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: | Conference or Workshop Item |
| 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 |
| Summary: | 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. |
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