cu -1.480281443
V .
P2_L_ratio -0.129797323
CEa .
CEj .
Fur_NoB 0.051100821
Fur_NoC .
Paint_mldCotpol 0.002589163
Paint_mldHolcoat .
Paint_mldIsomol .
Paint_mldSNS .
Paint_coreCotpol 0.020449112
1

Summary of some of the findings from phase #1
distinct groupings seen, reason found
• distinct groups seen – tap temp, ladle temp, mould
hardness, core hardness, furnace modifiers ratio,
casting sequence
• tap temp, ladle temp depends on furnace used, casting
time slot, weekday.
• mould hardness, core hardness depends coating type
• Chemistry many elements skewed, contamination seen
• furnace modifiers ratio – large difference in ratios
between furnaces
1

Conclusions, insights, summary
operating conditions, chemistry are correlated with cracking
• Several algorithms used to shortlist variables of
importance and their effect on cracking
• Findings:
– almost similar conclusion can be drawn from output of all algorithms
– Some of the variable have significant effect on cracking e.g. Melt
temperature, ladle temperature , furnace No...
– Effectof variables on cracking is as follows:
Tap temp (+), ladle temp (+), mould hardness (+), core hardness (+),
furnaceA ( +), time slot (-), weekday (-), modifiers ratio (+/-), casting
sequence(mid better), piece to liquid ratio (+)
Chemistry – C(-),Mn(-),S(-),P(-),Cr(+),Ni(-),Mo(-),
Cu(+),Carbon Eq. (-)
( Coding :- + higher level better, - lower level better)
1

Conclusions, insights, summary
operating conditions, chemistry are correlated with cracking
• Significantinsights gained from phase #1 by
studying existing data without making any
process change
• Other aspects like methoding, raw material,
operating conditions-delay, ladle conditions,
pouringheight, ambient humidity etc may
contributeto cracking
• Detailed industrial study such as DOE needs to
be done to get even better insight to solve this
problem (to be done in phase #2)
1

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content
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data analysis
data analysis
•Case study phase #1 – exploratory analysis

Methodology followed (phase #2)
Overview of steps – following slides give details of each box
Make general observations about the problem –frequency, where, when, macro, …
Choose a DOE experimental plan based on phase #1 conclusions, new considerations & cost aspects -
factors to vary, their levels , interactions , number of replicates per run
Taguchi DOE chosen
Factors – chemistry (pure, impure), tap temp (low, high), ladle treatment( high, low), methoding -design (old, new)
Do casting runs (8x4) according to plan
Collect data
Identify each casting
Strip casting, section, HT, grit blast, prepare for MPI
Make Interpretations, findings, recommendations
Make presentation
Measure for each casting -crack length by MPI & DP and record
Data entry to specialized software, analyse data
and generate reports and
charts (mean , S & N )
Metallurgy
casting
Machine
learning
Build regression and machine learning models using all individual data
Do casting runs using optimum levels of factors
Measure crack length by MPI & DP and record
2

Phase #2
Steps in finding important factors & their best levels by doing TM casting runs.
Overview of experimental plan was as follows, details in following slides
44
1. methoding
2.chemistry
3. tap temp
4. Ladle treatment
Factors
chosen
As many as 150 variables.
Other factors heldconstant/monitored:
e.g. Sand properties, binder properties, mould
preparationmethod, mouldcoatings, furnace
content, furnace treatment, ambient temp. ,
relative humidity..........
Design
chosen
Experimental
design (L8), (27)
array, 8 runsin
duplicate
(2x8x2=32
castings)
Experiments
run
Response &
other data
collected
Data
analysed
Program
output:
response
table and
charts (mean
& S/N)
Conclusions
1. Factorsof
significance
2. Interactions
of
significance
3. Optimum
factor levels
Response : length of
crack measured by
MPI
Note:
1.Morethefactors/levels chosen larger will be the
number of experimental runsto be done, hence more
expensive it becomes.
2. Do production run as per the design chosen
2

Castingexperimental plan based on DOE
plan shown factorswith their levels, interactions, response variable
Chemical analysis,tap temp, ladletemp, fill time, start/end time,
preparationdelay,knockoutdelay, etc noted down
Response: Crack length
Interactionsalso estimated :
chemistry with melt temp, melt temp with ladle treatment
chem furnace melt temp
ladle
treatment
methoding
design DOE_Run_No
pure high (1625-1640) high old 1
pure high (1625-1640) low new 2
impure low (1590-1605) high old 3
impure low (1590-1605) low new 4
pure low (1590-1605) high new 5
pure low (1590-1605) low old 6
impure high (1625-1640) high new 7
impure high (1625-1640) low old 8
Factorstested:
Chemistry furnace, melt temp, methoding design*, ladle
treatment
* See next slide for details
2

