Autonomous parts assembly: comparison of ART and neocognitron

Ryan G. Rosandich; Murat A. Ozbayoglu; Eric W. Roddiger; Cihan H. Dagli

doi:10.1117/12.152548

2 September 1993 Autonomous parts assembly: comparison of ART and neocognitron

Ryan G. Rosandich, Murat A. Ozbayoglu, Eric W. Roddiger, Cihan H. Dagli

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152548
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Ryan G. Rosandich, Murat A. Ozbayoglu, Eric W. Roddiger, and Cihan H. Dagli "Autonomous parts assembly: comparison of ART and neocognitron", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152548

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