OPTIMIZATION OF CUTTING PARAMETERS ON MILD STEEL WITH HSS & CEMENTED CARBIDE TIPPED TOOLS USING ANN

Optimum Selection of cutting conditions importantly contribute to the increase of productivity and the reduction of cost, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network based approach to complex optimization of cutting parameters is proposed. To reach higher precision of the predicted results a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. Surface roughness, an indicator of surface quality is one of the most specified customer requirements in a machining process. To predict the surface roughness, an Artificial Neural Network (ANN) model was designed through back propagation network using MATLAB 7.1 software for the data obtained.