Optimization of Spindle loading and Tool Wear for CNC Turning Machine by Using Intelligent System

Abstract

Intelligent knowledge based system (IKBS) is developed for optimizing dry CNC turning process using Taguchi method, CNC Machine, EN19 steel as the work piece material, andCutting Insert. Tool wear and spindle loading which are the machining parameters, spindle speed, feed rate, and depth of cut, areoptimized through the intelligent knowledge based system (IKBS). The experimental CNC turning machine is used to evaluate IKBS. IKBS is developed to determine the effect of the machining parameters such as tool wear and spindle loading. The simultaneous optimization is done by IKBS. Fourlevels of each machining parameter are used inexperimental verification on Model PTC 600, CNC lathe machinetool of PRAGA. The experimental verification designed based on Taguchi's method is used to evaluate the effect of the machining parameters on individual responses of IKBS. The simultaneous optimization is done by intelligent knowledge based system. Theoptimization of complicated multi-performancecharacteristics is simplified through this approach. Tool wear and spindle loading are twocharacteristics on the basis of which the machining parameters, spindle speed, feed rate, loading and depth of cut, areoptimized through IKBS.