Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel

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...

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Main Authors: Mebrahitom, A., Seow , Xiang, Azmir, Azhari, Tamiru, A.
Format: Conference or Workshop Item
Language:English
English
Published: American Society of Mechanical Engineers (ASME) 2017
Subjects:
TS Manufactures
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
id ump-19269
recordtype eprints
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
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TS Manufactures
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
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.
format Conference or Workshop Item
author Mebrahitom, A.
Seow , Xiang
Azmir, Azhari
Tamiru, A.
author_facet Mebrahitom, A.
Seow , Xiang
Azmir, Azhari
Tamiru, A.
author_sort Mebrahitom, A.
title Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
title_short Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
title_full Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
title_fullStr Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
title_full_unstemmed Remaining Tool Life Prediction Based On Force Sensors Signals During End Milling Of Stavax ESR Tool Steel
title_sort remaining tool life prediction based on force sensors signals during end milling of stavax esr tool steel
publisher American Society of Mechanical Engineers (ASME)
publishDate 2017
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
first_indexed 2023-09-18T22:27:38Z
last_indexed 2023-09-18T22:27:38Z
_version_ 1777416072620670976

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