Park, BT Park, MW Kim, SK 2024-01-21T11:33:26Z 2024-01-21T11:33:26Z 2021-09-05 2001-12 0268-3768 https://pubs.kist.re.kr/handle/201004/139989 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 previous 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. English SPRINGER-VERLAG LONDON LTD OPTIMIZATION SYSTEM ART Generation and evolutionary learning of cutting conditions for milling operations Article 10.1007/s001700170098 1 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, v.17, no.12, pp.870 - 880 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 17 12 870 880 scie scopus 000169770700002 2-s2.0-0034802949 Automation & Control Systems Engineering, Manufacturing Automation & Control Systems Engineering Article OPTIMIZATION SYSTEM ART computer-aided process planning (CAPP) cutting conditions neural network operation planning