Visualization and Use of machining know-how for Cutting Process Planning

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Since expert skills play important roles in metal cutting, it is necessary to use the knowledge of experts to establish highly automated machining systems. Expert systems based on the quantitative knowledge, however, often does not work well because there are many conflicting knowledge of machining, which would not be resolved easily.In this study cutting forces and cutting temperature are assumed as the solutions of welldefined problems and their visualization are used in order to clarify the know-how of experts and determine cutting conditions rationally.The visualization systems are constructed in a EWS and personal computers. This would enable many tool engineers and workers to experience how the cutting temperature changes with cutting conditions. Tool wear is further predicted using cutting temperature and cutting forces and a wear characteristic equation, which is revised through a newly proposed concept of adaptive prediction.Machining accuracy, surface finish and grooving wear, which are not able to be predicted analytically, are obtained through neural networks in this study. Data obtained by the simulations are used as reliable input of neural networks.A case study for finding the optimum cutting conditions under the constraints of maximum flank wear and the maximum surface finish are examined.