Methoding–design changes made during phase #2
Old design shown in picture, changes made iteratively to some of the chills and a risers 2

DOE-means plots findings
Modified mould design is significantly better, higher levels of ladle treatment, higher level of melt temp
& impure chemistry better
2

DOE interactionplots
Interaction terms aresignificant 2

DOE findings - ANOVA of means output
Modified mould design is significantly better & the most importantamong factors.
Interactionsof chem. analysis with melt temp and melt temp with ladle treatment are also significant
Analysis of Variance for Means
Source DF Seq SS Adj SS Adj MS F P
chem furnace 1 35.596 35.596 35.596 41.33 0.098
melt temp 1 17.627 17.627 17.627 20.46 0.138
ladle treatment 1 104.221 104.221 104.221 121.00 0.058
mould design 1 397.268 397.268 397.268 461.23 0.030
chem furnace*melt temp 1 265.939 265.939 265.939 308.76 0.036
melt temp*ladle treatment 1 176.955 176.955 176.955 205.44 0.044
Residual Error 1 0.861 0.861 0.861
Total 7 998.467
Response Table for Means
chem ladle mould
Level furnace melt temp treatment design
1 36.94(P) 33.34 (H) 38.44(high) 41.88 (old)
2 32.72(IP) 36.31 (L) 31.22 (low) 27.78 (new)
Delta 4.22 2.97 7.22 14.09
Rank 3 4 2 1
2

General conclusions on cracking susceptibility as per DOE findings & best operating conditions are as
follows
• Best operating conditions:
– New methoding design is significantlybetter than old
design
– Higher melt temperature range is better
– Higher levels of ladle treatment is better
– Recommended chemical composition is impure, but
effectis small between pure and impure.
– Also, interaction between chem. Composition/ ladle
treatmentwith melt temperature is important
• Several casting runs were done under the
recommended conditions to verify findings
• Overall, 4 iterations of new designs also tried
3

Linear Regression was done of the complete dataset
ANOVA table output
Mould design, sequence of casting, S,P, ladle temperature significant
Analysis of Variance Table
Response: crack length
Df Sum Sq Mean Sq F value Pr(>F)
mould.design 4 4684.0 1170.99 12.1631 1.86e-05 ***
sequence 3 833.7 277.91 2.8867 0.057489 .
C 1 0.8 0.77 0.0080 0.929651
S 1 782.3 782.25 8.1253 0.009053 **
P 1 606.3 606.28 6.2974 0.019581 *
Ni 1 141.7 141.70 1.4719 0.237362
Cr 1 8.4 8.45 0.0878 0.769705
ladleTemp 1 432.5 432.53 4.4927 0.045046 *
Residuals 23 2214.3 96.27
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
4

Modelwas also built using LASSO (generalized linear model via penalized maximum likelihood ) regression of
completedata set
Outputis seen below
The algorithm drops variables which are not important, important variables shown in the table below
Coefficients generated by LASSO after cross-validation
3 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) -472.51096012
C .
S 1594.72775370
P 3218.85844853
Ni -50.26465017
Cr .
Si .
Mn .
Cu .
Mo .
V .
Al .
Ti 8370.87631400
Fe.silinium .
tapTemp -0.05934132
ladleTemp 0.32709791
mould.designnew1 -15.17664341
sequence2 -7.92797995
sequence3 -12.29255408
sequence4 -3.85168798
4

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53
data analysis
••Phase #2– confirmation

Severalcasting runs weremade under optimum operating conditions to verify findings
BoxPlots comparison of most important factor– methoding design shown
New designs are significantly better, Iteration new #4 is best of the lot under optimum operating conditions
Longer casting runs will be produced using the optimum conditions found to evaluate long term performance
4
5

55
content
55
data analysis
••Findings, Conclusions, summary

OVERALL CONCLUSIONS
56
• Approach on use of Taguchi method and
Machine learning combination for yield &
productivity improvement was explained. Some
background information was also presented
• Illustrative case study related to sand casting
manufacturing process was presented.
• Use of various algorithms to quickly gain
valuable insights and to find variables of
importance and their effect was illustrated
• Optimum operating conditions found from both
the phases were revalidated in actual
production run
Continued.....

