A Neural Network Based Process Planning System to Infer Tool Path Pattern for Complicated Surface Machining
Mayu Hashimoto and Keiichi Nakamoto
Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan
Die and mold are necessary for the manufacture of present industrial products. In recent years, the requirement of high quality and low cost machining of complicated surfaces has increased. However, it is difficult to generalize process planning that depends on skillful experts and decreases the efficiency of preparation in die and mold machining. To overcome an issue that is difficult to generalize, it is well known that neural networks may have the ability to infer a valid value based on past case data. Therefore, this study aims at developing a neural network based process planning system to infer the required process parameters for complicated surface machining by using past machining information. The result of the conducted case studies demonstrates that the developed process planning system is helpful for determining the tool path pattern for complicated surface machining according to the implicit machining knowhow.
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