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A novel interpretable predictive model based on ensemble learning and differential evolution algorithm for surface roughness prediction in abrasive water jet polishing

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As an important indicator of the surface quality of workpieces, surface roughness has a great impact on production costs and the quality performance of the finished components. Effective surface roughness prediction can not only increase productivity but also reduce costs. However, the current methods for surface roughness prediction have some limitations. On the one hand, the prediction accuracy of classical experimental and statistical-based surface roughness prediction methods is low. On the other hand, the results of deep learning-based surface roughness prediction methods are uninterpretable due to their black-box learning mechanism. Therefore, this paper presents an ensemble learning with a differential evolution algorithm, applies it to the prediction of surface roughness of abrasive water jet polishing (AWJP), and conducts an interpretability analysis to identify key factors contributing to the prediction accuracy of surface roughness. First, we proposed automatically constructing features by an Evolution Forest algorithm to train the base regression models. The differential evolution algorithm with a simplified encoding mechanism was then used to search for the best weighted-ensemble to integrate the base regression models for obtaining highly accurate prediction results. Extensive experiments have been conducted on AWJP to validate the effectiveness of our proposed methods. The results show that the prediction accuracy of our proposed method is higher than the existing machine learning algorithms. In addition, this is the first of its time for the contributions of machining parameters (i.e., features) on surface roughness prediction by using interpretable analysis methods. The analysis results can provide a reference basis for subsequent experiments and studies.

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The work described in this paper was mainly supported by the Natural Science Foundation of Fujian Province of China (Project No: 2020J01697) and Research Grants Council (Project No. 15200119) of the Government of the Hong Kong Special Administrative Region, China. The research was partially supported by the National Natural Science Foundation of China (Project No. 52105534). The work was also supported by the Research and Innovation Office of the Hong Kong Polytechnic University (Project No: BBR8 and BD4A). The work was also supported by Shenzhen-Hong Kong-Macau Technology Research Programme (Project No: SGDX20201103095203029 and SGDX20220530110804030).

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Correspondence to Yu Yang or Chunjin Wang.

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Xie, S., He, Z., Loh, Y.M. et al. A novel interpretable predictive model based on ensemble learning and differential evolution algorithm for surface roughness prediction in abrasive water jet polishing. J Intell Manuf 35, 2787–2810 (2024).

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