A Comparative Study on Predictive Modeling of Tool Wear using Experimental, Analytical and Numerical Schemes

I.M. Deiab (UAE) and H.A. Kishawy (Canada)


Tool wear, MMCs, Machining, Neural networks.


This paper aims to analyze and model flank tool wear progression when machining MMCs using coated carbide tools. Experimental study was first conducted to understand the influence of different process parameters on the generated tool wear. The experimental values were then compared to prediction obtained from neural network and analytical models. It was observed that the general expected trend of tool wear increase with increased cutting speed was violated at certain cutting speeds. The analytical and numerically predicted results were in reasonable agreement with the measured ones.

Important Links:

Go Back