OVERALL CONCLUSIONS
57
• This project required relatively limited effort,
was of low cost, was of short duration, did
not disrupt operations significantly
• By using this approach, it is possible to
efficiently find optimum operating
conditions even in highly noisy, completely
manual process with large number of
variables
• It is also possible to use the information
gained by this approach to decide on
prioritizing areas of investment for
modernization

58
content
58
data analysis
••Questions,background, Contacts

THANKS!
QUESTIONS?
59
Jagadish C.A. (Rao)
jagadish.chandra@qmaxim.com
qmaxim001@gmail.com
+91 9900 606620
+91 9538 328704
blog: qmaxim.wordpress.com
Twitter: @JagSpeak
q-Maxim

Jagadish C.A. ‘s Profile
60LinkedIn profile: http://goo.gl/Lp3lWv
• Heads niche consultancy q-Maximfocused on adv. Optimization, quality, technology….
• B.Tech (MetallurgicalEngg., NIT-K,Surathkal,India)
• Done many graded online courses (4-15weeks) from leading US universities on
operationsmanagement, advanced data analysis, marketing, finance, accounting,
strategy,advancedcompetitive strategy,data scientist (5 courses),credit risk
management,game theory, logistics
• ASQ (American society for quality) certified Six sigma Black belt (since 2002)
• ASQ certified Manager of Org. Excellence/ Quality (since 1999)
• Certified EFQMassessor extensive experience assessing companies, fashioning
transformationalroadmap& implementing EFQMmodel
• JuranQI facilitator,Cert. adv. Industrial experimentation, Analytics
• ISO 9001:2008,14001,QS-9000(TS16949)lead auditor
• ~32years experience in Manufacturingandtransactionalfields, 7.5 years as
managementconsultant
• Richexperience in Quality, process management, R +D, Technology, Cost reduction,
Managementconsultancy.Extensive experience in using advanced methodologies for
problem solving and optimization
• Extensive exposure/ knowledge of heavy process oriented manufacturing – Aluminium
casthouse, Steel, Welding, casting, foundry, Metallurgy, etc
• Widely travelled – India, north America, Europe, M.E.

61
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61
data analysis
••Appendix-1: Primer on Machine learning & Taguchi Method

Whatis Machine learning ?
Some definitions
• Definition : “A computer is able to learn by experience
without explicitly being programmed – & improves
performance as it learns”
• Based on field of artificial intelligence
• Examples :
– Mining data from large datasets website click trough data to improve
purchase conversion rate
– Autonomous self flying helicopter (Stanford University)
– Classify e-mail as spam or not spam (filtering spam in outlook.com)
– handwriting recognition (tablets)
– Computer Vision (reading car number plates & giving speeding tickets)
– Self driven cars (Google self driving car)
– Recommendersystems (Amazon recommending books)
• Not that common in manufacturing....
62

Whatis Machine learning ..
2 major types - Predictive & descriptivetypes
• Predictive learning (supervised learning)
there is outcome variable to guide the learning process
– Learning phase
learning by example. Tune the model until error is sufficiently
low
– Scoring phase
Use the model for making predictions (or score) in real time
e.g.: linear regression, polynomial regression, LASSO, random
forest, Neural network, Decision tree
• Descriptive (Unsupervised learning)
– No outcome variable
– Self-organization, no teacher
– Groups observations (variables) based on similarity, to uncover
hidden relationships
– e.g.: clustering
• Regression or classification problem based on type of outcome
63

Machine learning example –data in simple table form
Learning algorithm for predicting house prices- various parts labelled
64
Row
no
Area
[sq. Ft.]
Number
of rooms
Age of
flat
[years]
Gym
[Y/N]
Swim
ming
pool
[Y/N]
............... Other
features not
shown.............
Market
price
( lakh
Rupees)
1 1800 5 1.1 yes yes 68.6
2 900 3 4 no no 34.5
3 1720 5 8 yes no 47.7
4 560 2 .7 no no 25.4
.....
1000 2400 6 3 yes yes 91.8
CalledTarget
or outcome
or outputCalledPredictors or
inputsor features
Records
orrows

