With the continuous progress of the society, the manufacturing industry is facing greater challenges and opportunities. Complex, diverse and customized requirements demand greater flexibility and faster response times in manufacturing process systems. As a machining method that can quickly remove metal materials, insert milling has attracted much attention in recent years, and has been widely used in aerospace and die manufacturing industries. In this paper, the interpolation and milling optimization of titanium alloy based on machine learning and multi-objective algorithm is studied. In this paper, taking milling TC4 titanium alloy as an example, a machining optimization model based on multi-objective optimization is proposed to improve the quality and improve the efficiency. DDQN is used to optimize and model the parameters (satisfying the minimum surface roughness, maximum material removal rate and optimal milling force stability). Finally, the effectiveness of the multi-objective algorithm is verified by comparing with the empirical parameters.

Saravan Kazemzadeh. Interpolation and Milling Optimization of Titanium Alloy Based on Machine Learning and Multi-objective Algorithm. Machine Learning Theory and Practice (2020), Vol. 1, Issue 1: 41-48. https://doi.org/10.38007/ML.2020.010105.

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