Artificial neural network implementation in machining & manufacturing

The main aim of the study was to evaluate temperature influence in cutting zone on the forms of chip shapes during turning of Steel 30CrNiMo8. It is very important to use optimal machining parameters to get the best production results or to high control of the machining process. Temperature distribution in cutting zone during machining process could affect the forms of chip shapes and surface roughness as well. Surface roughness could be considered as the most important indicator for the machining process quality. Since many machining parameters could be changed in order to obtain optimal machining condition in this study the main aim was to determine the most important parameters of the process. Neuro-fuzzy approach was applied since it is highly suitable approach for the nonlinear system with many different parameters. It is found that spot temperature is more influential than maximal temperature during the machining process.