The document discusses Q-Maxim's approach to reducing defects in sand casting through a combination of Taguchi methods and machine learning. A case study highlights the project's aim to decrease top defects and repair costs by optimizing various manufacturing factors, leading to significant improvements in defect rates and welding material consumption. The results of the experimental design and confirmatory runs demonstrate a 59% reduction in defect levels and a 70% decrease in welding electrodes used for repairs.

1. q-Maxim’s approach to problem solving & optimization – typical manufacturing project
A high impact project leading to dramatic reduction in defects in a Foundry application
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By
Jagadish C.A. (Rao) , Founder of q-Maxim
V 1.3F 17-10-2014

2. BY READING ONE CAN GET SOME INTUITION ABOUT THE APPROACH AND HOW ONE CAN APPLY IT IN THEIR OWN WORK
THIS PRESENTATION GIVES OVERVIEW OF WASTE REDUCTION APPROACH OF Q-MAXIM FOR RAPID OPTIMIZATION WITHOUT COMPROMISING PRODUCTIVITY. IT USES COMBINATION OF TAGUCHI METHOD AND & MACHINE LEARNING. THIS APPROACH IS ILLUSTRATED IN A SAND CASTING CASE STUDY
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3. 3
•Background, Business case
•Get insights
•Prepare for DOE
•Design and run experimentation
•Analyze results
•Confirmatory runs, conclusions, contacts, background of founder q-Maxim

4. Background, Business case
•An export focussed foundry was getting high level of rework during first stage inspection
•Manufacturing Process : Sand casting, highly manual, induction melting furnace (see the next few slides for overview of the mfg. process)
•Design : Casing (valve case), Aus. Stainless Steel (CF8M)
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5. 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
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6. Background, Business case
•An export focussed foundry was getting high level of rework during first stage inspection
•Manufacturing Process : Sand casting, highly manual, induction melting furnace (see the next few slides for overview of the mfg. process)
•Design : Casing (valve case), Aus. Stainless Steel (CF8M)
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7. Background, Business case
•Complicated production process (3 box, cored), low throughput
•Project relates to 1st stage inspection & repair, steps being:
–marking defectives, grinding, notching, repair welding, grinding, re-inspection, X- Ray, etc
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8. Background, Business case
•Severe cost / time implications :
–Non value adding activities : Inspection, marking defectives, notching, welding, grinding, re- inspection
–Additional cost: welding consumables, salaries of welders, grinders, inspectors
–Capital cost: welding equipment, grinders , ovens, etc
–Time lost: inspection, marking, notching, welding, grinding, re-inspection (all these activities are non-value adding & complete waste)
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9. Background, Business case Schematic process flow
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1st stage inspection, defect detection & repair

10. Background ,Business case
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Sand mould under preparation, core in place

11. Background ,Business case
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Metal being poured into sand moulds

12. Background ,Business case
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Sand moulds filled

13. Business case – background, goal of project
•Top 3 defects in the first stage visual inspection:
–Sand mark type
–Slag type
–Cold metal
•Sand mark type defect largest
•Several similar casing designs produced by company, similar defects
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14. Business case – background, goal of project
•Defect levels in the industry are somewhat similar
•Traditional problem solving approaches unsuccessful
•Target :
1.Reduce top 3 defects by 50%
2.Reduce cost of repair by welding by 50%
[See picture of sand mark type defect in the next slide]
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15. Background, Business case
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Sand mark type defects appearance (circled)

16. 17
•Background, Business case
•Get insights
•Prepare for DOE
•Design and run experimentation
•Analyze results
•Confirmatory runs, conclusions, contacts, background of founder q-Maxim

17. Get insights- Possible sources of variation
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Metallurgical literature
Historical data
Company documents, procedures
Casting literature
Theoretical / computer simulation
Analyse data Excel – VB programming, Pivot tables Data mining Monte Carlo simulation
Discussions Brainstorming
Technological information
INSIGHTS

18. Gating design- e.g. metal velocity
Ladle temperature
Sand production parameters
Ambient humidity, temperature
Pour height
Mould fill time
Wait time after mould preparation
……………..
……………..
Sand mark defect
Slag inclusion defect
Cold metal defect
Factors
Effects
Responses
++ ?
--?
No effect?
Strong / weak?
interactions?
responses interrelated?
Range dependant?
By brainstorming it is concluded that > 50 variables & their interactions could have influence on defects generation.
Casting temperature

19. Get insights- typical data analysis output
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In 40 % casts no defects
In 20 % casts all castings defective
Variation in Sand mark type defects

20. Get insights- typical output
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Output of Data mining – cluster analysis Which variables have possible linkages?

