Implementing Mahalanobis-Taguchi system to improve casting quality in grey iron foundry

International Journal of Productivity and Quality Management

Volumn 3, Issue 4, 2008, Pages 444-456

  Khanzode, Vivek V ^a   Maiti, J ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Foundry process control; Healthy observations; Mahalanobis Taguchi System; MTS; Orthogonal array; Unhealthy observations

Indexed keywords

EID: 49249113461     PISSN: 17466474     EISSN: 17466482     Source Type: Journal    
DOI: 10.1504/IJPQM.2008.019760     Document Type: Article

References (17)

1. Amasaka, K. (2000) 'A demonstrative study of a new SQC - concept and procedure in the manufacturing industry', Mathematical and Computer Modelling, Vol. 31, No. 10, pp. 1-10.
2. Bodecchi, L.M., Cocchi, M., Malagoli, M., Manfredini, M. and Marchetti, A. (2005) 'Application of infrared spectroscopy and multivariate quality control methods in PVC manufacturing', Analytica Chimica Acta, Vol. 554, Nos. 1-2, pp. 207-217.
3. 2 control chart for skewed populations using weighted standard deviations', Quality and Reliability Engineering International, Vol. 20, No. 1, pp. 31-46.
4. Khanzode, V.V. and Maiti, J. (2007) 'Improving foundry process control: an investigation of cluster analysis and path model', International Journal of Productivity and Quality Management, Vol. 2, No. 4, pp. 404-422.
5. Lee, Y.H., Min, K.G., Han, C., Chang, K.S. and Tae Hwa Choi, T.H. (2004) 'Process improvement methodology based on multivariate statistical analysis methods', Control Engineering Practice, Vol. 12, No. 8, pp. 945-961.
6. Miletic, I., Quinn, S., Dudzic, N., Vaculik, V. and Champagne, M. (2004) 'An industrial perspective on implementing on-line applications of multivariate statistics', Journal of Process Control, Vol. 14, No. 8, pp. 821-836.
7. Montgomery, D.C. (2004) Introduction to Statistical Quality Control 4th edition, John Wiley and Sons Inc.
8. Mukhopadhyay, A. and Iqbal, A. (2005) 'Prediction of mechanical properties of hot rolled, low carbon steel strips using artificial neural network', Materials and Manufacturing Processes, Vol. 20, No. 5, pp. 793-812.
9. Niaki, S.T.A. and Abbasi, B. (2005) 'Fault diagnosis in multivariate control charts using artificial neural networks', Quality and Reliability Engineering International, Vol. 21, No. 8, pp. 825-840.
10. Prajapati, D. and Mahapatra, P. (2007) 'An effective joint X-bar and R chart to monitor the process mean and variance', International Journal of Productivity and Quality Management, Vol. 2, No. 4, pp. 459-474.
11. Reis, M.S. and Saraiva, P.M. (2006) 'Heteroscedastic latent variable modelling with applications to multivariate statistical process control', Chemometrics and Intelligent Laboratory Systems, Vol. 80, No. 1, pp. 57-66.
12. Ribeiro, L.M.M. and Cabral, J.A.S. (1999) 'The use and misuse of statistical tools', Journal of Materials Processing Technology, Vols. 92-93, pp. 288-292.
13. Ross, P.J. (1996) Taguchi Techniques for Quality Engineering, 2nd edition, New York: McGraw Hill.
14. Saniga, E., McWilliams, T., Davis, D. and Lucas, J. (2006) 'Economic control chart policies for monitoring variables', International Journal of Productivity and Quality Management, Vol. 1, Nos. 1/2, pp. 116-138.
15. Skoglund, A., Brundin, A. and Mandenius, C.F. (2004) 'Monitoring a paperboard machine using multivariate statistical process control', Chemometrics and Intelligent Laboratory Systems, Vol. 73, No. 1, pp. 3-6.
16. Sukthomya W. and Tannock, J.D.T. (2005) 'Taguchi experimental design for manufacturing process optimization using historical data and a neural network process model', International Journal of Quality and Reliability Management, Vol. 22, No. 5, pp. 485-502.
17. Tracy, N.D., Young, J.C. and Mason, R.L. (1992) 'Multivariate control charts for individual observations', Journal of Quality Technology Vol. 24, No. 2, pp. 88-95.