Artificial Neural Network Approach In Printed Circuit Board Assembly

Author

Mahesh Vellanki
Cihan H. Dagli, Missouri University of Science and TechnologyFollow

Abstract

In this study, automation of the circuit board assembly process is considered using artificial neural networks with knowledge-based systems. Basic issues in achieving intelligent control that can adapt to changing conditions in the assembly process are discussed. The feasibility of using neural networks for pattern recognition and optimum component insertion sequence generation is examined. The study provides a basic foundation for designing a conceptual architecture for adaptive intelligent control of circuit board assembly. Real-time testing of component recognition is conducted using adaptive resonance theory (ART 1) as a neural network paradigm. © 1993 Chapman & Hall.

Recommended Citation

M. Vellanki and C. H. Dagli, "Artificial Neural Network Approach In Printed Circuit Board Assembly," Journal of Intelligent Manufacturing, vol. 4, no. 1, pp. 109 - 119, Springer, Feb 1993.

The definitive version is available at https://doi.org/10.1007/BF00124984

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

artificial neural networks; Automated assembly; electronic parts manufacturing

International Standard Serial Number (ISSN)

1572-8145; 0956-5515

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Springer, All rights reserved.

Publication Date

01 Feb 1993