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<p>Surface roughness is one of the most critical quality indicators in precision machining, as it directly affects product functionality, durability, and assemblability. Therefore, this study proposes a quantification method based on frequency response analysis to predict surface roughness improvement using abrasive force signals generated during the magnetic abrasive finishing process. Raw abrasive force signals measured with a dynamometer were analyzed using fast Fourier transform (FFT) and Welch’s power spectral density to identify the effective frequency band between of 500-1,200&#xa0;Hz. Subsequently, the real-time effective abrasive force was quantitatively extracted through inverse FFT and root mean square calculations. The extracted effective force was used as an input variable in the AI-based ridge regression model, and its predictive performance was compared with a model using only process variables based on a total of 27 experimental datasets. As a result, the model incorporating real-time effective abrasive force demonstrated superior prediction accuracy with a coefficient of determination of 0.989 and a root mean square error (RMSE) of 0.0110. In addition, further validation experiments conducted under arbitrary conditions confirmed its high generalization capability, with the RMSE of 0.0213.</p>

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AI regression model based on effective abrasive force in MAF process for high-precision surface roughness prediction

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Won-Jun Bae,
  • Jung-Hee Lee,
  • Jae-Seob Kwak

摘要

Surface roughness is one of the most critical quality indicators in precision machining, as it directly affects product functionality, durability, and assemblability. Therefore, this study proposes a quantification method based on frequency response analysis to predict surface roughness improvement using abrasive force signals generated during the magnetic abrasive finishing process. Raw abrasive force signals measured with a dynamometer were analyzed using fast Fourier transform (FFT) and Welch’s power spectral density to identify the effective frequency band between of 500-1,200 Hz. Subsequently, the real-time effective abrasive force was quantitatively extracted through inverse FFT and root mean square calculations. The extracted effective force was used as an input variable in the AI-based ridge regression model, and its predictive performance was compared with a model using only process variables based on a total of 27 experimental datasets. As a result, the model incorporating real-time effective abrasive force demonstrated superior prediction accuracy with a coefficient of determination of 0.989 and a root mean square error (RMSE) of 0.0110. In addition, further validation experiments conducted under arbitrary conditions confirmed its high generalization capability, with the RMSE of 0.0213.