Restricted accessResearch articleFirst published online 2025
Tool wear prediction and machining performance improvement using regression algorithms and the non-dominated sorting genetic algorithm II in the milling of Ti 2 AlNb intermetallic
Ti2AlNb intermetallic is a crucial material for advanced aero engines. Improper cutting parameters for milling Ti2AlNb intermetallic increase tool wear and reduce tool life. Therefore, predicting tool wear and selecting appropriate cutting parameters in milling is essential. In this paper, three tool wear prediction methods based on the nonlinear least squares regression algorithm, Gaussian process regression algorithm, and support vector regression algorithm are proposed. Among the three methods, the nonlinear least squares regression method achieves the highest prediction accuracy. This paper also presents a cutting parameter selection strategy based on the Non-dominated Sorting Genetic Algorithm II. The optimization goals are to maximize cutter life, maximize material removal rate, and minimize cutting force. The optimization results reveal the relationship between cutter life, material removal rate, and cutting force under certain cutting parameters. Seventeen sets of suitable cutting parameters are identified for the 6 mm diameter end mill, and five sets are identified for the 8 mm diameter end mill, considering conditions of cutter life, material removal rate, and cutting force.
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