A genetic algorithm for optimizing gravity die casting's heat transfer coefficients

M. L. Dennis Wong, William K. S. Pao

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Numerical simulation of solidification has improved our understanding of casting processes significantly over the last two decades. One of the most desirable features in the design of casting of high strength components is directional solidification. Generally, expertise from skilled foundry men is required during the design of casting-mould assembly interrogation in order to achieve a satisfactory thermal control, thus directional solidification. This process is not only costly, both financially and temporally to foundries, it also heavily rely on foundry men's experiences. Our main aim in this project is to explore a novel and fully automated computer scheme that ties the geometric features of the casting with evolutionary algorithms to achieve thermal control. By extracting the medial axes of the casting geometry and correlate it with the interfacial heat transfer coefficient via evolutionary algorithm, we are able to perform non-exhaustive search of the optimized solution. Preliminary results from our computer experiments showed favourable results. In this paper, the focus is sharpened on the convergence and optimality of the developed GA.

Original languageEnglish
Pages (from-to)7076-7080
Number of pages5
JournalExpert Systems with Applications
Volume38
Issue number6
DOIs
Publication statusPublished - Jun 2011

Keywords

  • Die casting
  • Genetic algorithm
  • Heat transfer coefficients
  • Medial Axis Skeletonization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Fingerprint Dive into the research topics of 'A genetic algorithm for optimizing gravity die casting's heat transfer coefficients'. Together they form a unique fingerprint.

  • Cite this