Tool Wear Prediction Based on Multi-Information Fusion and Genetic Algorithm-Optimized Gaussian Process Regression in Milling

Abstract

Tool wear condition monitoring plays a crucial role in intelligent manufacturing systems to enhance machining quality and efficiency. The indirect methods employ various sensor signals to monitor tool wear condition, attracting wide attention in industrial applications. Multi-information fusion technologies can promote tool wear monitoring results to be more accurate and reliable. For improving the prediction accuracy and ensuring the reliability of the indirect methods, this study proposes a tool wear prediction method based on multi-information fusion and genetic algorithm (GA)-optimized Gaussian process regression (GPR). First, wavelet packet denoising (WPD)-based signal processing is adopted to suppress the noise interference of multisensor signals. Then, kernel principal component analysis (KPCA)-based dimension reduction is employed to mine the most sensitive features to flank wear from candidate multidomain features. Next, a fusion model of GPR and GA optimization is designed to establish a nonlinear mapping relationship between sensitive characteristics and flank wear width. Finally, performance evaluations under three sets of milling tests are carried out to validate the effectiveness of the proposed method. Experimental results indicate that the proposed method can lower prediction error and uncertainty of flank wear width compared with other intelligent approaches, promoting a successful application of indirect monitoring methods in milling.