Bayesian linear regression for surface roughness prediction. (August 2020)

• Record Type: Journal Article
• Authors: Kong, Dongdong; Zhu, Junjiang; Duan, Chaoqun; Lu, Lixin; Chen, Dongxing
• Is Part Of: Mechanical systems and signal processing. Volume 142(2020)
• Publication Date: 2020-08
• Subjects: Surface roughness prediction -- Dimension-increment technique -- Bayesian linear regression; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106770
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A method of extracting effective features for monitoring surface roughness is presented. KPCA_IRBF is re-derivated according to the derivation of PCA. A new kind of BLR model abbreviated as Standard_SBLR is firstly proposed. KPCA_IRBF helps to improve the prediction accuracy and ameliorate the CI of BLR. Under the support of KPCA_IRBF, Standard_SBLR shows superior predictive performance. Abstract: To improve the prediction accuracy of surface roughness in milling process, this paper provides an unique feature extraction method and comprehensively analyzes four types of Bayesian linear regression (BLR) model (Standard_BLR, Gaussian_BLR, Standard_SBLR and Gaussian_SBLR). Among them, Standard_SBLR is firstly proposed. Vibration information of the workpiece, fixture and spindle is adopted as the monitoring signal. The unique feature extraction method consists of three stages: extraction of time-domain features from the vibration signals, dimension-reduction by principal component analysis (PCA) and dimension-increment by the integrated radial basis function based kernel principal component analysis (KPCA_IRBF). The BLR models can provide both the predicted value and the corresponding confidence interval (CI). Two types of milling experiment (down milling and up milling) are conducted to reveal the influence of dimension-increment process of KPCA_IRBF on the predictive performance of the BLR models. Experimental results show that when combined with KPCA_IRBF, Standard_SBLR has the best predictive performance among the four BLR models. This also shows that KPCA_IRBF is highly effective in improving the prediction accuracy and compressing the CI of Standard_SBLR. To further prove the superiority of Standard_SBLR, other powerful machine learning methods such as partial least squares regression (PLS), artificial neural network (ANN) and support vector machine (SVM) are also utilized to realize surface roughness prediction under the support of KPCA_IRBF. This paper lays the foundation for accurate monitoring of surface roughness in real industrial settings.