21. Get insights- conclusions
•Large number (>50) of variables (called factors, noise) & their interactions could have effect on defect(s) generation
•Noise – no control : e.g. humidity, ambient temperature
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22. Get insights- conclusions
•Defects could be interrelated. (i.e. increase one type of defect could lead to drop of others and vice versa. For example, increasing melt temp. may result in reduction of cold metal type defects which may lead to increase in slag type defects)
•Thus it is a complex problem not easy to solve
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23. Get insights- conclusions
•Traditional experimentation methods – ”experiement by changing one variable at a time”
or
technological / metallurgical solutions are of limited use due to complexity as the company found out
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24. Get insights- conclusions
•Statistical methdologies called Design of experiments (DOE) are suitable for solving such complex problems & have been used extensively in the industry
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25. 26
•Background, Business case
•Get insights
•Prepare for DOE (design of experiments)
•Design and run experimentation
•Analyze results
•Confirmatory runs, conclusions, contacts, background of founder q-Maxim

26. 27
Prepare DOE (design of experiments) – DOE some background info.
•A statistical methodology
•Not a theoretical exercise, experiments are run on the shop floor like any production run

27. 28
Prepare DOE (design of experiments) – DOE some background info.
•Highlights - steps
1.Select some factors & interactions for which knowledge has to be gained
2.Run experiments by varying all chosen factors at simultaneously unlike traditional experimentation methods in which experimentation is run by varying one factor at a time keeping others constant.
3.Run experiments as per prefixed experimental plans
4.Measure responses
5.Analyze results using specialized computer program like Minitab
6.Confirm findings

28. 29
Prepare DOE – DOE some background info. -advantages
•less expensive –fewer tests
•More sure of results, results have statistical validity
•Can estimate interactions
•Forms one of the main constituents of Six Sigma
•Number of variables (factors) to test has to be reduced to a manageble number considering insights gained during earlier stage. (Not reducing the number of factors results in unacceptably large number of experiements)

29. Prepare for DOE- DOE some background info.
•Before doing DOE, checked / ensured adequecy of measurement system ( MSA done )
•There are several types of DOE.
•Taguchi DOE chosen, which is one the most popular.
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30. Prepare for DOE- Who is Taguchi?
•Dr. in Engineering and Statistics, renowned Quality Guru – Deming award 3 times, Blue Ribbon Award from the Emperor of Japan ,Stewart Medal from the ASQC
• Concepts: Quality Engineering , Robust design, parameter design, Taguchi DOE
• Developed a earlier method for doing DOE ( first developed by R.Fisher 1935)
•Used extensively in Japan since 1960s, became popular in US since 1980 and in India
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31. Prepare for DOE- Where is Taguchi DOE used?
•Anywhere- manufacturing, transaction process, non-manufacturing, software development, website optimization, food production, health care…….
•Applications: production, R+D, process optimization, improving yield, product design
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32. Prepare for DOE- Where is Taguchi DOE used?
•Some application examples:
–Improving yield in Aluminium pressure die casting
–Improving yield in sand casting foundry
–Reducing the defects in welding process & choosing optimum parameters
–Reducing wait time in banking
–Improving the hospital discharge-process
–Software testing – improving coding errors
–Website design -deciding on optimum design
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33. Prepare for DOE- Taguchi design goals
•Primary Goal :
1. Find the effect of chosen factors & interactions on responses.
2.Find settings of factors that minimize responses while adjusting or keeping the process on target.
•Secondary goal
1. Find setting of factors to maximize the S/N (signal to noise ) ratio. A product designed with this goal will deliver more consistent performance regardless of the environment in which it is used.
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34. Prepare for DOE – Objectives of this project
•Reduce number of factors for testing & number of interactions to a manageble number - 4 chosen
•Four factors (called control factors) & some interactions chosen for experimentation:
1.Gating design
2.Pouring height
3.Melt temperature
4.Delay between mould prepartion and casting (other variables held constant in a certain range, or simply monitored)
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35. Prepare for DOE – Objectives of this project (cont.)
•Responses:
1.Count number of defects of each type in all castings
2.Measure total weight of Welding electrode consumed for repair
•Objective is to minimise following response variables by 50% :
1.Numbers of defects of each type
2.Overall weight of Welding electrodes consumed for repair
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36. 37
•Background, Business case
•Get insights
•Prepare for DOE
•Design and run experimentation
•Analyze results
•Confirmatory runs, conclusions, contacts, background of founder q-Maxim

