دورية أكاديمية
A comparison of four machine learning techniques and continuous wavelet transform approach for detection and classification of tool breakage during milling process
| العنوان: | A comparison of four machine learning techniques and continuous wavelet transform approach for detection and classification of tool breakage during milling process |
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| المؤلفون: | Demir, Habibe Gürsoy, Yeşilyurt, İsa |
| المساهمون: | Havacılık ve Uzay Bilimleri Fakültesi -- Havacılık ve Uzay Mühendisliği Bölümü, Demir, Habibe Gürsoy |
| بيانات النشر: | Canadian Science Publishing |
| سنة النشر: | 2022 |
| المجموعة: | DSpace@ISTE (Iskenderun Technical University Institutional Repository) |
| مصطلحات موضوعية: | Detection, Tool breakage, Milling, Neural network, Ranrandom forest, SVM, Continuous wavelet transform, Engineering, Support vector machine |
| الوصف: | In machining, the tool condition has to be monitored by condition monitoring techniques to prevent damage by the use of tools and the workpiece. Cutting forces acting on the tool between zero and maximum values cause the cutting edge to crack and break. Predetection of this situation in the cutting tool is very important to prevent any negative situation that may occur. This study introduces a vibration-based intelligent tool condition monitoring technique to detect involute form cutter faults such as tool breakage at different levels during gear production on a milling machine. Machine learning algorithms such as artificial neural network, random forest, support vector machine, and K-nearest neighbor were used to detect the broken teeth and its level of breakage. According to the results obtained, it was observed that all the algorithms are successful in detecting faults in different teeth; also they have identification advantages according to different fault levels. In addition, the time and frequency domain analysis and continuous wavelet transform were used to determine the local faults. The developed machine learning-based detection performances compared the classical time and frequency domain analyses and continuous wavelet transform to prove the effectiveness and precision of the proposed methods. The results showed that all of the machine learning techniques have satisfactory performance to be used as fast and precise detection tools without complex calculations for detecting tool breakage. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | Transactions of the Canadian Society for Mechanical Engineering; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; Web of Science; Web of Science Core Collection - Science Citation Index Expanded; https://doi.org/10.1139/tcsme-2022-0052Test; https://hdl.handle.net/20.500.12508/2347Test |
| DOI: | 10.1139/tcsme-2022-0052 |
| الإتاحة: | https://doi.org/20.500.12508/2347Test https://doi.org/10.1139/tcsme-2022-0052Test https://hdl.handle.net/20.500.12508/2347Test |
| حقوق: | info:eu-repo/semantics/closedAccess |
| رقم الانضمام: | edsbas.2EE5C530 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1139/tcsme-2022-0052 |
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