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

Shutong Xie, Zongbao He, Yee Man Loh, Yu Yang (), Kunhong Liu, Chao Liu, Chi Fai Cheung, Nan Yu and Chunjin Wang ()
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Shutong Xie: Jimei University
Zongbao He: Jimei University
Yee Man Loh: The Hong Kong Polytechnic University
Yu Yang: The Hong Kong Polytechnic University
Kunhong Liu: Xiamen University
Chao Liu: Aston University
Chi Fai Cheung: The Hong Kong Polytechnic University
Nan Yu: University of Edinburgh
Chunjin Wang: The Hong Kong Polytechnic University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 6, No 18, 2787-2810

Abstract: Abstract 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.

Keywords: Surface roughness prediction; Polishing; Finishing; Interpretable machine learning; Ensemble learning; Ultra-precision machining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02175-4

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