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The Key Process Factors in Prestressed Laser Peen Forming and the Design of Parameters Through an Artificial Neural Network

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Publicado en:Metals vol. 15, no. 4 (2025), p. 445
Autor principal: Lyu Jiayang
Otros Autores: Wang, Yongjun, Wang, Zhiwei, Wang Junbiao
Publicado:
MDPI AG
Materias:
Finite element method
Mathematical analysis
Rectangular plates
Prestressing
Residual stress
Lasers
Experiments
Artificial neural networks
Neural networks
Bending machines
Aluminum alloys
Aerospace industry
Process planning
Curvature
Thickness
Laser shock processing
Process parameters
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Resumen:This research investigated the influences of some key factors in the prestressed laser peen forming (PLPF) process, namely, the plate thickness, the coverage ratio, and the prestress, on the deformation of 2024-T351 rectangular plates through experiments and numerical simulations. In the experiments, laser parameters, such as the laser energy and spot size, were kept unchanged, and prestress was applied through a piece of self-developed, four-point-bending equipment. The curvature radius of the samples was measured through a digital radius gauge. A corresponding finite element analysis (FEA) model of PLPF was also established to simulate the full procedure of the PLPF, including prebending, laser shock peening, and spring back. Based on the PLPF experimental results, an artificial neural network (ANN) was trained to help to design the process parameters, including the coverage ratio and the amount of prebending, according to the plate thickness and the target curvature radius. By adding a penalty term to the loss function, the amount of prebending (AOP) can be reduced as much as possible. The validation of the ANN was confirmed by three other PLPF experiments.
ISSN:2075-4701
DOI:10.3390/met15040445
Fuente:Materials Science Database