Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters

Traditional statistical process control (SPC) charting techniques were developed for use in discrete industries where independence exists between process parameters over time. Process parameters from many manufacturing industries are not independent, however, but they are serially correlated. Consequently, the power of traditional SPC charts was greatly weakened. The paper discusses the development of neural network models to identify successfully shifts in the variance of correlated process parameters. These neural network models can be used to monitor manufacturing process parameters and signal when process adjustments are needed.

[1]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[2]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[3]  Jianjun Shi,et al.  The GLRT for statistical process control of autocorrelated processes , 1999 .

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  J. D. T. Tannock,et al.  A neural network approach to characterize pattern parameters in process control charts , 1999, J. Intell. Manuf..

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

[7]  Layth C. Alwan,et al.  Time-Series Modeling for Statistical Process Control , 1988 .

[8]  Alice E. Smith,et al.  X-bar and R control chart interpretation using neural computing , 1994 .

[9]  Chih-Chou Chiu,et al.  Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters , 1998 .

[10]  William E. Jones,et al.  Back-propagation, a generalized delta learning rule , 1987 .

[11]  T. Harris,et al.  Statistical process control procedures for correlated observations , 1991 .

[12]  Marion R. Reynolds,et al.  Control Charts for Monitoring the Mean and Variance of Autocorrelated Processes , 1999 .

[13]  Deborah F. Cook,et al.  A predictive neural network modelling system for manufacturing process parameters , 1992 .

[14]  Yi-Chih Hsieh,et al.  A neural network based model for abnormal pattern recognition of control charts , 1999 .

[15]  Shing I. Chang,et al.  An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study , 1999 .

[16]  Herbert Moskowitz,et al.  Control Charts in the Presence of Data Correlation , 1992 .

[17]  I. Dedeakayogullari,et al.  The determination of mean and/or variance shifts with artificial neural networks , 1999 .

[18]  A. Lapedes,et al.  Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .

[19]  G. Allen Pugh.  A COMPARISON OF NEURAL NETWORKS TO SPC CHARTS , 1991 .

[20]  Herbert Moskowitz,et al.  Run Length Distributions of Residual Control Charts for Autocorrelated Processes , 1994 .

[21]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[22]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .