Abstract
Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time‐frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM‐RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G‐mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.
Highlights
Milling is one of the most commonly used processes in today’s industry and mechanical machining workshop for machining parts to precise sizes and shapes
Yang et al [20] established an integrated prediction model by using the trajectory similarity-based prediction (TSBP) and the differential evolution SVR (DE-SVR) algorithm to predict milling tool wear and life, and the results demonstrated that this integrated prediction model was better than other four single algorithms (TSPB, SVR, PSO-support-vector machine (SVM), and HMM)
Flank wear bandwidth of new milling tool max normalization method is used to standardize and normalize all input data to the range of (0-1). e calculation formula is expressed as follows: XNorm
Summary
Milling is one of the most commonly used processes in today’s industry and mechanical machining workshop for machining parts to precise sizes and shapes. The milling tool that withstands high temperature and mechanical shock during the milling process will be worn gradually, which directly affect the surface quality of the workpiece and increase the rejection rate and production cost [1, 2], especially in the case of high-precision material cutting. To reduce this effect, the tool condition monitoring (TCM) system has been widely used and achieved remarkable results [3].
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