Gate positioning design of injection mould using bi-objective micro genetic algorithm

J. Lee, J. Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The use of a micro genetic algorithm (mGA)-based approach to solve a bi-objective optimization of an injection mould design problem is presented. The advantage of the mGA-based approach is that it requires fewer computational resources than a conventional GA because it has a smaller population than a conventional GA. The main drawback of the mGAbased approach is that design diversity is not secured when multi-modal and multi-objective designs are investigated. To implement the mGA-based bi-objective optimization procedure, the present study proposes a memory set, a filtering process, weight control, and reproduction from the memory set in order to explore new optimal solutions, and identify more-evenly distributed Pareto surfaces. A number of mathematical functions and a typical structural optimization problem are tested to verify the proposed strategies. The approach is subsequently applied to the bi-objective injection moulding design problem of minimizing both the maximum injection pressure and maximum pressure difference between the gate positions in the runner system.

Original languageEnglish
Pages (from-to)687-699
Number of pages13
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume222
Issue number6
DOIs
Publication statusPublished - 2008 Aug 4

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Genetic algorithms
Weight control
Data storage equipment
Structural optimization
Injection molding

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

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