A predictive neural network modelling system for manufacturing process parameters

A methodology to predict the occurrence of out-of-control process conditions in a composite board manufacturing facility was developed using neural network theory. Multi-variable regression and time series analysis techniques were applied to analyse the data set for comparison and informational purposes. Regression models were developed to mode] specific process parameters and could account for only 25% of the variation in those parameters. When analysed as a time series, the data stream was non-stationary in the variance and transformations failed to achieve stationarity. Back-propagation neural networks were successfully trained to represent the process parameters. Inputs to the network consisted of data representing the current process condition along with historical data on relevant parameters, including temperature, moisture content, and bulk density. The training data set was graphically analysed to demonstrate the type of response surface successfully modelled. The trained neural networks were able...

[1]  Terrence J. Sejnowski,et al.  NETtalk: a parallel network that learns to read aloud , 1988 .

[2]  H. Newton,et al.  TIMESLAB: A Times Series Analysis Laboratory , 1989 .

[3]  Halbert White,et al.  Neural-network learning and statistics , 1989 .

[4]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

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

[6]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[7]  Deborah F. Cook,et al.  A sensitivity analysis of a back-propagation neural network for manufacturing process parameters , 1991, J. Intell. Manuf..

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

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

[10]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

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

[12]  Niels Konrad Bauer,et al.  Parallel distributed processing models using the back-propagation rule for studying analytic and holistic modes of processing in category learning , 1988 .

[13]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[14]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[15]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[16]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.