Genetic-algorithm-based controlling of microcontact distributions to minimize electrical contact resistance

Noh Sung Kwak, Jongsoo Lee, Yong Hoon Jang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

When two large conductors are in contact over a finite area, the real contact area is determined by the number of clusters of microcontacts where the positions of the clusters are determined by the large-scale waviness of the surface. In addition, the microcontacts are influenced by the small-scale surface roughness. It is widely recognized that the constriction resistance is determined partly by the number and size of the microcontacts and partly by their grouping into clusters. This paper focuses on a parameter study and on the design of the microcontact clusters in terms of the electrical contact resistance (ECR). This paper investigates the positioning and/or sizing optimization of microcontact spots in order to minimize the ECR. The optimal solutions are obtained by a novel method of a real-coded genetic-algorithm implemented with a subpopulation-based selection method and a normal-distribution-probability-based crossover. Also, this paper emphasizes the advantage of the formal optimization method when a total contact area limitation is imposed as a constraint.

Original languageEnglish
Article number6303967
Pages (from-to)1768-1776
Number of pages9
JournalIEEE Transactions on Components, Packaging and Manufacturing Technology
Volume2
Issue number11
DOIs
Publication statusPublished - 2012

Bibliographical note

Funding Information:
Manuscript received October 20, 2010; revised July 9, 2012; accepted August 8, 2012. Date of publication September 14, 2012; date of current version October 30, 2012. The work of Y. H. Jang was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, and Technology under Grant 2009-0087847. Recommended for publication by Associate Editor J. McBride upon evaluation of reviewers’ comments.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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