Experimental investigations and empirical modeling for optimization of surface roughness and machining time parameters in micro end milling using Genetic Algorithm

Surface finish is a predominant requirement of a micro part inorder to perform satisfactory function. It is dependent on process variables such as cutting conditions, tool and work material properties, etc. In this work, an effort has been taken to propose realistic machining conditions for process improvement in micro end milling for C360 Copper alloy material. Solid Tungsten Carbide flat end mill cutter of size 700 mu m and 800 mu m are chosen as the tool material. Response surface methodology was incorporated for Design of experiments. First, experimental investigation was carried out to examine the effect of process condition include spindle speed and feed rate on Arithmetic Average Surface Roughness (R-a) and machining time values and also uncertainty in measured values. Analysis of variance was performed to establish the significant effect of cutting conditions on response values. Empirical model has been developed by experimental results using regression techniques in order to frame the fitness function. Parameters optimization for fine surface finish with minimum machining time has been carried out using Genetic Algorithm (GA). Confirmation experiments were carried out to validate the correctness of GA result and micro channels are fabricated successfully.