Method of Identifying Key Quality Characteristics in Multistage Manufacturing Process Based on PLSR

Abstract:

To improve the existing methods of identifying the key quality characteristics in multistage manufacturing process, the partial least squares regression (PLSR) method is combined with the state space model that a new method of identifying the key quality characteristics in multistage manufacturing process based on PLSR is proposed. According to the feature of multistage manufacturing process, the state space model is introduced to build the key quality characteristics identifying model for multistage manufacturing process, using the PLSR method to solve the problem of the quality characteristics such as multicollinearity, do model analyzing and identify the key quality characteristics. At last, the cigarette production process is presented as an example to introduce the application of this method. The result shows that this method can not only identify the key quality characteristics in multistage manufacturing process, but also establish the model of output quality effecting of all levels on the final product quality and its quality characteristics relationship, which reflect the structure of the multistage manufacturing process and causal relationship between quality characteristics at all process levels, provide the basis for quality analysis and control in multistage manufacturing process.

• Periodical: Applied Mechanics and Materials (Volumes 217-219)
• Pages: 2580-2584
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.217-219.2580
• Online since: November 2012
• Authors: Ning Wang, Ji Chao Xu, Jian Feng Yang
• Keywords: Key Quality Characteristics, Multistage Manufacturing Process, PLSR, State Space Model

References

[1] Wold S，Martens H，Wold H.The multivariate calibration problem in chemistry solved by the PLS method[J].Ruhe A，Kagstrom B（EDs），Proc.Conf.Matrix Pencils，Lectures Notes in Mathematics.Heidelberg：Springer- Verlag.1983.

DOI: 10.1007/bfb0062108

[2] SIMPA- P for Windows：Multivariate Modeling，Analysis and SPC of Process Data.Suede：UMETRI AB，Box 7960，S- 907 19 Umea，1996.

[3] Ding, Y., Ceglarek, D. and Shi, J. (2002a) Fault diagnosis of multistage manufacturing processes by using state space approach[j]. ASME Transactions, Journal of Manufacturing Science and Engineering,124(2), 313–322.

DOI: 10.1115/1.1445155

[4] Djurdjanovic, D. and Ni, J. (2007) On-line stochastic control of dimensional quality in multi-station manufacturing systems[j]. Journal of Engineering Manufacture, 221(B5), 865–880.

[5] Zhou, S., Ding, Y., Chen Y. and Shi, J. (2003b) Diagnosability study of multi-station manufacturing processes based on linearmixed model. Technometrics, 45(4), 312–325.

DOI: 10.1198/004017003000000131

[6] Loose, J., Zhou, S., Ceglarek, D. Variation source identification in manufacturing processes based on relation measurements of key product characteristics [J]. Journal of Manufacturing Science and Engineering, 2008, 130(3):1-11.

DOI: 10.1115/1.2844591