Tool Wear Online Monitoring Method Based on DT and SSAE-PHMMSource: Journal of Computing and Information Science in Engineering:;2021:;volume( 021 ):;issue: 003::page 034501-1DOI: 10.1115/1.4050531Publisher: The American Society of Mechanical Engineers (ASME)
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.
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contributor author | Zhang, Xiangyu | |
contributor author | Liu, Lilan | |
contributor author | Wan, Xiang | |
contributor author | Feng, Bowen | |
date accessioned | 2022-02-05T22:32:23Z | |
date available | 2022-02-05T22:32:23Z | |
date copyright | 3/25/2021 12:00:00 AM | |
date issued | 2021 | |
identifier issn | 1530-9827 | |
identifier other | jcise_21_3_034501.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4277721 | |
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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM | |
type | Journal Paper | |
journal volume | 21 | |
journal issue | 3 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4050531 | |
journal fristpage | 034501-1 | |
journal lastpage | 034501-9 | |
page | 9 | |
tree | Journal of Computing and Information Science in Engineering:;2021:;volume( 021 ):;issue: 003 | |
contenttype | Fulltext |