期刊
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
卷 -, 期 -, 页码 -出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13369-023-07642-x
关键词
Cutting force; Cutting parameters; Surface roughness; Response surface methodology; Ensemble machine learning; Artificial neural network
Optimizing cutting parameters is crucial for cost, energy, and time efficiency in milling processes. Through response surface methodology, the optimal parameters for milling AISI 321 material were determined. The study identified that depth of cut was the most influential parameter, affecting cutting forces and surface roughness.
Optimizing the parameters in the milling method is important in terms of cost, energy, and time. The forces that arise during milling cause undesirable results, such as tool wear and energy loss. In this study, cutting parameters were optimized during the milling of AISI 321 material. Cutting speed (60, 70, 80 m/min), feed per tooth (0.04, 0.05, 0.06 mm/tooth), and depth of cut (0.25, 0.5, 0.75 mm) were selected as input parameters. Cutting force in the X and Y axes and the surface roughness were selected as the output parameters. Optimum parameters (60.80 m/min for cutting speed, 0.04 mm/tooth for feed per tooth, and 0.25 mm for depth of cut) were found using response surface methodology. The effect of cutting parameters was calculated by analysis of variance. The most influential parameters were found, depth of cut as 87.49% for cutting force on the X-axis, 86.48% on the Y-axis, and for surface roughness, the cutting speed with 36.48%. Prediction models are compared to choose the best model. Individual (Neural network, decision tree, and k-nearest neighbor algorithms) and ensemble methods (vote) from machine learning and response surface methodology from statistical methods were used for models. The error rates of the models were compared according to the mean absolute percentage error performance criterion. The lowest MAPE values were obtained with the vote method 11.163% in the X-axis force, the artificial neural network algorithm with 7.749% in the Y-axis force, and RSM with 0.93% in the surface roughness.
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