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• 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. 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. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]