Optimization algorithm for deep hole milling parameters of nickel-based alloys based on deep learning

Chuntang Xu; Yaping Wang

doi:10.1117/12.3030153

5 June 2024 Optimization algorithm for deep hole milling parameters of nickel-based alloys based on deep learning

Chuntang Xu, Yaping Wang

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131631D (2024) https://doi.org/10.1117/12.3030153
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

In order to reduce the roughness of the milling, a optimization algorithm is designed. Firstly, the deep-hole milling parameters of nickel-based alloy were preprocessed, and then the unqualified parameters of nickel-based alloy were detected as the parameters optimization objects. Finally, the optimization method of nickel-based alloy deep-hole milling parameters was designed based on the deep learning algorithm to complete the whole optimization process of deep-hole milling parameters of nickel-based alloy. The experimental results show that the roughness of the design method is low. Under the case of different scanning speed and feed speed, the roughness of nickel base alloy deep hole milling can reach 0.01 μm, the lowest is only 0.006 μm, the efficiency is high, and the lowest time is only 2.5min, The optimization effect is better.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chuntang Xu and Yaping Wang "Optimization algorithm for deep hole milling parameters of nickel-based alloys based on deep learning", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131631D (5 June 2024); https://doi.org/10.1117/12.3030153

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