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The prediction model and experimental research of grinding surface roughness based on AE signal

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Abstract

This paper is based on the investigation of the relationship between the processing parameters and the characteristic parameters of acoustic emission signal (AE signal) including RMS value, ringing count, and signal spectrum during the grinding of several difficult-to-machine metallic materials; the variation of AE signal characteristic parameters and spectrum with the parameters of grinding depth ap, grinding wheel velocity vs, and feed velocity vw was analyzed, then the corresponding relationship between acoustic emission signal characteristic parameters and machining surface roughness was given. On this basis, the multi-information fusion algorithm based on BP neural network was used to reasonably fuse various characteristic parameters of AE signals, then predict and recognize the surface roughness of grinding workpieces. Finally, the established model was optimized by using genetic algorithm, which significantly improved the prediction accuracy and provided a reliable prediction model for the grinding of difficult-to-machine alloys, providing a feasible method for predicting surface roughness for practical production.

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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

This work is supported by the National Natural Science Foundation of China (No. 51771193, 52005092) and the Fundamental Research Funds for the Central Universities (No. N2103013).

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, People’s Republic of China

    Guoqiang Yin, Jiahui Wang, Yunyun Guan & Yao Sun

  2. Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, People’s Republic of China

    Dong Wang

Authors
  1. Guoqiang Yin
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  2. Jiahui Wang
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  3. Yunyun Guan
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  4. Dong Wang
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  5. Yao Sun
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Contributions

Guoqiang Yin: investigation, conceptualization, methodology, experiment, writing—original draft. Jiahui Wang: investigation, experiment, writing—reviewing and editing. Yunyun Guan: investigation, experiment. Dong Wang: funding. Yao Sun: funding, supervision.

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Correspondence to Guoqiang Yin.

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Yin, G., Wang, J., Guan, Y. et al. The prediction model and experimental research of grinding surface roughness based on AE signal. Int J Adv Manuf Technol 120, 6693–6705 (2022). https://doi.org/10.1007/s00170-022-09135-x

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  • DOI: https://doi.org/10.1007/s00170-022-09135-x

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