TY - JOUR
TI - Monitoring and neural network modeling of cutting temperature during turning hard steel
AU - Tarić Mirfad R
AU - Kovač Pavel P
AU - Nedić Bogdan P
AU - Rodić Dragan Đ
AU - Ješić Dušan D
JN - Thermal Science
PY - 2018
VL - 22
IS - 6
SP - 2605
EP - 2614
PT - Article
AB - In this study, cutting tools average temperature was investigated by using  thermal imaging camera of FLIR E50-type. CBN inserts with zero and negative  rake angles were taken as cutting tools and round bar of EN 90MnCrV8 hardened  steel was used as the workpiece. Since the life of the cutting tool material  strongly depends upon cutting temperature, it is important to predict heat  generation in the tool with intelligent techniques. This paper proposes a  method for the identification of cutting parameters using neural network. The  model for determining the cutting temperature of hard steel, was trained and  tested by using the experimental data. The test results showed that the  proposed neural network model can be used successfully for machinability data  selection. The effect on the cutting temperature of machining parameters and  their interactions in machining were analyzed in detail and presented in this  study.