Explainable prediction of surface roughness in multi-jet polishing based on ensemble regression and differential evolution method

• Published in: Expert systems with applications 2024-09, Vol.249, p.123578, Article 123578
• Main Authors: Wang, Yueyue, He, Zongbao, Xie, Shutong, Wang, Ruoxin, Zhang, Zili, Liu, Shimin, Shang, Suiyan, Zheng, Pai, Wang, Chunjin
• Format: Article
• Language: English
• Subjects: Differential evolution algorithm; Ensemble learning; Explainable analysis; Polishing; Surface roughness prediction; Ultra-precision machining
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2024.123578

Surface roughness is a critical parameter for quantifying the surface quality of a workpiece. Accurate surface roughness prediction can significantly influence the overall quality of the final components by reducing costs and enhancing productivity. Although various prediction methods have been explored in previous research, less attention has been given to explainable prediction methods for surface roughness. This paper proposes a surface roughness prediction model based on the Differential Evolution (DE) algorithm with ensemble learning. It aims to elucidate the intrinsic correlation between processing parameters and surface roughness values in Multi-Jet Polishing (MJP) using explainable analysis methods. The proposed Ensemble Regression with Differential Evolution (ERDE) algorithm comprises two main modules: data processing and analytics, and ensemble model prediction. These modules investigate the surface roughness of MJP from the data and model levels, respectively. To validate the effectiveness of ERDE, MJP experiments were conducted on three-dimensional printed components of 316L stainless steel. The experimental data indicated that ERDE outperforms other existing algorithms, reducing the mean absolute percentage error by approximately 42 %.