The document discusses methods for discovering product-specific process knowledge to improve quality in the foundry industry. It proposes using statistical techniques like principal component analysis and penalty matrices on in-process data to determine optimal tolerance ranges for factors like percentage of carbon, iron, and aluminum. This helps reduce defective castings from 2-3% to below 1% by minimizing deviations from expected results. The methods also align with requirements for risk-based thinking and documenting organizational knowledge in the ISO 9001:2015 standard.

1. CIE45
International Conference on Computers & Industrial Engineering
28-30th October 2015, Metz / France
CIE45 Conference, 28-30 October 2015, Metz / France
Mr Raed S. Batbooti, Dr. Rajesh S. Ransing and Dr. Meghana R. Ransing1
College of Engineering, Swansea University, Swansea SA1 8EN, UK
1p-matrix Ltd, Swansea SA2 8PP, UK
HOW TO DISCOVER AND EMBED PRODUCT SPECIFIC PROCESS
KNOWLEDGE INLINE WITH ISO9001:2015 REQUIREMENTS

2. CIE45 Conference, 28-30 October 2015, Metz / France
-Foundry industry loses 2-3% of casting produced as defective components.
Problem Statement
-Lead to increase in the cost of productions.
-Produce tonnes of wastes.
Goal for In-process Quality Improvement
Reduce deviation from expected results in any process.
Optimise tolerance limits.

3. CIE45 Conference, 28-30 October 2015, Metz / France
-Foundry industry loses 2-3% of casting produced as defective components.
Problem Statement
Why?
Defects
-Lead to increase in the cost of productions.
-Produce tonnes of wastes.
Goal for In-process Quality Improvement
Reduce deviation from expected results in any process.
Optimise tolerance limits.

4. CIE45 Conference, 28-30 October 2015, Metz / France
-Foundry industry loses 2-3% of casting produced as defective components.
Problem Statement
Why?
Goal for In-process Quality Improvement
Reduce deviation from expected results in any process.
Optimise tolerance limits.
Defects
-Lead to increase in the cost of productions.
-Produce tonnes of wastes.
Variation in Factor in-process data
(Tolerance limits)
Variation in Response in-process data
(or Deviation from expected Results)

5. CIE45 Conference, 28-30 October 2015, Metz / France
Transfer the penalty onto factor
diagrams
Penalise Deviation from expected
Results (www.7epsilon.org)
The discovery of tolerance adjustment opportunity of %Carbon is in line with
the risk based thinking requirements (Clause 6.1) of ISO9001:2015
Knowledge discovery using quality
correlation algorithm[1]
0 Penalty Values
100 Penalty Values
[1] Ransing R. S. , Batbootia Raed S. , Giannettia C. , Ransing M.R. 2015. A quality correlation algorithm to reduce the occurrence of
No-Fault-Found defective batches, Computers & Industrial Engineering, under review.
0.11
0.105
0.1
0.095
0.09
% C
ISO 9001:2015, Risk Based Thinking and 7Epsilon

6. Product Specific Process Knowledge
Organizational knowledge for a given product is [2]
i. the actionable information
ii. in form of optimal list of measurable factors and their
ranges
(%C: 0.093-0.112, % Iron: 0.095-0.2,% Aluminium: 3.145-3.306)
iii. in order to meet desired business goals (process
responses)
(e.g. minimize defect rates, porosity scores or rework time
etc and/or maximize mechanical properties)
CIE45 Conference, 28-30 October 2015, Metz / France
[2] Giannetti, C., Ransing, R.S., Ransing, M.R., Bould, D.C., Gethin, D.T., Sienz, J. 2014. A novel variable selection approach based on
co-linearity index to discover optimal process settings by analysing mixed data, Computers & Industrial Engineering, 72 (217-229), pp.

7. Reusing organisational product specific process knowledge via
process FMEA tables (This step is inline with the clause 7.1.6
of ISO9001:2015)
CIE45 Conference, 28-30 October 2015, Metz / France
.%C in range (>0.09 & < 0.093) & (>0.112 & <0.12) has been identified
as a root cause for high incidence of % Shrinkage defect.
.A new tolerance limit is suggested > 0.093 & < 0.112 to prevent the
occurrence of the failure.
FMEA table
Incorrect
Percentage of C
% Shrinkage
% C in the range
(0.09-0.093) or
(0.112-0.12)
Keep % C in
the range
(0.093-0.112)

8. • Mathematical Formulation to discover product
specific process knowledge.
CIE45 Conference, 28-30 October 2015, Metz / France

9. CIE45 Conference, 28-30 October 2015, Metz / France
Root Case Analysis of Defects
- Co Linearity Index [3]
1-Data pre-treatment.
- Penalty matrices.
- Robust standardization
- MFA transformation
2- Apply PCA on covariance matrix resulted
before step (1).
3- Estimate the loading matrix based on the
following equation.
4- Evaluate the correlation matrix from
for p principal components, where the inner
product of ith and jth row vectors of Ls
represents the correlation between variable
i and j. After that co-linearity index can be
plotted by plotting angles and length of the
loading vectors.
X=
Response Factors
- PROVIDE NOISE FREE CORRELATIONS.
- % Co Correlated positively with response penalty.
Co-Linearity Index for Nickel based alloy
[3] Ransing, R.S., Giannetti, C., Ransing, M.R., James, M.W. 2013. A coupled penalty
matrix approach and principal component based co-linearity index technique to discover
product specific foundry process knowledge from in-process data in order to reduce
defects., Computers in Industry, 64 (5), pp. 514-523.

10. CIE45 Conference, 28-30 October 2015, Metz / France
Predicting Optimal Ranges
PCA Scores Projection
. This approach will help engineers to
refine current tolerance limits of process
factors to sustain the continual process
improvement effort.
.The present work give un constrained
ranges compared with penalty matrix,
which produce ranges constrained by
quartiles.

11. CIE45 Conference, 28-30 October 2015, Metz / France
Scores projection
on each variable
Corresponding
tolerance limit

12. Process
Parameter
Minimum
Value
Maximum
Value
Optimal Values
Niobium 0.656 0.893 >0.77 & < 0.865
Carbon 0.086 0.113 > 0.093 & < 0.112
Iron 0.057 0.2 >0.095 & < 0.2
Aluminium 3.059 3.306 > 3.145 & < 3.306
Zirconium 0.019 0.05 > 0.023 & < 0.05
Aluminium+
Titanium
6.204 6.527 > 6.299 & < 6.498
Tungsten 2.29 2.594 > 2.413 & < 2.594
Cobalt 7.714 8.028 < 7.714 & < 7.847
Or < 8.018 & < 8.02
CIE45 Conference, 28-30 October 2015, Metz / France

13. CIE45 Conference, 28-30 October 2015, Metz / France
7Epsilon for ISO9001:2015
[4] Roshan, H.M., Giannetti, C., Ransing, M.R., Ransing, R.S. 2014. “If only my foundry knew what it knows …”: A 7Epsilon perspective on root
cause analysis and corrective action plans for ISO9001:2008, Proceedings of the 71st World Foundry Congress, Bilbao, Spain 19th - 21st May 2014.

14. CIE45 Conference, 28-30 October 2015, Metz / France
Conclusion
. A new approach has been proposed to predict the optimal process settings for
correlated variables by using the analogue between loading and scores of principal
component analysis.
. This approach will help engineers to refine current tolerance limits of process
factors to sustain the continual process improvement effort.
.The present work give un constrained ranges compared with penalty matrix,
which produce ranges constrained by quartiles.
. Embeds ISO9001:2015’s Risk based thinking
. Modified process FMEA table helps compiling organisational knowledge.