Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes

doi:10.1007/s40436-024-00520-1

Abstract

Abstract: There are a large number of low rigidity parts in the aerospace field, and how to achieve high-performance manufacturing in their multistage machining processes has received increasing attention. Optimizing the distribution of machining allowance and machining parameters is one of the most convenient ways to improve the machining performance of these parts. In this paper, firstly, considering the machining accuracy and machining efficiency comprehensively, the error efficiency cooperation coefficient of low rigidity parts during machining is established. Based on the semi-parametric regression theory and measured data, the machining error transfer factor within the cooperation coefficient is calibrated. Secondly, the machining optimization strategy based on the Bayesian framework is proposed, and the optimization of multiple machining parameters is realized with the goal of minimizing the error efficiency cooperation coefficient. Finally, the optimization software of machining processes of low rigidity parts for engineering application is developed. In the verification experiments of blade parts, the error efficiency cooperation coefficient is reduced to 0.032 1 after optimization, and the average improvement of machining errors of all measured points is 14.31 μm. Besides, the above method is applied to low rigidity shaft parts, and the effectiveness of the proposed method is further verified.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00520-1

Key words: Machining allowance distribution, Machining parameters optimization, High-performance machining, Low rigidity parts, Multistage processes

Hao Sun, Sheng-Qiang Zhao, Fang-Yu Peng, Rong Yan, Xiao-Wei Tang. Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes[J]. Advances in Manufacturing, 2025, 13(3): 584-605.

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