| description abstract | The real-time requirements of tool wear states monitoring are getting higher and higher, at the same time, tool wear monitoring lacks a modeling data comprehensive carrier, which hinders its application in the actual machining process. In order to solve this problem, combining the high fidelity real-time behavior simulation characteristics of digital twin (DT) and the powerful data mining capabilities of artificial intelligence, an online tool wear monitoring method based on DT and Stack Sparse Auto-Encoder-parallel hidden Markov model (SSAE-PHMM) was proposed. First, a DT which can reflect the real state of the tool was established, and the tool wear state was predicted by visual display and analysis in the virtual space; Second, a tool wear state recognition model based on SSAE-PHMM was established, which can adaptively complete time domain feature extraction. And for each tool wear state, multiple HMM models were combined into a PHMM model to realize accurate recognition of tool wear state. PHMM overcome the defects of poor convergence and long training time of artificial neural network, and greatly improved the performance of classifier. Through the deep integration of DT and artificial intelligence, real-time data-driven tool wear qualitative and quantitative online monitoring was realized, and the effectiveness of this method was verified by experiments. | |
