Multi-objective Optimization of Turning Performance Characteristics using GA Coupled with AHP based Approach

Abstract

Heat resistive super alloys (HRSAs) which are commonly known as Inconel alloys are extensively used in aeronautical, food processing and automobile industries. The machinability and parametric optimization of Inconel 825 have not been reported much in the literatures. This study attempts to experimentally investigate and optimize the process parameters during machining Inconel 825 for multiple performance characteristics. Spindle speed (N), feed rate (f) and depth of cut (d) are optimized for different responses namely surface roughness (Ra), cutting force (Fz) and metal removal rate (MRR). Feed is found to have the highest influence on Ra and Fz. A mathematical model based on multiple regression analysis is developed for predicting Ra, Fz and MRR. Taguchi analysis is used for optimizing single objective through mean effect plots. For simultaneously optimizing all the responses a weighted combination of objective function is formulated and optimized using genetic algorithm (GA).The optimum parametric combination being 1200 rpm, 0.113 mm/rev and 0.825 mm for N, f and d respectively. In the present work analytical hierarchy processes (AHP) is employed for evaluating weights for each performance measures based on their relative importance. Also, Pareto optimality approach is used for obtaining optimum solutions that produce components with maximum MRR at desired value of Ra is another new contribution of this research. The Pareto optimal solution yields a minimum surface roughness of 1.42µm at N=1204.5 rpm, f=0.124 mm/rev and d=0.503 mm. This is quite lower than the minimum value of 1.6µm obtained experimentally. The NSGA-II result was verified experimentally and the actual surface roughness obtained was1.46µm resulting in an error percentage of 2.8%. The developed approach can be economically applied for the production of quality components from Inconel 825 by industries.