Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling

Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the producti...
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Autor*in:	Bing-Syun Wan [verfasserIn] Ming-Chyuan Lu [verfasserIn] Shean-Juinn Chiou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	AE signals micro-milling hidden Markov model (HMM) tool wear monitoring

Übergeordnetes Werk:	In: Journal of Manufacturing and Materials Processing - MDPI AG, 2018, 6(2022), 2, p 42
Übergeordnetes Werk:	volume:6 ; year:2022 ; number:2, p 42

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jmmp6020042

Katalog-ID:	DOAJ045247102

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520			\|a Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.
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source	In Journal of Manufacturing and Materials Processing 6(2022), 2, p 42 volume:6 year:2022 number:2, p 42
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callnumber-first	T - Technology
author	Bing-Syun Wan
spellingShingle	Bing-Syun Wan misc T58.7-58.8 misc AE signals misc micro-milling misc hidden Markov model (HMM) misc tool wear monitoring misc Production capacity. Manufacturing capacity Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling
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topic_title	T58.7-58.8 Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling AE signals micro-milling hidden Markov model (HMM) tool wear monitoring
topic	misc T58.7-58.8 misc AE signals misc micro-milling misc hidden Markov model (HMM) misc tool wear monitoring misc Production capacity. Manufacturing capacity
topic_unstemmed	misc T58.7-58.8 misc AE signals misc micro-milling misc hidden Markov model (HMM) misc tool wear monitoring misc Production capacity. Manufacturing capacity
topic_browse	misc T58.7-58.8 misc AE signals misc micro-milling misc hidden Markov model (HMM) misc tool wear monitoring misc Production capacity. Manufacturing capacity
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title	Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling
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title_full	Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling
author_sort	Bing-Syun Wan
journal	Journal of Manufacturing and Materials Processing
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title_sort	analysis of spindle ae signals and development of ae-based tool wear monitoring system in micro-milling
callnumber	T58.7-58.8
title_auth	Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling
abstract	Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.
abstractGer	Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.
abstract_unstemmed	Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.
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title_short	Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling
url	https://doi.org/10.3390/jmmp6020042 https://doaj.org/article/8ed96c4ec0f64e0e98d246bde9c9a300 https://www.mdpi.com/2504-4494/6/2/42 https://doaj.org/toc/2504-4494
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up_date	2024-07-03T13:51:25.511Z
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Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling

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