Publication Type:

Journal Article

Source:

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Scienc, Volume 234, Issue 1, p.329-342 (2020)

URL:

https://journals.sagepub.com/doi/full/10.1177/0954406219873932

Keywords:

Flank wear, hard turning, neural network, Response surface methodology

Abstract:

In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time

Cite this Research Publication

T. Mohanraj, SK, T., and Shankar, S., “Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Scienc, vol. 234, no. 1, pp. 329-342, 2020.