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Predictive model of surface roughness in milling of 7075Al based on chatter stability analysis and back propagation neural network

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Abstract

Accurate prediction of the machining quality such as the surface roughness is one of the main objectives of the intelligent manufacturing research. In this study, we investigate the feasibility of combining milling stability analysis and a back propagation (BP) neural network model to predict the surface roughness of aerospace aluminum alloy 7075Al in high-speed precision milling. The difference between the critical depth of cut obtained from the milling stability lobe diagram (SLD) and the actual depth of cut (termed the “chatter stability feature”) was used as a critical input variable of the neural network model, thereby improving its prediction performance. It is demonstrated that the proposed chatter stability feature has a strong correlation with the surface roughness, making it an information-rich input feature of the prediction model. The experimental results show that the prediction accuracy can be improved by 7.8% compared with a neural network model that only uses the cutting parameters (i.e., spindle speed, feed rate, depth of cut, and feed rate per tooth) as predictors. In addition, univariate and multivariate sensitivity analysis results suggest that the performance of the proposed approach is robust to errors in the SLD measurements. Compared to conventional methods which only consider the cutting parameters, an improvement in prediction accuracy can be expected with up to 10% errors in modal parameters.

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Funding

This work is supported by the National Key R&D Program of China (Grant No. 2020YFB1710400), the National Natural Science Foundation of China (Grant No. 52005205), and the National Science Fund for Distinguished Young Scholars (Grant No. 52225506).

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Long Bai, Xin Cheng, Qizhong Yang & Jianfeng Xu

  2. Wuhan Digital Design and Manufacturing Innovation Center Co., Ltd, Wuhan, 430074, China

    Jianfeng Xu

Authors
  1. Long Bai
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  2. Xin Cheng
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  3. Qizhong Yang
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  4. Jianfeng Xu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Long Bai, Xin Cheng, and Qizhong Yang. The first draft and revised version of the manuscript were written by Long Bai and Xin Cheng.

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Correspondence to Jianfeng Xu.

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Bai, L., Cheng, X., Yang, Q. et al. Predictive model of surface roughness in milling of 7075Al based on chatter stability analysis and back propagation neural network. Int J Adv Manuf Technol 126, 1347–1361 (2023). https://doi.org/10.1007/s00170-023-11133-6

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  • DOI: https://doi.org/10.1007/s00170-023-11133-6

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