Process Fingerprint in Micro-EDM Drilling
The micro electrical discharge machining (micro-EDM) process is extensively used in aerospace, automotive, and biomedical industries for drilling small holes in difficult-to-machine materials. However, due to the complexity of the electrical discharge phenomena, optimization of the processing parame...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/10/4/240 |
| _version_ | 1819210776518328320 |
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| author | Mattia Bellotti Jun Qian Dominiek Reynaerts |
| author_facet | Mattia Bellotti Jun Qian Dominiek Reynaerts |
| author_sort | Mattia Bellotti |
| collection | DOAJ |
| description | The micro electrical discharge machining (micro-EDM) process is extensively used in aerospace, automotive, and biomedical industries for drilling small holes in difficult-to-machine materials. However, due to the complexity of the electrical discharge phenomena, optimization of the processing parameters and quality control are time-consuming operations. In order to shorten these operations, this study investigates the applicability of a process fingerprint approach in micro-EDM drilling. This approach is based on the monitoring of a few selected physical quantities, which can be controlled in-line to maximize the drilling speed and meet the manufacturing tolerance. A Design of Experiments (DoE) is used to investigate the sensitivity of four selected physical quantities to variations in the processing parameters. Pearson’s correlation is used to evaluate the correlation of these quantities to some main performance and hole quality characteristics. Based on the experimental results, the potential of the process fingerprint approach in micro-EDM drilling is discussed. The results of this research provide a foundation for future in-line process optimization and quality control techniques based on machine learning. |
| first_indexed | 2024-12-23T06:16:33Z |
| format | Article |
| id | doaj.art-b33e6d3fe4944cd990fbb89c42d0ae13 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-12-23T06:16:33Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-b33e6d3fe4944cd990fbb89c42d0ae132022-12-21T17:57:18ZengMDPI AGMicromachines2072-666X2019-04-0110424010.3390/mi10040240mi10040240Process Fingerprint in Micro-EDM DrillingMattia Bellotti0Jun Qian1Dominiek Reynaerts2Department of Mechanical Engineering, KU Leuven, Member Flanders Make, 3001 Leuven, BelgiumDepartment of Mechanical Engineering, KU Leuven, Member Flanders Make, 3001 Leuven, BelgiumDepartment of Mechanical Engineering, KU Leuven, Member Flanders Make, 3001 Leuven, BelgiumThe micro electrical discharge machining (micro-EDM) process is extensively used in aerospace, automotive, and biomedical industries for drilling small holes in difficult-to-machine materials. However, due to the complexity of the electrical discharge phenomena, optimization of the processing parameters and quality control are time-consuming operations. In order to shorten these operations, this study investigates the applicability of a process fingerprint approach in micro-EDM drilling. This approach is based on the monitoring of a few selected physical quantities, which can be controlled in-line to maximize the drilling speed and meet the manufacturing tolerance. A Design of Experiments (DoE) is used to investigate the sensitivity of four selected physical quantities to variations in the processing parameters. Pearson’s correlation is used to evaluate the correlation of these quantities to some main performance and hole quality characteristics. Based on the experimental results, the potential of the process fingerprint approach in micro-EDM drilling is discussed. The results of this research provide a foundation for future in-line process optimization and quality control techniques based on machine learning.https://www.mdpi.com/2072-666X/10/4/240electrical discharge machiningmicro drillingprocess monitoringquality control |
| spellingShingle | Mattia Bellotti Jun Qian Dominiek Reynaerts Process Fingerprint in Micro-EDM Drilling Micromachines electrical discharge machining micro drilling process monitoring quality control |
| title | Process Fingerprint in Micro-EDM Drilling |
| title_full | Process Fingerprint in Micro-EDM Drilling |
| title_fullStr | Process Fingerprint in Micro-EDM Drilling |
| title_full_unstemmed | Process Fingerprint in Micro-EDM Drilling |
| title_short | Process Fingerprint in Micro-EDM Drilling |
| title_sort | process fingerprint in micro edm drilling |
| topic | electrical discharge machining micro drilling process monitoring quality control |
| url | https://www.mdpi.com/2072-666X/10/4/240 |
| work_keys_str_mv | AT mattiabellotti processfingerprintinmicroedmdrilling AT junqian processfingerprintinmicroedmdrilling AT dominiekreynaerts processfingerprintinmicroedmdrilling |