Machine learning– supervised learning -overview
example- predicting market price of house using simple linear learning algorithm
65
SampledTrainingdataset
Known
1. Area of house
2. Number of rooms
3. Age of house
4. Location
5. Gym [y/n]
6. ..... Etc, etc
Learning algorithm
predictive hypothesis
h(x)
Prediction
marketprice of
house
Calledtarget or
outcomeCalledfeatures
or predictors
h(x)is a linear equationof
the type:
hθ(x) = θ0+ θ1x1 + θ2x2 +....... Θnxn
Past data of
housing market
having features &
predictors
Learning
phase
scoring
phase

Unsupervised learning – K means cluster analysis and dendrogram
Groups observations (variables)based on similarity, to uncoverhidden
relationships
• K-means clustering- a type of unsupervised
learning
• Example:
Clustering of wine database from 1996, of wines grown
in the same region of Italy, but derived from three
different types ( cultivars )
Databasehas 78 observations, 13 chemical analysis
variables & 1 column of 3 cultivars
Source :University of California at Irving (UCI) Machine Learning
Depository
• We want the machine learning algorithm to cluster the
data & uncover the types without seeing the type (
cultivars ) & check the accuracy

Unsupervised learning – K means cluster analysis
It has classified 178 observations into 3 clusters using 13 chemical analysis
variables
Data: Universityof California at
Irving (UCI)Machine Learning
Depository

Unsupervised learning – K means cluster analysis- colored based on type (cultivar)
classificationaccuracy is pretty good as seen by coloring the cluster on type(cultivar) of wine

Supervised learning – decision tree a simple predictive learning algorithm
Decision tree to predict –’what possibility of a person surviving Titanic sinking given his/her
age,sex,sibling?’
69
A tree showing survivalof passengers on the Titanic ("sibsp" is the number of spouses or siblings aboard).
Thefigures under the leaves show the probability of survival and the percentage of observations in the leaf.
Source:WIKIPEDIA

Supervised learning – Random forest a predictivealgorithm based on decision tree
Exampleof random forest use for classifying land type using Landsat satelliteThermal
InfraredSensor image of 4 spectral bands
The algorithm can classify landmass automaticallyafter it has ‘learnt’ by seeing known
data
70Credit:internet resources

Supervised learning – Artificial Neural network predictive learning algorithm
Mimic working of brain and nervous system, Ideal for modelling complex relationships
Use of artificial neuralnetwork to do handwriting recognition.
71Credit:internet resources

Want to know more about data mining and machine
learning?
Read my presentation titled ‘Data mining and
Machine Learning - in jargon free, lucid
language’ .

Taguchi method (TM) is very popular optimization method. Some
details and advantages
• TaguchiMethod (TM) is very common optimization method
in science & engineering for over 40 years
• Has following components - System design, Tolerance design
and parameter design
• We are using parameter design
• TM is not a theoretical exercise, done in production
environment, justsomeadditional measurements
• TM has many advantages :
– Less effort, economicalthan T & E & full factorial
– much more insight
– Faster
– Canfind important factors, their best levels& interactions
– Also Signal to Noise to find best levelsfor minimizingvariation
– Ableto decide on parameterskeeping productivityin mind
– Findingshave Statisticalvalidity
73

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• Factors :
– Control: best levelsfor desirable output : e.g. Pouring temperature
– Signal: can influence,can be controlled : e.g. volume knob position
– Noise : can’t be controlledOr are intentionallynotcontrollede.g. ambient
temperature
– Others: not changing during experimentation
• Response:
we are desirous of controllinge.g.- defect %age
• Factor levels:
– Range of values for doing experiments
• Interaction of factors
– when one factor has influenceon the effect of the other factorrespectively
DOE terminology

Taguchimethod (TM) steps involved are as follows
1. Define the problem and objective.
2. Choose response variable(s) for doing optimization.
3. List out all potential factors.
4. Choose important control factors and their range & number
of levels. Also, decide factors not to be varied and the
ranges in which they should be held constant.
5. Decide on experimental design
6. Conduct trials (called experimental runs) in actual
production environment & collect response variable(s) data.
7. Analyze the data, determine the importance & best levels of
controlfactors – conclude optimum operating conditions
8. Reconfirm findings by doing production using optimum
operating conditions
75

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content
76
data analysis
••Appendix-2: Sand casting some resources

Sand casting additional information
typical sequence of steps
Credit:Prof. Timothy Gutowski

Sand casting additional information
1. Sand casting page from Wikipedia gives a
good introduction. There are numerous
resources on the web
2. Simplified sand casting process video is
available here . There are numerous videos
on the internet
78

Improvement strategy by using ML and TM. A case study for solving cracking in sand casting

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