Research Initiation: Neural Networks and Fuzzy Control Application for Quality Engineering in Short Run Manufacturing Environments

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9410613 Chang This research is focused on the development and use of a fuzzy controller to address the problem of process design and control in a short run production environment. The controller will provide recommendations on the choice of machine operating parameter in a given production instance in order to produce a part of acceptable quality. Computer simulation will be used to test the controller in the initial phase of the research. Subsequently, data generated from other currently existing models for process control purposes will be used to test and fine-tune the performance of the controller. Upon the successful development of the controller, sample parts will be designed and processed on computer numerical control (CNC) machine to simulate a short run production environment to further test the controller. A second objective is to develop a process monitoring approach that uses neural network control chart (NNCC) to integrate observations from quality characteristic and process related information to determine process status. The outcome of the research has some potential for industrial application to improve process quality and process parameter selection for short-run production situation. The integration of process control and process design provides opportunity for on-line process monitoring for process data collection with the objective of improving product quality through reduction in process operational variation.