Modelling Metal Cutting Parameters Using Intelligent Techniques

Cutting temperature, which depends on many factors, has a significant and, MOWN negative influence on cutting process parameters. On the other hand the quality of the machined surface is one of the most important qualitative indicators of a cutting process. Both parameters cannot be omitted in the modeling of Metal cutting. Due to the high complexity of the process itself, it is almost impossible to encompass all the relevant factors and their influence within a mathematical formula. In such cases, it is much more efficient to use and process data obtained through experiments. Nowadays systems that are based on artificial intelligence are often used for this purpose. The paper presents the application of the artificial neural networks and hybrid, neuro-fuzzy model in the Prediction of a workpiece temperature and surface roughness. The approach is based on the thermographic method and infra red camera imaging system. (C) 2010 Journal of Mechanical Engineering. All rights reserved.