Title: Neural network based prediction of drill wear from theoretically analysed and experimentally measured values of thrust force and torque
Authors: Karali Patra, Kingshook Bhattacharyya, Surjya K. Pal
Addresses: School of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore. ' Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur-721302, India. ' Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
Abstract: This work introduces a new approach of drill wear monitoring by combining model-based and experimental values of thrust force and torque. The drill wear prediction performance of the neural network using the difference of experimental and theoretical values of thrust force and torque is shown to be better than the performance of the same using only experimental values. Experimental data were acquired from strain gauge type dynamometer during drilling on mild steel workpiece with high-speed steel (HSS) drill bits. In this work, Williams| orthogonal model has been implemented for the theoretical prediction of thrust force and torque in drilling under different cutting conditions.
Keywords: drill wear; dynamometer; torque; thrust force; analytical modelling; regression analysis; artificial neural networks; ANNs; wear monitoring; tool wear; mild steel; high-speed steel; HSS.
International Journal of Machining and Machinability of Materials, 2009 Vol.5 No.2/3, pp.207 - 231
Published online: 20 Feb 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article