[01] Paul Sawadogo, Mechnical Engineering, National Chung Hsing University, Taichung, Taiwan. [02] Pai-Chung Tseng, Mechnical Engineering, National Chung Hsing University, Taichung, Taiwan. [03] Hsueh-Yu Liao, Mechnical Engineering, National Chung Hsing University, Taichung, Taiwan.

In this paper, a fuzzy modeling technique is used for the selection of end milling parameters for a required surface roughness (Ra) with maximum material removal rate (MRR). Spindle speed (N), feed rate (fr) and depth of cut (d) are the inputs and outputs are MRR and SR. Three different levels of each input parameter were used to curry out the experimental work under dry conditions. Optimal sets of parameters were identified using artificial neural network (ANN) for prediction followed by genetic algorithm (GA) multi-objective for optimization. Fuzzy logic model (FLM) was then used to develop a fuzzy rule base in the form of IF-THEN rules for the selection of cutting parameters. The performance of the developed model was evaluated through a validation test. The results show that the average errors of the FLM are 2.463% and 6.08% for Ra and MRR respectively, which is less than 10%.

Artificial Neural Network, Genetic Algorithm, Multi-objective, Fuzzy Logic Model

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