7Epsilon - World Foundry Congress 2014 presentation

Dr. HMd Roshan, Maynard Steel Casting, WI, USA
Cinzia Giannetti, Swansea University, UK
Dr. Meghana R. Ransing, p-matrix Ltd.
Dr. Rajesh S. Ransing, Swansea University, UK
A 7Epsilon Continual Process
Improvement Case Study for Defect
Reduction and Quality Control
(Official UK Exchange Paper)
71st World Foundry Congress. Bilbao 2014

 7Epsilon term coined by Dr. Patricia Caballero, Tecnalia Spain.
 7Steps of 7Epsilon to ERADICATE defects – introduced by
Dr. Rajesh Ransing, Swansea University, UK

Dr. Pedro Egizabal,
Tecnalia, Spain
Dr. HMd Roshan,
Maynard Steel, USA
Dr. Meghana Ransing
P-matrix Ltd, UK
Dr. Conny Gustavson,
Swerea Swecast, Sweden
Dr. Salem Saffeidine
Swerea Swecast, Sweden
Prof. Natalia Sobczak
Foundry Research Institute,
Poland
Mr. Sham Arjunwadkar
Institute of Indian Foundrymen,
India

Acknowledgements
p-matrix LtdMaynard Steel
Casting, WI, USA

 “If TI only knew what TI knows” – Jerry Junkins, the late
chairman, president and CEO of Texas Instruments
 Lew Platt, chairman of Hewlett –Packard echoed with
“I wish we knew what we know at HP”
“If only my foundry knew what it knows …”

 Challenges the global foundry industry faces today
 Maynard Steel Casting Case Study using 7steps of 7Epsilon
 Penalty Matrix Approach for rootcause analysis
 Conclusion

 ISO 9001:2008 or similar quality accreditation
 Problem solving & continuous improvement strategies in place
 E.g. Physics based simulations, best practice principles, process
stability, in-process data capture

 Assumption – Variability in ALL measurable factors is robust. It does not
influence process variation

 Assumption – Variability in ALL measurable factors is robust. It does not
influence process variation
 7Epsilon challenges this assumption
 discovers ranges of factors within current tolerance limits that can be
associated with process response variations

 It is a methodology of simultaneously tweaking single or multiple process
parameter settings to reduce the variation in response values.
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055
0 10 20 30 40 50 60 70
Observations No.
%Zirconium
Top 50%
Bottom 50%
Mean /
Median

 Maynard Steel Casting Foundry, Wisconsin, USA
 Low alloy steel foundry
 Continual process improvement in melting sub-process
 Discover product specific process knowledge
 Find new tolerance limits for melting parameters

 Requirement 0% fractured surface area with conchoidal nature
 Fracture tests failing in conchoidal fracture
 Rock candy fracture / intergranular fracture
 Chemistry within specification but considered to play a significant role in
incidence of conchoidal fracture
GOAL: achieve reduction of conchoidal fracture by optimizing chemistry
parameters in melting sub process

1. Form a project team
2. Acquire team members knowledge about
 processes, their factors, responses and causal relationships
3. Gather process knowledge codified using
 Process maps, SIPOC diagrams and cause and effect diagrams

 Process knowledge is
 The understanding that Y = f(Xs)
 How variability in Xs affects variability in Ys
 Foundries rely on experts for process knowledge
 Generic knowledge comes from experience, published literature
 Foundry knowledge needs to be systematically collected, recorded for
reuse
 Systematic research on process factors and how they affect response
with written descriptions

 Knowledge discovery
 in-process data routinely collected as part of ISO 9001:2008
implementation
 Perform rootcause analysis and discover correlations using penalty
matrix approach
 Prioritise patterns using p-matrix software

