Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors
The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/885 |
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| author | Jitesh Ranjan Karali Patra Tibor Szalay Mozammel Mia Munish Kumar Gupta Qinghua Song Grzegorz Krolczyk Roman Chudy Vladislav Alievich Pashnyov Danil Yurievich Pimenov |
| author_facet | Jitesh Ranjan Karali Patra Tibor Szalay Mozammel Mia Munish Kumar Gupta Qinghua Song Grzegorz Krolczyk Roman Chudy Vladislav Alievich Pashnyov Danil Yurievich Pimenov |
| author_sort | Jitesh Ranjan |
| collection | DOAJ |
| description | The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results. |
| first_indexed | 2024-04-14T02:21:54Z |
| format | Article |
| id | doaj.art-4d50bb4dfbb94a4a9b8e03f85e750dad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T02:21:54Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4d50bb4dfbb94a4a9b8e03f85e750dad2022-12-22T02:18:01ZengMDPI AGSensors1424-82202020-02-0120388510.3390/s20030885s20030885Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple SensorsJitesh Ranjan0Karali Patra1Tibor Szalay2Mozammel Mia3Munish Kumar Gupta4Qinghua Song5Grzegorz Krolczyk6Roman Chudy7Vladislav Alievich Pashnyov8Danil Yurievich Pimenov9Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna-801103, IndiaDepartment of Mechanical Engineering, Indian Institute of Technology Patna, Patna-801103, IndiaDepartment of Manufacturing Science and Engineering, Budapest University of Technology and Economics, H-1111 Budapest, HungaryDepartment of Mechanical Engineering, Imperial College London, Exhibition Rd., London SW7 2AZ, UKKey Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250100, ChinaKey Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250100, ChinaFaculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, PolandFaculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, PolandDepartment of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, RussiaDepartment of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, RussiaThe prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.https://www.mdpi.com/1424-8220/20/3/885micro drillingvibrationcutting forcewavelet packetadaptive neuro fuzzy inference system |
| spellingShingle | Jitesh Ranjan Karali Patra Tibor Szalay Mozammel Mia Munish Kumar Gupta Qinghua Song Grzegorz Krolczyk Roman Chudy Vladislav Alievich Pashnyov Danil Yurievich Pimenov Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors Sensors micro drilling vibration cutting force wavelet packet adaptive neuro fuzzy inference system |
| title | Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors |
| title_full | Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors |
| title_fullStr | Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors |
| title_full_unstemmed | Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors |
| title_short | Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors |
| title_sort | artificial intelligence based hole quality prediction in micro drilling using multiple sensors |
| topic | micro drilling vibration cutting force wavelet packet adaptive neuro fuzzy inference system |
| url | https://www.mdpi.com/1424-8220/20/3/885 |
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