37. Design and run experiments – DOE flow chart- schematic representation
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Gating design
Pouring height
Melt temp
Delay between prep. & casting
Factors chosen
Other factors held constant/ monitored: e.g. mould preparation method, mould coatings, furnace content, furnace treatment, ambient temp. , relative humidity
Design chosen
Experimental design
Experiments run
Response & other data collected
Data analysed
Program output: response table and charts (mean & S/N)
Conclusions
1.Factors of significance
2.Interactions of significance
3.Optimum factor levels

38. Design and run experiments – choose an appropriate experimental design
•A prefixed Taguchi experimental design [Taguchi array] chosen, choice of design depends on:
–Number of control factors
–Number of levels of each control factor
–Number of Interactions to be known between control factors
–Number of replicates
–Other considerations – such as confoundings
•Responses are measured at selected combinations of the control factor levels
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39. Design and run experiments – choose an appropriate experimental design (cont.)
•Levels of Four control factors (two levels) chosen for experimentation
1.Gating design (old, new)
2.Pouring height (normal-present, low)
3.Melt temperature (procedure, low)
4.Delay between mould prepartion and casting (low, high) Others variables held constant in a certain range
[see next slide for the experiemental design]
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40. Design and run experiments - Experimental plan
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DOE_Run_ NoA mould designB ladle_tempC pour_ htD dealy_ cst-mld1oldprocedurepresent low 2oldprocedurelow high 3newlow present low 4newlow low high 5oldlow present high 6oldlow low low 7newprocedurepresent high 8newprocedurelow low

41. Design and run experiments – Do experimentation
•Production done as usual but controlling factor levels as specified in the experiemental plan- each combination called Run
•Each run repeated twice, operating conditions noted down in a specially designed form
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42. Design and run experiments – Do experimentation
•Responses and tabulation
1.During first stage of inspection count the total number of each type of defect in all castings
2.Weight of Welding electrode consumed for repair of all the castings for each run
[Schematic picture of all the steps in DOE is later in the presentation]
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43. 44
•Background, Business case
•Get insights
•Prepare for DOE
•Design and run experimentation
•Analyze results
•Confirmatory runs, conclusions, contacts, background of founder q-Maxim

44. Analyze results – data analysis
•Response data for each run fed into specilaised Statistical program- Minitab
•Program output in the form of response table and charts (Response of means and S/N ratio)
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45. Analyze results – data analysis
•By studying the output we can see:
1.To what extent each factor has influence
2.which levels of the factors are better
•Typical outputs in the next 3 slides [only information on for sand mark type of defect is shown in this presentation]
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46. Analyze results – program output – response table
Sand inclusion defect analysis:
•Response Table for Signal to Noise Ratios
Level A gating B ladle_temp C pour_ ht Delay cst-mld
1 -13.088 -9.505 -10.193 -12.112
2 -7.185 -10.768 -10.080 -8.161
Delta 5.903 1.263 0.114 3.952
Rank 1 3 4 2
•Response Table for Means
Level A gating B ladle_temp C pour_ ht D Delay cst-mld
1 3.792 2.875 3.042 3.292
2 2.000 2.917 2.750 2.500
Delta 1.792 0.042 0.292 0.792
Rank 1 4 3 2
Conclusion: Gating design has the most influence & pouring height has least influence on generation of sand mark type defects

47. Analyze results –
program output- sand mark type of defects
old new
4.0
3.5
3.0
2.5
2.0
proc low
present low
4.0
3.5
3.0
2.5
2.0
low high
A gating
Mean of Means
B ladle_temp
C pour_ ht D dealy_ cst-mld
Main Effects Plot for Means
Data Means
This level is
better
This level is
better

48. DOE runs findings
old new
-8
-10
-12
proc low
present low
-8
-10
-12
low high
A gating
Mean of SN ratios
B ladle_temp
C pour_ ht D dealy_ cst-mld
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
This level is
better
This
level is
better
Analyze results –
typical program output- sand type of defects

49. Analyze results – Conclusions - sand mark type defects
Influential factors:
1.Gating design has the highest effect
2.Delay between mould preparation and casting has the 2nd largest influence.
3.Others factors have minimal effect

50. Analyze results – Conclusions - sand mark type defects
•Most desirable Level of influential factors
1.Gating design: New design is better
2.Delay between mould preparation and casting : longer delay is better
3.Levels of other two factors are not important

51. Analyze results – Findings were confirmed by using machine learning
•A statistical model was built using machine learning algorithm of Artificial neural networks
•Simulation was done, data generated, and 3d plot was made with response (i.e. number of sand type defects ) in z axis & important features in x & y axis
[see next 2 slides]

52. DOE results 3d simulations Number of defects Vs design + delay
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This confirms previous finding from DOE means charts
Best operating range

