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Modeling and optimization of surface residual stress profiles in milling of aluminum 7075-T6 alloy

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Abstract

Aluminum 7075-T6 alloy has been widely employed in aviation, transport, and automobile applications due to its remarkable properties, while a lot of residual stresses can be generated in the machined surface and subsurface during the machining process. The machining parameters have significant effects on the formation of residual stress, it is important to predict the residual stress distribution with the cutting parameters and optimize the machining parameters to acquire the desirable residual profiles. Although many efforts of current studies have been paid to the prediction of residual stress profiles in different materials and machining processes, however, few works focused on residual stress in-depth profiles in the machining of 7075-T6 aluminum alloy, and the optimization of cutting parameters for required residual stress profile has also rarely been reported. Therefore, this study proposed an integrated prediction model, which combines exponential decay cosine function (EDC), particle swarm optimization (PSO), and back propagation neural network (BP), to predict the in-depth residual stress profile of the machined surface in milling of 7075-T6 aluminum alloy. Furthermore, according to the residual stress profile, the key features for describing the residual stress profile include the surface residual stress (SRS), maximum compressive residual stress (MCRS), depth of maximum compressive residual stress (DMCS), and depth of residual stress (DRS), were identified and analyzed. And a multiple objective optimization was conducted, where Kriging-based models were employed to establish the relationships between machining parameters and objectives (SRS and MCRS), and the MRR was also employed as an objective taking account of high production efficiency requirement. Finally, a two-stage optimization strategy integrating NSGA-III, MOPSO, and TOPSIS algorithms was used to address the multi-objective optimization model to obtain the expected residual stress profile and MRR. This work can provide some practical guidance for industrial production in machining 7075-T6 aluminum alloy.

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Funding

The authors would like to acknowledge the financial supports from the General Program of the National Natural Science Foundation of China (No. 52175453, No. 52375481) and the Graduate Scientific Research and Innovation Foundation of Chongqing, China (No. CYB22011).

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  1. State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, 400044, China

    Qibin Yue, Yan He, Yufeng Li & Shufei Tian

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QY: conceptualization, methodology, software, and writing—original draft. YH: conceptualization, supervision, and funding acquisition. YL: conceptualization and supervision. ST: methodology, experiments, software, and writing—original draft.

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Yue, Q., He, Y., Li, Y. et al. Modeling and optimization of surface residual stress profiles in milling of aluminum 7075-T6 alloy. Int J Adv Manuf Technol 130, 5913–5934 (2024). https://doi.org/10.1007/s00170-024-13057-1

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