DECISION OF AN OPTIMUM CUTTING CONDITION BY NEURAL NETWORK
Optimal selection of cutting conditions contributes greatly to increased productivity and cost savings, but it is decided by workers‘ experience and subjective judgement in most machining companies. Herein, in order to reduce the dependence on those skilled workers, a neural network based optimum cutting condition decision method is suggested. Real machining data in the fields is used for the cutting condition optimization by using the life of cutting tools. The technology, economy and organization is considered for an optimization method. To satisfy the requirement of fields, unit production time is chosen as the object function. In order to reach a higher resonable result, real machining data is collected, and neural network is devepled and presented for the life estimation of cutting tools. Finally, the optimum cutting condition is recommended from a propsed method, and the results show that the approach is resonable in detetmining optimum cutting parameters.
HYUNCHUL KIM AND DUONG THANH HUNG