AI-based tool wear prediction with feature selection from sound signal analysis
With the advancement of Industry 4.0, there has been a growing demand for the automation and digitalization of manufacturing processes, including machining. One of the core elements of this evolution is tool wear monitoring. In automated production systems, the condition of tools greatly influences...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Mechanical Engineering |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2025.1608067/full |
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| author | Viet Q. Vu Tien-Ninh Bui Minh-Quang Tran |
| author_facet | Viet Q. Vu Tien-Ninh Bui Minh-Quang Tran |
| author_sort | Viet Q. Vu |
| collection | DOAJ |
| description | With the advancement of Industry 4.0, there has been a growing demand for the automation and digitalization of manufacturing processes, including machining. One of the core elements of this evolution is tool wear monitoring. In automated production systems, the condition of tools greatly influences production efficiency, cutting stability, and the quality of machined surfaces. The present study proposes an effective tool condition monitoring system based on cutting sound signature analysis and a machine learning model for milling processes. In the proposed system, the correlation between the sound signal and the tool flank wear under various cutting conditions is investigated. First, the measured sound signals in the milling process are extracted into a series of intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD). Hilbert transform (HT) is then applied to each IMF to generate the respective instantaneous frequencies, and the most significant statistic features correlated to the tool wear are selected using the collinearity diagnostics. Finally, an artificial neural network (ANN) model is designed to estimate tool wear levels. Experimental results confirm that the developed approach maintains excellent accuracy in tool wear prediction across of various cutting conditions. Moreover, the proposed approach has the potential to be implemented in practical applications as a cost-effective method for tool condition monitoring. |
| format | Article |
| id | doaj-art-44bbe543f1324f8b9d2a478f90dfec64 |
| institution | Kabale University |
| issn | 2297-3079 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Mechanical Engineering |
| spelling | doaj-art-44bbe543f1324f8b9d2a478f90dfec642025-08-20T05:32:35ZengFrontiers Media S.A.Frontiers in Mechanical Engineering2297-30792025-08-011110.3389/fmech.2025.16080671608067AI-based tool wear prediction with feature selection from sound signal analysisViet Q. Vu0Tien-Ninh Bui1Minh-Quang Tran2Department of Engineering and Technology, International Training Faculty, Thai Nguyen University of Technology, Thai Nguyen, VietnamDepartment of Mechanical Engineering, TUETECH University, Thai Nguyen, VietnamDepartment of Mechanical Engineering, TUETECH University, Thai Nguyen, VietnamWith the advancement of Industry 4.0, there has been a growing demand for the automation and digitalization of manufacturing processes, including machining. One of the core elements of this evolution is tool wear monitoring. In automated production systems, the condition of tools greatly influences production efficiency, cutting stability, and the quality of machined surfaces. The present study proposes an effective tool condition monitoring system based on cutting sound signature analysis and a machine learning model for milling processes. In the proposed system, the correlation between the sound signal and the tool flank wear under various cutting conditions is investigated. First, the measured sound signals in the milling process are extracted into a series of intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD). Hilbert transform (HT) is then applied to each IMF to generate the respective instantaneous frequencies, and the most significant statistic features correlated to the tool wear are selected using the collinearity diagnostics. Finally, an artificial neural network (ANN) model is designed to estimate tool wear levels. Experimental results confirm that the developed approach maintains excellent accuracy in tool wear prediction across of various cutting conditions. Moreover, the proposed approach has the potential to be implemented in practical applications as a cost-effective method for tool condition monitoring.https://www.frontiersin.org/articles/10.3389/fmech.2025.1608067/fullAI-based tool wear predictionartificial neural networkfeature selectionsound signal analysisintrinsic mode functions |
| spellingShingle | Viet Q. Vu Tien-Ninh Bui Minh-Quang Tran AI-based tool wear prediction with feature selection from sound signal analysis Frontiers in Mechanical Engineering AI-based tool wear prediction artificial neural network feature selection sound signal analysis intrinsic mode functions |
| title | AI-based tool wear prediction with feature selection from sound signal analysis |
| title_full | AI-based tool wear prediction with feature selection from sound signal analysis |
| title_fullStr | AI-based tool wear prediction with feature selection from sound signal analysis |
| title_full_unstemmed | AI-based tool wear prediction with feature selection from sound signal analysis |
| title_short | AI-based tool wear prediction with feature selection from sound signal analysis |
| title_sort | ai based tool wear prediction with feature selection from sound signal analysis |
| topic | AI-based tool wear prediction artificial neural network feature selection sound signal analysis intrinsic mode functions |
| url | https://www.frontiersin.org/articles/10.3389/fmech.2025.1608067/full |
| work_keys_str_mv | AT vietqvu aibasedtoolwearpredictionwithfeatureselectionfromsoundsignalanalysis AT tienninhbui aibasedtoolwearpredictionwithfeatureselectionfromsoundsignalanalysis AT minhquangtran aibasedtoolwearpredictionwithfeatureselectionfromsoundsignalanalysis |