Adaptive and Dynamic Process Planning Using Neural Networks
نویسندگان
چکیده
Recently, discrete part manufacturing systems have encountered more impending requests for adaptability and flexibility in order to efficiently survive the increasing dynamics of manufacturing environment. Although featurebased computer-aided process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prohibit the shop floor controller from rapidly coping with dynamic shop floor status such as unexpected production errors, rush order. The objective of the paper is to address a conceptual framework of the adaptive and dynamic process planning system that can rapidly and dynamically generate the needed process plans based on current shop status information. In particular, the construction procedures of the dynamic process planning models are suggested. At off-line are the dynamic planning models constructed as neural network forms, and then embedded into each removal feature in the process plan. The shop floor controller will execute the dynamic planning model to determine cutting parameters, tool paths, NC codes just before the associated removal feature is machined. Owing to the dynamic planning model, the shop floor controller will increase flexibility and robustness in unexpected situations occurring.
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تاریخ انتشار 1999