论文信息 - Pattern identification in statistical process control using fuzzy neural networks

Pattern identification in statistical process control using fuzzy neural networks

Today customers are demanding more diversified products, with higher quality and shorter delivery times. It has led to the adoption of flexible manufacturing systems (FMS). The quality control of the manufacturing process in FMS is a critical factor, requiring flexible and intelligent quality control systems. Quality control windows (QCW) is an adequate framework for development of automated quality control systems. QCW is formed by five steps: observation, evaluation, diagnostic, decision and implementation. The most important step is evaluation, where quality control charts indicate out of control situations and possible underlying causes. This work studied the performance of three fuzzy neural networks (radial basis functions network, RBF, fuzzy artmap, and a new network, RBF fuzzy-artmap) in the identification of six different "patterns" in quality control charts. The new RBF fuzzy-artmap network presented the best performance of classification (78.8%), while allowing on-line incremental learning.

G. Tontini

[1] N F Hubele,et al. X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING , 1993 .

[2] James M. Lucas,et al. The Design and Use of V-Mask Control Schemes , 1976 .

[3] Richard E. DeVor,et al. A methodology for integrated quality systems , 1987 .

[4] Mohamad T. Musavi,et al. On the training of radial basis function classifiers , 1992, Neural Networks.

[5] Kevin J. Dooley,et al. Identification of change structure in statistical process control , 1992 .

[6] A. R. Crathorne,et al. Economic Control of Quality of Manufactured Product. , 1933 .

[7] Norma Faris Hubele,et al. Back-propagation pattern recognizers for X¯ control charts: methodology and performance , 1993 .

[8] Charles W. Champ,et al. Exact results for shewhart control charts with supplementary runs rules , 1987 .

[9] Chuen-Tsai Sun,et al. Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[10] Derek A. Linkens,et al. Learning control using fuzzified self-organizing radial basis function network , 1993, IEEE Trans. Fuzzy Syst..

[11] S. Grossberg,et al. Fuzzy ART: an adaptive resonance algorithm for rapid, stable classification of analog patterns , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.