Dynamic Optimum Method of Control Charts for Small Batch Manufacturing Based on Stochastic Weighted Theory

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Abstract:

For the quality control of developed small batch manufacturing mode, a method based on stochastic weighted theory is proposed to optimize control parameters of control charts. The small sample data is sampled again by stochastic weighted method to obtain more information. Then, the sample data’s distribution parameters are estimated. After the estimated distribution parameter are tested, control parameters of control charts could be optimized dynamically. The method improves deficiencies of traditional control charts under low-volume production and realizes the quality control of small batch manufacturing. Experiments show that the optimized control parameters approach theory true values and are better than the traditional control charts’ control parameters. Therefore, the proposed method is fit for the quality control of small batch manufacturing.

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5556-5560

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September 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] YANG Yue-jin. Statistical Quality Control Technology (Aeronautics Industry Press, Beijing2003).

Google Scholar

[2] MIAO Rui, SUN Xiao-ming, LI Shu-gang, et al. Research on statistical process quality control based on low-volume manufacturing. Computer Integrated Manufacturing Systems, Vol. 11(2005), pp.1633-1635.

Google Scholar

[3] YANG Xu, MA Yu-lin, YANG Xiao-hui. Statistical quality control based on low-volume production. Computer Integrated Manufacturing Systems, Vol. 7(2001), pp.62-64.

Google Scholar

[4] YANG Xiao-hui, SONG Shi-long, GU De-yi. Statistical Quality Control Oriented to Flexible Manufacturing System. System Engineering Theory and Practice, Vol. 2(2003), pp.91-94.

Google Scholar

[5] HU Xing-cai, YE Wen-hua. Research on the statistical process control in small batch production. MECHANICAL Mechanical Research and Application, Vol. 19(2006), pp.36-38.

Google Scholar

[6] ZHANG Gong-xu. Two Quality Diagnosis Theory and Application. (Science Press, Beijing2001).

Google Scholar

[7] ZHU Hui-ming, HAN Yu-qi. Bayesian Statistical Quality Control Models Based on the Random Parameters. Journal of Nanjing University of Science and Technology, Vol. 28(2004), pp.445-448.

Google Scholar

[8] F. S. HILLIER. X and R chart control limits based on a small number of subgroups. Journal of Quality Technology, Vol. 1(1969), pp.17-26.

Google Scholar

[9] C.P. QUESENBERY. SPC Q charts for start-up processes and short or long runs. Journal of Quality Technology, Vol. 23(1991), pp.213-224.

DOI: 10.1080/00224065.1991.11979327

Google Scholar

[10] ZHENG Guo-zhong. Stochastic Weighted Method. Acta Mathematicae Applicate Sinica, Vol. 10(1987), pp.247-253.

Google Scholar