Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling
High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/32785/ http://irep.iium.edu.my/32785/9/SCN_0010.jpg http://irep.iium.edu.my/32785/1/icom13brochure.pdf http://irep.iium.edu.my/32785/2/2180conference_iium_mechatroniuc_2013.pdf |
| _version_ | 1848780624347791360 |
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| author | Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas ., Muhammad Reza |
| author_facet | Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas ., Muhammad Reza |
| author_sort | Al Hazza, Muataz Hazza Faizi |
| building | IIUM Repository |
| collection | Online Access |
| description | High speed milling has many advantages such as higher removal rate and
high
productivity. However, higher cutting speed increase the flank wear rate and thus reducing the
cutting
tool life. Therefore estimating and predicting the flank wear length in early stages
reduces the risk of unaccepted tooling cost. This research presents a neural network model for
predicting and simulating the flank wear in the CNC end milling process. A
set of sparse
experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been
conducted to measure the flank wear length. Then the measured data have been used to train
the developed neural network model. Artificial neural network (ANN
) was applied to predict
the flank wear length. The neural network contains twenty hidden layer with feed forward back
propagation hierarchical. The neural network has been designed with
MATLAB
Neural
Network Toolbox. The results show a high correlation be
tween the predicted and the observed
flank wear which indicates the validity of the models. |
| first_indexed | 2025-11-14T15:36:38Z |
| format | Proceeding Paper |
| id | iium-32785 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T15:36:38Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-327852013-11-19T01:43:40Z http://irep.iium.edu.my/32785/ Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas ., Muhammad Reza T Technology (General) High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN ) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation be tween the predicted and the observed flank wear which indicates the validity of the models. 2013-07 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/32785/9/SCN_0010.jpg application/pdf en http://irep.iium.edu.my/32785/1/icom13brochure.pdf application/pdf en http://irep.iium.edu.my/32785/2/2180conference_iium_mechatroniuc_2013.pdf Al Hazza, Muataz Hazza Faizi and Adesta, Erry Yulian Triblas and ., Muhammad Reza (2013) Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling. In: 5th International Conference on Mechatronics (ICOM'13), 2 – 4 July 2013, Kuala Lumpur, Malaysia. (Unpublished) |
| spellingShingle | T Technology (General) Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas ., Muhammad Reza Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling |
| title | Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling |
| title_full | Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling |
| title_fullStr | Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling |
| title_full_unstemmed | Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling |
| title_short | Flank wears simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling |
| title_sort | flank wears simulation by using back propagation neural network when cutting hardened h-13 steel in cnc end milling |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/32785/ http://irep.iium.edu.my/32785/9/SCN_0010.jpg http://irep.iium.edu.my/32785/1/icom13brochure.pdf http://irep.iium.edu.my/32785/2/2180conference_iium_mechatroniuc_2013.pdf |