Response
(Y)
fract-
Surface
Carb
on
Drop
Tap
Temp
eratur
e,F
Pourin
g
Tempe
rature,
F
Arg
on
stir,
mts
%C %Mn %S %P %Si %Ni %Cr %Mo %Cu %Al %Ti
Mn/S
Ratio
%Zr %Ca
%Ca/%
Al
ratiox1
000
0 57 3039 2840 8 0.21 1.00 0.008 0.013 0.42 1.70 1.04 0.40 0.160 0.025 0.0009 125 0.0096 0.0012 48
0 62 2965 2830 3 0.21 1.07 0.008 0.011 0.54 1.61 1.17 0.42 0.170 0.033 0.0018 134 0.0166 0.0027 82
0 73 2971 2850 8 0.21 0.96 0.01 0.012 0.54 1.68 1.11 0.42 0.149 0.04 0.0013 96 0.0224 0.0023 58
3 33 2980 2850 4 0.2 0.94 0.007 0.01 0.51 1.76 1.05 0.45 0.147 0.043 0.0015 134 0.0201 0.0024 56
0 60 2955 2820 2 0.24 1.12 0.01 0.013 0.4 1.72 1.09 0.43 0.153 0.032 0.0010 112 0.0129 0.0029 91
5 84 2905 2836 2 0.19 1 0.01 0.01 0.48 1.71 1.04 0.4 0.135 0.041 0.0153 100 0.0029 0.0030 73
5 35 3007 2846 4 0.2 0.96 0.009 0.01 0.43 1.69 1.07 0.42 0.133 0.013 0.0094 107 0.0021 0.0006 46
0 50 2988 2858 4 0.2 1.06 0.011 0.013 0.5 1.63 1.12 0.4 0.182 0.02 0.0075 96 0.0017 0.0002 10
0 61 2960 2850 4 0.19 0.9 0.009 0.01 0.37 1.64 1.02 0.41 0.146 0.022 0.0102 100 0.0025 0.0007 32
5 64 2950 2852 4 0.18 0.97 0.01 0.009 0.48 1.63 1.06 0.41 0.179 0.026 0.0136 97 0.0037 0.0035 135
15 31 2948 2850 4 0.2 1.07 0.011 0.014 0.4 1.63 1.14 0.4 0.173 0.032 0.0135 97 0.0029 0.0038 119
3 42 2983 2860 8 0.2 0.95 0.013 0.015 0.41 1.67 1.05 0.41 0.150 0.035 0.0011 73 0.0125 0.0125 357
10 51 2915 2840 3 0.23 1.07 0.01 0.013 0.38 1.65 1.11 0.4 0.139 0.028 0.0160 107 0.0027 0.0017 61
10 56 2942 2850 6 0.21 1.1 0.011 0.015 0.39 1.67 1.17 0.41 0.145 0.037 0.0134 100 0.0044 0.0024 65
5 48 2957 2860 2 0.23 0.97 0.01 0.014 0.42 1.63 1.09 0.41 0.117 0.033 0.0114 97 0.0030 0.0018 55
3 58 2990 2860 12 0.19 0.99 0.009 0.014 0.43 1.73 1.09 0.4 0.120 0.046 0.0159 110 0.0042 0.0054 117
0 20 2943 2818 4 0.24 0.95 0.008 0.011 0.6 1.64 1 0.41 0.136 0.03 0.0090 119 0.0029 0.0002 7
3 47 2966 2850 3 0.21 1.02 0.01 0.009 0.5 1.65 1.08 0.41 0.124 0.031 0.0110 102 0.0029 0.0014 45
20 45 2938 2850 4 0.21 1.01 0.013 0.013 0.5 1.63 1.08 0.41 0.119 0.049 0.0150 78 0.0036 0.0027 55
5 68 2994 2850 4 0.18 0.95 0.012 0.014 0.47 1.65 1.06 0.41 0.106 0.046 0.0149 79 0.0041 0.0022 48
0 53 2892 2832 2 0.21 0.95 0.011 0.011 0.52 1.63 0.98 0.42 0.140 0.046 0.0143 86 0.0046 0.0026 57
10 14 2978 2855 4 0.23 0.99 0.012 0.011 0.48 1.62 1.08 0.44 0.153 0.032 0.0121 83 0.0050 0.0015 47
0 72 2959 2832 4 0.19 0.97 0.009 0.009 0.35 1.64 0.97 0.43 0.171 0.041 0.0091 108 0.0022 0.0016 39
0 40 3019 2850 4 0.19 1.05 0.011 0.012 0.4 1.63 1.11 0.41 0.135 0.024 0.0055 95 0.0030 0.0007 29
5 38 2942 2861 4 0.18 1.01 0.011 0.011 0.46 1.68 1.04 0.4 0.144 0.033 0.0109 92 0.0033 0.0019 58
10 26 2925 2875 2 0.21 1 0.01 0.011 0.48 1.6 1.09 0.41 0.140 0.035 0.0134 100 0.0038 0.0036 103
15 47 3028 2865 2 0.18 0.93 0.011 0.015 0.44 1.64 1.06 0.4 0.160 0.027 0.0121 85 0.0029 0.0013 48
30 13 3034 2850 15 0.22 0.93 0.012 0.015 0.41 1.68 1.01 0.43 0.149 0.018 0.0058 78 0.0015 0.0008 44
5 40 2913 2850 2 0.17 0.93 0.009 0.015 0.42 1.66 1.01 0.41 0.145 0.039 0.0125 103 0.0032 0.0037 95
20 58 2940 2855 6 0.2 1.02 0.011 0.014 0.48 1.63 1.12 0.41 0.153 0.034 0.0142 93 0.0030 0.0023 68
0 30 3020 2850 6 0.19 1.04 0.01 0.013 0.46 1.6 1.04 0.41 0.146 0.037 0.0135 104 0.0044 0.0023 62
10 39 2993 2850 5 0.22 0.96 0.01 0.013 0.41 1.67 1.02 0.41 0.117 0.039 0.0153 96 0.0039 0.0045 115
5 49 2944 2850 3 0.18 1.03 0.013 0.013 0.39 1.69 1.01 0.42 0.164 0.026 0.0090 79 0.0029 0.0003 12
3 37 3045 2870 12 0.23 1.18 0.013 0.015 0.52 1.64 1.13 0.42 0.127 0.029 0.0097 91 0.0032 0.0032 110
5 46 2933 2850 6 0.19 0.92 0.012 0.011 0.46 1.68 1.08 0.42 0.110 0.033 0.0109 77 0.0034 0.0024 73
Factors
(X)