53. DOE results 3d simulation Number of defects Vs design Vs ladle temp
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This confirms previous finding from DOE means charts

54. 56
•Background, Business case
•Get insights
•Prepare for DOE
•Design and run experimentation
•Analyze results
•Confirmatory runs, conclusions, contacts, background of founder q-Maxim

55. Confirmatory runs, conclusions- Confirmatory runs
•It is necessary to confirm findings by repeating the runs under most desirable levels of influential factors & compare the responses with baseline levels
•Responses are compared by
1. Box plot and
2. statistical tests
[Comparison shown in next two slides]

56. Confirmatory runs, conclusion
Sand mark type defect results / comparison with base line
Sand_after sand_before
20
15
10
5
0
count of defects
Boxplot of Sand_after (confirmatory), Sand_before
Two-sample T for Sand_after
vs sand_before
N Mean StDev
SE Mean
Sand_after 28 3.07 2.68
0.51
sand_before 17 7.47 4.08
0.99
Difference = mu (Sand_after)
- mu (sand_before)
Estimate for difference: -
4.40
95% upper bound for
difference: -2.50
T-Test of difference = 0 (vs
<): T-Value = -3.96 P-Value
= 0.000 DF = 24
Mean Defect
level is
about 60%
better than
baseline levels

57. Two-sample T for Ele_consumed_after
vs Ele_cons_before
N Mean StDev
SE Mean
Ele_consumed_after 28 22.8 24.1
4.6
Ele_cons_before 6 74.9 56.6
23
Difference = mu
(Ele_consumed_after) - mu
(Ele_cons_before)
Estimate for difference: -52.1
95% upper bound for difference: -
4.6
T-Test of difference = 0 (vs <): T-Value
= -2.21 P-Value = 0.039 DF
= 5
Ele_consumed_after Ele_cons_before
160
140
120
100
80
60
40
20
0
Net Wt of electrode used (gm)
Boxplot of Electrode consumed_after (confirmatory runs), Ele_cons_before
Confirmatory runs, conclusion
Electrode consumed for repair -comparison with baseline
Mean
Electrode
consumed for
repair is
about 70 %
less than
baseline

58. Box plot comparison of sand defects before project and after project for 1 year period significant benefits seen in 1 year results also
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before
after

59. •Reponses (means) comparison:
–Sand mark type defect : using optimum operating conditions as determined by this project lead to 59% lower level of defects as compared to baseline operating conditions
–Similar benefits seen in 1 year operating results also
Confirmatory runs, conclusion overall conclusion, benefits

60. •Reponses (means) comparison:
–Welding rod consumed for repair : using optimum operating conditions as determined by this project lead to about 70% less welding electrode consumption for defect repair as compared to baseline operating conditions
Confirmatory runs, conclusion overall conclusion, benefits

61. •Findings are statistically valid
•Implementation lead to significant cost savings – rework effort, time, money
•Meets project objectives consistently
•Knowledge gained will be extended to the other designs of same family of products
Confirmatory runs, conclusion overall conclusion, benefits

62. CONTACT US FOR DETAILS (DETAILS NEXT SLIDE)
QUESTIONS?
DOUBTS?
WHAT NEXT?
WOULD YOU LIKE TO DISCUSS FURTHER TO DO SIMILAR TRANSFORMATIONAL PROJECT?
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63. THANKS! QUESTIONS?
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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

64. Jagadish C.A. ‘s Profile
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LinkedIn profile: http://goo.gl/Lp3lWv
•B.Tech (Metallurgical Engg., NIT-K,Surathkal, India)
•Done many graded online courses (4-15 weeks) from leading US universities on operations management, advanced data analysis, marketing, finance, accounting, strategy, advanced competitive strategy, data scientist (5 courses), credit risk management, game theory
• ASQ (American society for quality) certified Six sigma Black belt (since 2002)
• ASQ certified Manager of Org. Excellence/ Quality (since 1999)
•Certified EFQM assessor extensive experience assessing companies, fashioning transformational roadmap & implementing EFQM model
•Juran QI facilitator, Cert. adv. Industrial experimentation, Analytics
• ISO 9001:2008,14001,QS-9000 (TS 16949) lead auditor
•~32 years experience in Manufacturing and transactional fields, 7.5 years as management consultant
•Rich experience in Quality, process management, R +D, Technology, Cost reduction, Management consultancy. Extensive experience in using advanced methodologies for problem solving and optimization
•Extensive exposure/ knowledge of heavy process oriented manufacturing – Aluminium cast house, Steel, Welding, casting, foundry, Metallurgy, etc
•Widely travelled – India, north America, Europe, M.E.