Surface roughness prediction using kernel locality preserving projection and Bayesian linear regression. (1st May 2021) - Kong, Dongdong; Zhu, Junjiang; Duan, Chaoqun; Lu, Lixin; Chen, Dongxing
Highlights: A new two-stage feature-fusion method by combining PCA and KLPP is presented. A unique approach for determining the model parameters of KLPP is presented. KLPP is comprehensively analyzed under three weighting methods. KLPP helps to improve the prediction accuracy and compress the CI of Standard_SBLR. Under the support of KLPP, Standard_SBLR shows superior predictive performance. Abstract: To further improve the prediction accuracy of surface roughness in milling process, this paper presents a new two-stage feature-fusion method by combining principal component analysis (PCA) and kernel locality preserving projection (KLPP). PCA is utilized for dimension-reduction while KLPP is utilized for dimension-increment. Vibration information of the workpiece, fixture and spindle is adopted as the monitoring signal. Firstly, the commonly-used time-domain features are extracted from the vibration signals. Then, the presented two-stage feature-fusion method is carried out for extracting more effective signal features. Besides, two types of Bayesian linear regression (BLR) model (Standard_BLR and Standard_SBLR) are utilized for model construction. Before the two-stage feature-fusion, Standard_BLR is utilized to determine the optimum dimension of PCA-based fusion features and the model parameters of KLPP. After the two-stage feature-fusion, Standard_SBLR is utilized to construct the BLR-based surface roughness predictive model. Two types of milling experiment (down milling and up milling) are carried out to show the influence of the presented two-stage feature-fusion method on the predictive performance of Standard_SBLR. Experimental results show that KLPP is highly effective in improving the prediction accuracy and compressing the confidence interval (CI) of Standard_SBLR. Moreover, the comparison results show that the effectiveness of KLPP is not inferior to kernel principal component analysis (KPCA). This paper lays the foundation for accurate monitoring of surface roughness in real industrial settings.