Refine current tolerance limits of process factors to sustain the continual process improvement...

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, UK1p-matrix Ltd, Swansea SA2 8PP, UK

HOW TO DISCOVER AND EMBED PRODUCT SPECIFIC PROCESS KNOWLEDGE INLINE WITH ISO9001:2015 REQUIREMENTS


-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 ImprovementReduce deviation from expected results in any process.

Optimise tolerance limits.



Why?

Defects







Why?



Defects



Variation in Factor in-process data

(Tolerance limits)

Variation in Response in-process data

(or Deviation from expected Results)


Transfer the penalty onto factor

diagramsPenalise 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

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)


[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.

Reusing organisational product specific process knowledge via process FMEA tables (This step is inline with the clause 7.1.6 of ISO9001:2015)


.%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)

• Mathematical Formulation to discover product specific process knowledge.



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.


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.


Scores projection on each variable

Corresponding tolerance limit

Product specific process knowledge

Process Parameter

Minimum Value

Maximum Value

Optimal Values

Niobium 0.656 0.893 >0.77 & < 0.865Carbon 0.086 0.113 > 0.093 & < 0.112Iron 0.057 0.2 >0.095 & < 0.2 Aluminium 3.059 3.306 > 3.145 & < 3.306Zirconium 0.019 0.05 > 0.023 & < 0.05Aluminium+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



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.


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.

Refine current tolerance limits of process factors to sustain the continual process improvement...

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