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Surface roughness prediction and process parameter optimization of Ti-6Al-4 V by magnetic abrasive finishing

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Abstract

In order to effectively predict the surface roughness Ra of Ti-6Al-4 V material after magnetic abrasive finishing (MAF) process, and optimize the process parameters to improve the surface quality of the material. Firstly, diamond/Fe-based magnetic abrasive powders (MAPs) are prepared for the MAF process of Ti-6Al-4 V by using the gas–solid two-phase double-stage atomization and rapid solidification method. The effects of rotational speed of the magnetic pole, working gap, feed velocity of workpiece, and filling quantity of MAPs on the surface roughness efficiency are discussed. Secondly, the orthogonal experiment is designed. The prediction model of surface roughness based on gray wolf optimization (GWO) algorithm and support vector regression (SVR), which is constructed according to the experimental results. The simulation shows that the R2 of the optimized prediction model is 0.987456, and the MAPE is less than 1.99%. Finally, GWO algorithm is employed again to perform a global optimization search on the constructed prediction model. The optimal combination of process parameters is searched and verified, the surface roughness Ra is 0.098 μm, and the relative error is less than 2.82% compared with the model prediction. The comparison of surface morphology before and after MAF of Ti-6Al-4 V shows that the MAF technology combined with the prediction model based on GWO-SVR can effectively improve the surface quality of Ti-6Al-4 V.

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Availability of data and material

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 acknowledge all the support received from the National Natural Science Foundation of China (No. 51875328) and the Natural Science Foundation of Shandong Province (No. ZR2019MEE013).

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Authors and Affiliations

  1. Institute for Advanced Manufacturing, Shandong University of Technology, Zibo, 255049, People’s Republic of China

    Zhuang Song, Yugang Zhao, Guangxin Liu, Yuewu Gao, Xiajunyu Zhang, Chen Cao, Di Dai & Yueming Deng

  2. School of Mechanical Engineering, Shandong University of Technology, 266 Xincun West Road, NO, Zibo, 255049, People’s Republic of China

    Yugang Zhao

Authors
  1. Zhuang Song
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  3. Guangxin Liu
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Contributions

The method was conceived by Yugang Zhao, the experiments were designed by Zhuang Song and performed by Guangxin Liu, Yuewu Gao, Xiajunyu Zhang, Chen Cao, Di Dai, and Yueming Deng. All the authors took part in the paper.

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Correspondence to Yugang Zhao.

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This article is part of the Topical Collection: New Intelligent Manufacturing Technologies through the Integration of Industry 4.0 and Advanced Manufacturing

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Song, Z., Zhao, Y., Liu, G. et al. Surface roughness prediction and process parameter optimization of Ti-6Al-4 V by magnetic abrasive finishing. Int J Adv Manuf Technol 122, 219–233 (2022). https://doi.org/10.1007/s00170-022-09354-2

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