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Proceedings Paper

GARP: genetic algorithm for part packing in a rapid prototyping machine
Author(s): Ilkka T. Ikonen; William E. Biles; James E. Lewis; Anup Kumar; Rammohan K. Ragade
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Paper Abstract

A unique 3D packing problem with non-convex parts and without a gravity constraint can be defined in a selective laser sintering rapid prototyping machine. The goal for the packing task is to pack the parts to be manufactured as tightly as possible to maximize volume and machine time utilization. A genetic algorithm is used as a search engine to find a good packing pattern for parts. Each individual in a population represents one packing solution. The chromosomal representation is a 3D ordered list of integers where each sublist has a different allele set. A fitness function simulates the packing of parts and also evaluates the quality of a solution. To calculate part intersections, the fitness function uses methods common in computational geometry. Due to the chromosome structure used, there is a lack of genetic material in the population. Methods to introduce new material into the population are defined and tested. Experiments with more difficult packing problems, where all parts are complex in shape, prove that the developed genetic algorithm is robust and able to find a good solution in most problem instances.

Paper Details

Date Published: 9 October 1998
PDF: 9 pages
Proc. SPIE 3517, Intelligent Systems in Design and Manufacturing, (9 October 1998); doi: 10.1117/12.326945
Show Author Affiliations
Ilkka T. Ikonen, Univ. of Louisville (United States)
William E. Biles, Univ. of Louisville (United States)
James E. Lewis, Univ. of Louisville (United States)
Anup Kumar, Univ. of Louisville (United States)
Rammohan K. Ragade, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 3517:
Intelligent Systems in Design and Manufacturing
Bhaskaran Gopalakrishnan; San Murugesan, Editor(s)

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