Generation and evolutionary learning of cutting conditions for milling operations

In metal cutting processes, cutting conditions have an influence on reducin g the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions. It is called GELCC (generation and evolutionary learning of cu tting conditions). GELCC is a key component of an operation planning system for milling operations. It performs the following three functions. 1. The modification of recommended cutting conditions obtained from a machi ning data handbook. 2. The incremental learning of obtained cutting conditions using fuzzy ARTM AP neural networks. 3. The substitution of better cutting conditions for those learned previous by a proposed replacement algorithm. Various simulations illustrate the performance of GELCC. and then the simul ation results for a given part are provided and discussed.