Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
This paper focuses on the prediction of the Remaining Useful Life (RUL) of a carbide insert end mill. As tool life degradation due to wear is the main limitation to machining productivity and part quality, prediction and periodic assessment of the condition of the tool is very helpful for the machin...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
American Society of Mechanical Engineers (ASME)
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19269/ http://umpir.ump.edu.my/id/eprint/19269/1/Remaining%20tool%20Life%20prediction%20based%20on%20force%20sensors%20signals%20during%20end%20milling%20of%20Stavax%20ESR%20tool%20steel.pdf http://umpir.ump.edu.my/id/eprint/19269/7/Remaining%20tool%20Life%20prediction%20based%20on%20force%20sensors%20signals%20during%20end%20milling%20of%20Stavax%20ESR%20tool%20steel%201.pdf |
| _version_ | 1848820865924333568 |
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| author | Mebrahitom, A. Seow , Xiang Azmir, Azhari Tamiru, A. |
| author_facet | Mebrahitom, A. Seow , Xiang Azmir, Azhari Tamiru, A. |
| author_sort | Mebrahitom, A. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper focuses on the prediction of the Remaining Useful Life (RUL) of a carbide insert end mill. As tool life degradation due to wear is the main limitation to machining productivity and part quality, prediction and periodic assessment of the condition of the tool is very helpful for the machining industry. The RUL prediction of tools is demonstrated based on the force sensor signal values using the Support Vector Regression (SVR) method and Neural Network (NN) techniques. End milling tests were performed on a stainless steel workpiece at constant machining parameters and the cutting force signal data was collected using force dynamometer for feature extraction and further analysis. Both the SVR and NN models were compared based on the same set of experimental data for the prediction performance. Results have shown a good agreement between the predicted and actual RUL of the tools for both models. The difference in the level of the prognostic matrices such as accuracy, precision and prediction horizon for both models was discussed. |
| first_indexed | 2025-11-15T02:16:15Z |
| format | Conference or Workshop Item |
| id | ump-19269 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:16:15Z |
| publishDate | 2017 |
| publisher | American Society of Mechanical Engineers (ASME) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-192692018-02-08T03:38:07Z http://umpir.ump.edu.my/id/eprint/19269/ Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel Mebrahitom, A. Seow , Xiang Azmir, Azhari Tamiru, A. TS Manufactures This paper focuses on the prediction of the Remaining Useful Life (RUL) of a carbide insert end mill. As tool life degradation due to wear is the main limitation to machining productivity and part quality, prediction and periodic assessment of the condition of the tool is very helpful for the machining industry. The RUL prediction of tools is demonstrated based on the force sensor signal values using the Support Vector Regression (SVR) method and Neural Network (NN) techniques. End milling tests were performed on a stainless steel workpiece at constant machining parameters and the cutting force signal data was collected using force dynamometer for feature extraction and further analysis. Both the SVR and NN models were compared based on the same set of experimental data for the prediction performance. Results have shown a good agreement between the predicted and actual RUL of the tools for both models. The difference in the level of the prognostic matrices such as accuracy, precision and prediction horizon for both models was discussed. American Society of Mechanical Engineers (ASME) 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19269/1/Remaining%20tool%20Life%20prediction%20based%20on%20force%20sensors%20signals%20during%20end%20milling%20of%20Stavax%20ESR%20tool%20steel.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/19269/7/Remaining%20tool%20Life%20prediction%20based%20on%20force%20sensors%20signals%20during%20end%20milling%20of%20Stavax%20ESR%20tool%20steel%201.pdf Mebrahitom, A. and Seow , Xiang and Azmir, Azhari and Tamiru, A. (2017) Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel. In: International Mechanical Engineering Congress and Exposition (IMECE) , 3-9 November 2017 , Swiss Garden Hotel, Balok, Kuantan, Pahang. pp. 1-7.. ISBN 9780791858356 (Published) |
| spellingShingle | TS Manufactures Mebrahitom, A. Seow , Xiang Azmir, Azhari Tamiru, A. Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel |
| title | Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
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| title_full | Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
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| title_fullStr | Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
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| title_full_unstemmed | Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
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| title_short | Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
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| title_sort | remaining tool life prediction based on force sensors signals during end milling of stavax esr tool steel |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/19269/ http://umpir.ump.edu.my/id/eprint/19269/1/Remaining%20tool%20Life%20prediction%20based%20on%20force%20sensors%20signals%20during%20end%20milling%20of%20Stavax%20ESR%20tool%20steel.pdf http://umpir.ump.edu.my/id/eprint/19269/7/Remaining%20tool%20Life%20prediction%20based%20on%20force%20sensors%20signals%20during%20end%20milling%20of%20Stavax%20ESR%20tool%20steel%201.pdf |