TY - GEN
T1 - Tool wear prediction using evolutionary Dynamic Fuzzy Neural (EDFNN) Network
AU - Pratama, Mahardhika
AU - Er, Meng Joo
AU - Li, Xiang
AU - Gan, Oon Peen
AU - Oentaryo, Richad J.
AU - Linn, San
AU - Zhai, Lianyin
AU - Arifin, Imam
PY - 2011
Y1 - 2011
N2 - In development of self-organizing fuzzy neural network, selection of optimal parameters is one of the key issues. This is especially so for a system with more than 10 parameters whereby it will be challenging for expert users to determine the optimal parameters. This paper presents a hybrid Dynamic Fuzzy Neural Network (DFNN), and Genetic Algorithm (GA) termed Evolutionary Dynamic Fuzzy Neural Network (EDFNN) for the prediction of tool wear of ball nose end milling process. GA, well known for its powerful search method, is implemented to obtain optimal parameters of DFNN, so as to circumvent the complex time varying property without prior knowledge or exhaustive trials. Degradation of machine tools in ball nose end milling process is highly non-linear and time varying. Benchmarked again original DFNN in the experimental study, EDFNN demonstrates the effectiveness and versatility of proposed algorithm which not only produces higher prediction accuracy, and faster training time, but also serves to more compact and parsimonious network structure.
AB - In development of self-organizing fuzzy neural network, selection of optimal parameters is one of the key issues. This is especially so for a system with more than 10 parameters whereby it will be challenging for expert users to determine the optimal parameters. This paper presents a hybrid Dynamic Fuzzy Neural Network (DFNN), and Genetic Algorithm (GA) termed Evolutionary Dynamic Fuzzy Neural Network (EDFNN) for the prediction of tool wear of ball nose end milling process. GA, well known for its powerful search method, is implemented to obtain optimal parameters of DFNN, so as to circumvent the complex time varying property without prior knowledge or exhaustive trials. Degradation of machine tools in ball nose end milling process is highly non-linear and time varying. Benchmarked again original DFNN in the experimental study, EDFNN demonstrates the effectiveness and versatility of proposed algorithm which not only produces higher prediction accuracy, and faster training time, but also serves to more compact and parsimonious network structure.
KW - DFNN
KW - Genetic Algorithm
KW - ball nose end milling process
KW - tool wear prediction
UR - https://www.scopus.com/pages/publications/84863081061
U2 - 10.1109/IECON.2011.6119997
DO - 10.1109/IECON.2011.6119997
M3 - Conference contribution
AN - SCOPUS:84863081061
SN - 9781612849720
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 4739
EP - 4744
BT - Proceedings
T2 - 37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011
Y2 - 7 November 2011 through 10 November 2011
ER -