Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
Drilling, one of the most used machining processes, has wide application in different industrial fields. Monitoring the system health and operation status of the drilling process is essential for maintaining production efficiency. In this study, a convolutional neural network (CNN), a deep-learning...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10799 |
| _version_ | 1797469252394942464 |
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| author | Yongchao Yu Qi Liu Boon Siew Han Wei Zhou |
| author_facet | Yongchao Yu Qi Liu Boon Siew Han Wei Zhou |
| author_sort | Yongchao Yu |
| collection | DOAJ |
| description | Drilling, one of the most used machining processes, has wide application in different industrial fields. Monitoring the system health and operation status of the drilling process is essential for maintaining production efficiency. In this study, a convolutional neural network (CNN), a deep-learning method, is applied to the defect diagnosis of drill bits. Four drill bits with different health conditions were used to drill holes in an aluminum block, and a vibration sensor collected the signals. Vibration spectrograms generated using short-time Fourier transform were applied to a 2D CNN algorithm, and they were then reconstructed into a 1D data set and applied to a 1D CNN algorithm. The input data size was reduced significantly compared to the raw vibration data after the data-reconstruction process. As a result, the 2D CNN process shows a diagnostic accuracy of 97.33%. On the other hand, the 1D CNN provides a diagnostic accuracy of 96.6%, but it only requires 2/3 of the computational time required by the 2D CNN. |
| first_indexed | 2024-03-09T19:17:45Z |
| format | Article |
| id | doaj.art-0eda050d47f3440896ba251a1ebccc4e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:45Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0eda050d47f3440896ba251a1ebccc4e2023-11-24T03:32:59ZengMDPI AGApplied Sciences2076-34172022-10-0112211079910.3390/app122110799Application of Convolutional Neural Network to Defect Diagnosis of Drill BitsYongchao Yu0Qi Liu1Boon Siew Han2Wei Zhou3Schaeffler Hub for Advanced Research, 61 Nanyang Dr, ABN-B1b-11, Singapore 637460, SingaporeSchaeffler Hub for Advanced Research, 61 Nanyang Dr, ABN-B1b-11, Singapore 637460, SingaporeSchaeffler Hub for Advanced Research, 61 Nanyang Dr, ABN-B1b-11, Singapore 637460, SingaporeSchaeffler Hub for Advanced Research, 61 Nanyang Dr, ABN-B1b-11, Singapore 637460, SingaporeDrilling, one of the most used machining processes, has wide application in different industrial fields. Monitoring the system health and operation status of the drilling process is essential for maintaining production efficiency. In this study, a convolutional neural network (CNN), a deep-learning method, is applied to the defect diagnosis of drill bits. Four drill bits with different health conditions were used to drill holes in an aluminum block, and a vibration sensor collected the signals. Vibration spectrograms generated using short-time Fourier transform were applied to a 2D CNN algorithm, and they were then reconstructed into a 1D data set and applied to a 1D CNN algorithm. The input data size was reduced significantly compared to the raw vibration data after the data-reconstruction process. As a result, the 2D CNN process shows a diagnostic accuracy of 97.33%. On the other hand, the 1D CNN provides a diagnostic accuracy of 96.6%, but it only requires 2/3 of the computational time required by the 2D CNN.https://www.mdpi.com/2076-3417/12/21/10799defect diagnosisconvolutional neural networkcondition monitoring |
| spellingShingle | Yongchao Yu Qi Liu Boon Siew Han Wei Zhou Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits Applied Sciences defect diagnosis convolutional neural network condition monitoring |
| title | Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits |
| title_full | Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits |
| title_fullStr | Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits |
| title_full_unstemmed | Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits |
| title_short | Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits |
| title_sort | application of convolutional neural network to defect diagnosis of drill bits |
| topic | defect diagnosis convolutional neural network condition monitoring |
| url | https://www.mdpi.com/2076-3417/12/21/10799 |
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