Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes
In metal cutting processes, tool condition monitoring (TCM) plays an important role in maintaining the quality of surface finishing. Monitoring of tool wear in order to prevent surface damage is one of the difficult tasks in the context of TCM. Through early detection, high quality surface finishing...
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/101096 http://hdl.handle.net/10220/16312 |
| _version_ | 1811693020278423552 |
|---|---|
| author | Ge, H. Huang, S. Torabi, Amin J. Li, X. Er, Meng Joo Gan, Oon Peen Zhai, Lian yin San, Linn |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ge, H. Huang, S. Torabi, Amin J. Li, X. Er, Meng Joo Gan, Oon Peen Zhai, Lian yin San, Linn |
| author_sort | Ge, H. |
| collection | NTU |
| description | In metal cutting processes, tool condition monitoring (TCM) plays an important role in maintaining the quality of surface finishing. Monitoring of tool wear in order to prevent surface damage is one of the difficult tasks in the context of TCM. Through early detection, high quality surface finishing and near-zero loss for potential failures can be ensured. Real-time/online tool degradation detection by using machine learning is highly desired. The ability to predict the tool wear, which is related to the remaining useful life of a tool, will improve efficiency and optimize tool usage while ensuring the quality of the work piece produced. In this paper, examine two popular methods of machine learning, namely the Adaptive Network Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) are used to estimate the tool wear and correlation models for tool wear estimation using ANFIS and SVM are estimated. A case study for six sets of ball nose cutters in a high speed milling machining process of Inconel 718 is carried out. Comparative studies of the two methods are carried out and experimental results analysed and discussed. In turns out that the accuracy of the ANFIS is generally better than the SVM whereas SVM is much faster than ANFIS in terms of speed. |
| first_indexed | 2024-10-01T06:45:02Z |
| format | Conference Paper |
| id | ntu-10356/101096 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:45:02Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1010962020-03-07T13:24:50Z Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes Ge, H. Huang, S. Torabi, Amin J. Li, X. Er, Meng Joo Gan, Oon Peen Zhai, Lian yin San, Linn School of Electrical and Electronic Engineering Annual Conference on IEEE Industrial Electronics Society (38th : 2012 : Montreal, Canada) DRNTU::Engineering::Electrical and electronic engineering In metal cutting processes, tool condition monitoring (TCM) plays an important role in maintaining the quality of surface finishing. Monitoring of tool wear in order to prevent surface damage is one of the difficult tasks in the context of TCM. Through early detection, high quality surface finishing and near-zero loss for potential failures can be ensured. Real-time/online tool degradation detection by using machine learning is highly desired. The ability to predict the tool wear, which is related to the remaining useful life of a tool, will improve efficiency and optimize tool usage while ensuring the quality of the work piece produced. In this paper, examine two popular methods of machine learning, namely the Adaptive Network Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) are used to estimate the tool wear and correlation models for tool wear estimation using ANFIS and SVM are estimated. A case study for six sets of ball nose cutters in a high speed milling machining process of Inconel 718 is carried out. Comparative studies of the two methods are carried out and experimental results analysed and discussed. In turns out that the accuracy of the ANFIS is generally better than the SVM whereas SVM is much faster than ANFIS in terms of speed. 2013-10-10T01:17:31Z 2019-12-06T20:33:20Z 2013-10-10T01:17:31Z 2019-12-06T20:33:20Z 2012 2012 Conference Paper Li, X., Er, M. J., Ge, H., Gan, O. P., Huang, S., Zhai, L. Y., Linn, S., & Torabi, A. J. (2012). Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp.2821-2826. https://hdl.handle.net/10356/101096 http://hdl.handle.net/10220/16312 10.1109/IECON.2012.6389448 en |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Ge, H. Huang, S. Torabi, Amin J. Li, X. Er, Meng Joo Gan, Oon Peen Zhai, Lian yin San, Linn Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| title | Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| title_full | Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| title_fullStr | Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| title_full_unstemmed | Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| title_short | Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| title_sort | adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/101096 http://hdl.handle.net/10220/16312 |
| work_keys_str_mv | AT geh adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT huangs adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT torabiaminj adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT lix adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT ermengjoo adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT ganoonpeen adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT zhailianyin adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses AT sanlinn adaptivenetworkfuzzyinferencesystemandsupportvectormachinelearningfortoolwearestimationinhighspeedmillingprocesses |