 Penalise variability in one or more process response values
 0% given 0 penalty
 10% and above given 100 penalty
 Linear scaling for intermediate values
100 Penalty Values
0 Penalty Values
Response Scatter Diagram Response Bubble Diagram

Main Effects Bubble DiagramMain Effects Scatter Diagram
Bottom 50%
 Bubbles with smaller diameter correspond to 0 penalty (optimal)
 Bubbles with bigger diameter correspond to 100 penalty (avoid)

Penalty Matrix
Main Effects Bubble Diagram

Interactions Bubble Diagram Penalty Matrix
Main Effects Bubble Diagram

 p-matrix analyses hundreds and thousands of penalty matrices among
factors
 Classifies factor settings as Optimal, Avoid or No Effect
 Ranks them in order of importance
 At end of analysis, a process engineer would get a list of top 15-20
matrices he/she needs to look at
 Analyses discrete and continuous parameters together in one analysis
 Up to 200 factors and 40 responses can be analysed at once.

 Findings need to be used as pointers for discussion
 p-matrix discovers correlations based on data collected
 Correlation does not mean Causation
 Domain knowledge necessary to interpret results
 Hidden causes may be found which require further investigation

 Knowledge Discovery and Reuse
 Analyse p-matrix reports
 Hypotheses on causation are established using knowledge acquired
in Step 2
 Hypotheses are potential solutions
 New tolerance limits proposed and
 corrective action plan is outlined or collect more in-process data
or conduct one or more design of experiments

 Confirmation trials are carried out to validate the hypotheses and create
new product specific process knowledge
 Optimal ranges for all the process variables (X) are determined
 New product specific process knowledge is created in the form of
 list of values with their new specification ranges

Sub-Process
Process
Variable (CTQ)
New tolerance
limits
Frequency of
data collection
Melting and
Pouring
Carbon Drop (X1) 47-84 Every Heat
Melting and
Pouring
% Sulfur (X7) 0.007-0.009 Every Heat
Melting and
Pouring
%Titanium (X15) 0.0009-0.011 Every Heat
Melting and
Pouring
Mn/S Ratio (X16) 104-134 Every Heat
Melting and
Pouring
%Ca/%Al
Ratiox1000 (X19)
6.67-57.5 Every Heat

 New knowledge obtained stored in knowledge repository in tabular
form
 This is specific for a given part and process
 The new knowledge acquired contributes to devise preventive and
corrective action plans to achieve desired response as required by
ISO 9001:2008 standard

 Continually monitor performance so that continual improvement on the
processes can be made to meet the requirement of ISO 9001:2008.
 The foundry specific 7Epsilon process knowledge repository can also be
used to train operators and process engineers.
7 Epsilon Knowledge
repository

 7 Epsilon steps
 Tolerance limit optimisation to reduce defects
 Penalty matrix visualization to verify evidence in data
 Establish causation using domain knowledge
 Design corrective actions plan
 Validate by implementing on shop floor
 Update process knowledge, retain and reuse

 Visit the 7Epsilon website at www.7epsilon.org
 One day training courses hosted by
 Institute of Cast Metals Engineers, ICME, UK on 16th September
2014
 On Demand Online course with American Foundrymen Society, AFS - by
Dr. HMd Roshan, course instructor
 Join Webinar with Dr. Meghana Ransing, p-matrix Ltd.
 Write to info@p-matrix.com to book your next course

7Epsilon - World Foundry Congress 2014 presentation

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