Authors: Alakesh Manna, Sandeep M. Salodkar
Addresses: Department of Mechanical Engineering, Punjab Engineering College, Deemed University, Chandigarh-160012, India. ' Department of Mechanical Engineering, Punjab Engineering College, Deemed University, Chandigarh-160012, India
Abstract: This paper describes predictive machining approach with fuzzy neural network (FNN) modelling of the cutting tool flank wear in order to estimate the performance of CNMG 12 04 08 E-M 6630 insert during turning of EN-31 alloy steel. In the present work, a new approach for cutting tool wear detection with cutting conditions estimated wear through acoustic emission (AE) signal is presented. The measured tool wear and estimated tool wear by conditions monitoring and detected signals are compared and graphically analysed. Investigated results prove that the new method of FNN is reliable and appropriate to control and monitor the cutting tool wear.
Keywords: fuzzy neural networks; FNNs; EN-31 alloy steel; flank wear; acoustic emission; fuzzy logic; predictive machining; modelling; cutting tools; tool wear; condition monitoring; wear monitoring.
International Journal of Machining and Machinability of Materials, 2010 Vol.8 No.1/2, pp.76 - 86
Published online: 05 Aug 2010 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article