Title: CBN tool flank wear modelling using Hybrid Neural Network

Authors: Xiaoyu Wang, Yong Huang, Nhan Nguyen, Kalmanje Krishnakumar

Addresses: Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921, USA. ' Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921, USA. ' Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA 94035, USA. ' Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA 94035, USA

Abstract: Accurate tool wear modelling is indispensable for successful hard turning technology implementation. In this study, a Hybrid Neural Network-based modelling approach, which integrates an analytical tool wear model and an artificial neural network, is proposed to predict Cubic Boron Nitride (CBN) tool flank wear in turning hardened 52100 bearing steel. Extended Kalman Filter algorithm is used to train the proposed neural network, and the network connectivity is further optimised to achieve an improved and robust modelling performance. Results show that the proposed Hybrid Neural Network excels the analytical tool wear model approach and the general neural network-based modelling approach.

Keywords: tool wear; hard turning; hybrid neural networks; HNNs; extended Kalman filter; EKF; connectivity optimisation; CBN tooling; flank wear; wear modelling; artificial neural networks; ANNs; bearing steel.

DOI: 10.1504/IJMMS.2008.018280

International Journal of Mechatronics and Manufacturing Systems, 2008 Vol.1 No.1, pp.83 - 102

Published online: 14 May 2008 *

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