Evolutionary multi-objective optimization and decision making for selective laser sintering

Authors:
Nikhil Padhye;Kalyanmoy Deb
Affiliations:
Indian Institute of Technology Kanpur, Kanpur, India;Indian Institute of Technology Kanpur, Kanpur, India
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 8
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Some Methods for Nonlinear Multi-objective Optimization

EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers

ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Topology optimization of compliant mechanism using multi-objective particle swarm optimization

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Comparison of archiving methods in multi-objectiveparticle swarm optimization (MOPSO): empirical study

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Rapid prototyping using evolutionary approaches: part 1

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Rapid prototyping using evolutionary approaches: part 2

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Empirical comparison of MOPSO methods: guide selection and diversity preservation

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an integrated approach to arrive at optimal build orientations, simultaneously minimizing surface roughness `Ra' and build time `T', for object manufacturing in SLS process. The optimization task is carried out by two popularly known multi-objective evolutionary optimizers - NSGA-II (non-dominated sorting genetic algorithm) and MOPSO (multi-objective particle swarm optimizer). The performance comparison of these two optimizers along with an approximation of Pareto-optimal front is done using two statistically significant performance measures. Three proposals addressing the task of decision making, i.e. selecting one solution in presence of multiple trade-off solutions, are introduced to facilitate the designer. The overall procedure is integrated into MORPE - Multi-objective Rapid Prototyping Engine. Several sample objects are considered for experimentation to demonstrate the working of MORPE. A careful study of optimal build directions for several components indicates a trend, providing insight into the SLS processes which can be regarded highly useful for various practical RP applications.