In metal cutting processes, cutting conditions have an influence on reducing 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 cutting 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 machining data handbook.
2. The incremental learning of obtained cutting conditions using fuzzy ARTMAP neural networks.
3. The substitution of better cutting conditions for those learned previously by a proposed replacement algorithm.
Various simulations illustrate the performance of GELCC, and then the simulation results for a given part are provided and discussed.
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Park, BT., Park, MW. & Kim, SK. Generation and Evolutionary Learning of Cutting Conditions for Milling Operations. Int J Adv Manuf Technol 17, 870–880 (2001). https://doi.org/10.1007/s001700170098
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DOI: https://doi.org/10.1007/s001700170098