Surface Roughness Prediction using Artificial Neural Networks when Drilling Udimet 720

Article deals with design of appropriate control strategy for prediction of surface roughness as one of the important indicators of machined surface quality applying artificial intelligence. Test sample was nickel based super alloy UDIMET 720, which is used as material of jet engines components such as discs etc. Experimental data collected from tests were used as input parameters into neural network to identify the sensitivity among cutting conditions, tool wear and monitoring parameters and surface roughness. Selected parameters were used to design a suitable algorithm for control and monitoring of the drilling process. Moreover, the developed software for implementation to machine tool control system for surface roughness on-line identification through monitoring indices is described.