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Tao Yang

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Proceedings Article | 30 August 2022 Paper

Simulation modeling and optimization of surface residual stress in aluminum alloy milling

Tao Yang, Heng Zhou, SiBao Wang

Proceedings Volume 12309, 1230909 (2022) https://doi.org/10.1117/12.2645643

KEYWORDS: Finite element methods, Aluminum, Data modeling, Optimization (mathematics), Neural networks, Spindles, Neurons, Error analysis

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The residual stress on the machined surface has a significant impact on the service performance of the parts and needs to be well predicted. However, considering the existing research, the experimental method is expensive and the analytical method is complex to predict the residual stress. Although the existing finite element method can be used to predict the residual stress, its simulation accuracy is not high to meet the requirements for the residual stress optimization. Therefore, based on the J-C constitutive model of aluminum alloy 7075-T6, the simulation model of residual stress on the milling surface of aluminum alloy is established by AdvantEdge cutting simulation software. The experimental results show that the average simulation error of residual stress in 𝑥 and 𝑦 directions is 9.8%, which verifies the proposed finite element model. Based on the finite element model, the effects of milling process parameters on residual stress are analyzed. Then, the residual stress samples are generated by the finite element model to reduce the cost and period of the experiment. And GRNN is established to predict the residual stress based on generated data samples. The prediction accuracy generated by GRNN reached more than 80%. Based on the GWO algorithm, a process parameter optimization method is proposed for minimum surface residual stress. The optimal process parameters are spindle speed 7200r/min, feed rate 0.1mm/z, depth of cut 1.4mm, and 𝜎_xx is 105.68 MPa, 𝜎_yy is 64.82 MPa. The proposed method provided a basis for improving the service performance of parts by optimizing the surface residual stress.

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