Synthetic neural networks for process control
Computers and Industrial Engineering
A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
A neural network-based procedure for the monitoring of exponential mean
Computers and Industrial Engineering
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
A multivariate synthetic double sampling T2 control chart
Computers and Industrial Engineering
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The need for multivariate statistical process control (MSPC) becomes more important as several variables should be monitored simultaneously. MSPC is implemented using a variety of techniques including neural networks (NNs). NNs have excellent noise tolerance in real time, requiring no hypothesis on statistical distribution of monitored processes. This feature makes NNs promising tools used for monitoring process changes. However, major NNs applied in SPC are based on supervised learning, which limits their wide applications. In the paper, a Self-Organizing Map (SOM)-based process monitoring approach is proposed for enhancing the monitoring of manufacturing processes. It is capable to provide a comprehensible and quantitative assessment value for current process state, which is achieved by the Minimum Quantization Error (MQE) calculation. Based on these MQE values over time series, an MQE chart is developed for monitoring process changes. The performance of MQE chart is analyzed in a bivariate process under the assumption that the predictable abnormal patterns are not available. The performance of MQE is further evaluated in a semiconductor batch manufacturing process. The experimental results indicate that MQE charts can become an effective monitoring and analysis tool for MSPC.