Share Email Print

Proceedings Paper

Autonomous parts assembly: comparison of ART and neocognitron
Author(s): Ryan G. Rosandich; Murat A. Ozbayoglu; Eric W. Roddiger; Cihan H. Dagli
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this paper, we present the performance analysis of three different neural network paradigms, ART-1, ARTMAP inspired ART-1 and Neocognitron, for part recognition in an autonomous assembly system. This intelligent manufacturing system integrates machine vision, neural networks and robotics in order to identify, locate and assemble randomly places components on printed circuit boards requiring precision assembly. The system uses an IBM 7547 robot controlled by an IBM PS/2 computer, a CCD camera and an image capture card. The electronic components are identified and located by using artificial neural networks. The system's component location and identification accuracy are tested on all test components. The results show that the neocognitron-based system performed better than the other two systems.

Paper Details

Date Published: 2 September 1993
PDF: 11 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152548
Show Author Affiliations
Ryan G. Rosandich, Univ. of Missouri/Rolla (United States)
Murat A. Ozbayoglu, Univ. of Missouri/Rolla (United States)
Eric W. Roddiger, Univ. of Missouri/Rolla (United States)
Cihan H. Dagli, Univ. of Missouri/Rolla (United States)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

© SPIE. Terms of Use
Back to Top