Intelligent Method for Process Quality Diagnosis and Improvement

Abstract:

An intelligent quality diagnosis method for process quality diagnosis and improvement was proposed to find out influencing input parameters for output quality and then provide suggestion for quality engineering to adjust them to acquire high quality performance. The diagnosis method extends the traditional quality control and diagnosis method that only for the output quality of manufacturing process. It can detect the input parameters of the manufacturing process and provide sensitivities of input parameter for adjustment. BN-MTY method was applied to explain the reason of quality failure in T² control chart and the root output quality indicators that aroused the process quality anomaly was located. The integrated method of neural network and sensitivity analysis was applied to get the weight and threshold value of neural cell in the forecasting network. his integrated quality diagnosis method can diagnose the input parameters and provide accurate sensitivities for quality improvement.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1332-1337
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1332
• Online since: July 2013
• Authors: Wen Jie Xu, Jin Yao
• Keywords: Control Chart, Neural Network (NN), Process Quality, Quality Diagnosis, Quality Improvement

References

[1] M. A. Shams, H. M. Budman, T. A. Duever, Fault detection, identification and diagnosis using CUSUM based PCA, Chemical Engineering Science, 2011, 66(20): 4488-4498.

DOI: 10.1016/j.ces.2011.05.028

[2] M. Castellani. Evolutionary Artificial Neural Network Design and Training for wood veneer classification, Engineering Applications of Artificial Intelligence, 2009, 22(4-5): 732-741.

DOI: 10.1016/j.engappai.2009.01.013

[3] L. H. Jiang, A. G. Wang, N. Y. Tian, et al. BP Neural Network of Continuous Casting Technological Parameters and Secondary Dendrite Arm Spacing of Spring Steel, Journal of Iron and Steel Research, International, 2011, 18(8): 25-29.

DOI: 10.1016/s1006-706x(11)60099-x

[4] W. Hachicha, A. Ghorbel. A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme, Computers & Industrial Engineering, 2012, 63(1): 204-222.

DOI: 10.1016/j.cie.2012.03.002

[5] P. Castagliola, P. E. Maravelakis. A CUSUM control chart for monitoring the variance when parameters are estimated, Journal of Statistical Planning and Inference, 2011, 141(4): 1463-1478.

DOI: 10.1016/j.jspi.2010.10.013

[6] C. H. Chen, J. K. Yin, J. M. Li, et al. Quality Control Method of Manufacturing Processes with Nestedness and Auto-Correlation, Journal of University of Electronic Science and Technology of China, 2012, 41(6): 954-959. ].

[7] R. Miao, X. Y. Zhang, D. Yang, et al. A conjugate bayesian approach for calculating process capability indices, Expert Systems with Applications, 2011, 38(7): 8099-8104.

DOI: 10.1016/j.eswa.2010.12.151

[8] A. Adel, D. Ibrahim. Using Bayesian networks for root cause analysis in statistical process contro, Expert Systems with Applications, 2011, 38(9): 11230-11243.

DOI: 10.1016/j.eswa.2011.02.171

[9] H M. Zhu, Y Q. Han, Bayesian multivariate statistical inference theory, Beijing: Science Press, (2006).

[10] W. K. Wong, C. W. Yuen, D. D. Fan, et al. Stitching defect detection and classification using wavelet transform and BP neural network, Expert Systems with Applications, 2009, 36(2): 3845-3856.

DOI: 10.1016/j.eswa.2008.02.066