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An intelligent parameters optimization method of titanium alloy belt grinding considering machining efficiency and surface quality

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Abstract

Abrasive belt grinding is widely used in typical difficult materials such as titanium alloy, due to its lower grinding temperature and flexible machining. Processing efficiency and processing quality are the two most concerning problems. However, when enhancing processing efficiency, it is a key issue to guarantee the quality of the machining surface. This study provided a parameter optimization model based on the optimization objectives of surface roughness (Ra) and material removal rate (MRR), and the grinding parameters obtained by the solution were verified by experiments. It is found that the performance of the improved non-dominated sorting genetic algorithm (CNSGA-II) is generally good. The algorithm can converge faster and the diversity of Pareto solutions is improved. Besides, when the process parameters obtained by the multi-objective optimization model are used for machining, the surface roughness of the workpiece is reduced to 0.499 μm, and the material removal amount can reach 0.115 mg/min. This shows that the method can not only improve the grinding efficiency of titanium alloy workpiece, but also improve the surface quality. Furthermore, the surface morphology is better with the optimal combination of process parameters; there are no obvious tearing and wear debris on the surface of the CNSGA-II (Ra, MRR), which exhibited deeper wear scars than that of the DF (Ra) and improves surface fineness and flatness.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The authors would like to gratefully acknowledge the financial support from the National Natural Science Foundation of China (No. U1908232), the National Science and Technology Major Project (No. 2017-VII-0002–0095), and the Graduate Scientific Research and Innovation Foundation of Chongqing (No. CYB22009).

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Authors and Affiliations

  1. College of Mechanical and Vehicle Engineering, Chongqing University, No. 174, Shazhengjie Street, Shapingba, Chongqing, 400044, China

    Guijian Xiao, Hui Gao, Youdong Zhang, Bao Zhu & Yun Huang

  2. State Key Laboratory of Mechanical Transmissions, Chongqing University, No. 174, Shazhengjie Street, Shapingba, Chongqing, 400044, China

    Guijian Xiao & Yun Huang

Authors
  1. Guijian Xiao
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  2. Hui Gao
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  3. Youdong Zhang
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  4. Bao Zhu
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  5. Yun Huang
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Contributions

Guijian Xiao: conceptualization, methodology, investigation, and funding acquisition. Hui Gao: experiment, result analysis, and writing-original draft preparation. Youdong Zhang: experiment and data curation. Bao Zhu: experiment and data curation.

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Correspondence to Guijian Xiao.

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Xiao, G., Gao, H., Zhang, Y. et al. An intelligent parameters optimization method of titanium alloy belt grinding considering machining efficiency and surface quality. Int J Adv Manuf Technol 125, 513–527 (2023). https://doi.org/10.1007/s00170-022-10723-0

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