Multi-objective optimisation of grinding process parameters using NSGA-II

Authors:
P. J. Pawar;D. P. Rai-Kalal
Affiliations:
Production Engineering Department, K.K. Wagh Institute of Engineering Education and Research, Nasik, Maharashtra-422003, India;Production Engineering Department, K.K. Wagh Institute of Engineering Education and Research, Nasik, Maharashtra-422003, India
Venue:
International Journal of Metaheuristics
Year:
2013

Citing 3
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization of Surface Grinding Process Using NSGA II

ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Selection of appropriate combination of process parameters in any machining process is a crucial task as it significantly affects the process performance. In the present work, an attempt is made to optimise the process parameters of grinding process. A well-known multi-objective optimisation technique known as non-dominated sorting genetic algorithm II NSGA-II is applied to obtain the optimum values of process variables such wheel speed, work-piece speed, depth of dressing, and lead of dressing in order to improve the process performance in terms of production cost, production rate, and surface finish. Various process constraints such as thermal damage of work-piece, wheel wear, and machine tool stiffness are also taken into account.