Modeling and Multi-objective Optimization of Laser Cleaning Quality of 7075 Aluminum Alloy

doi:10.3901/JME.2025.03.422

Abstract

Abstract: In the process of laser cleaning, it has become a challenge to coordinate and control parameters to ensure efficient and high-precision cleaning quality due to the numerous energy and motion parameters involved in laser processing. To address these issues, a BOX Behnke design (BBD) response surface experiment is designed with process parameters (laser power, scanning speed, pulse frequency) as optimization variables, and surface roughness, oxygen removal rate, and static contact angle as multi-objective optimization indicators after cleaning. A response surface model and GA-BP neural network model are established. Particularly, a multi-objective sparrow algorithm based on the improvement of the good point set and adaptive normal distribution weights is proposed. Therefore, the problems of poor initial population quality, easy to fall into local optima, low population diversity in the later stages of iteration, and low local development ability have been solved. Furthermore, the model is optimized and the optimal process parameter combination obtained through TOPSIS is as follows: pulse frequency 2.6 kHz, laser power 245 W, and scanning speed 2 900 mm/s. In addition, comparative analysis and process experiments of multiple algorithms are conducted to verify the effectiveness of the algorithms. The results showed that the proposed model and algorithm are applied for optimization. As a result, compared with the original sample, the surface roughness is reduced by 40.26%, the oxygen content is reduced by 96.97%, the static contact angle is increased by 58.32%, and the cleaning quality was significantly improved.

Key words: laser cleaning, aluminium alloy, cleaning quality, process parameters, multi objective optimization

CLC Number:

TG156

WANG Wei, LI Xiaoxu, LIU Weijun, BIAN Hongyou, XING Fei, WANG Jing. Modeling and Multi-objective Optimization of Laser Cleaning Quality of 7075 Aluminum Alloy[J]. Journal of Mechanical Engineering, 2025, 61(3): 422-439.

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