Open AccessComputer diagnostics of resistance spot welding based on Hamming neural network
Author(s) -
V. S. Klimov,
А. С. Климов,
S. V. Mkrtychev
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1333/4/042015
Subject(s) - artificial neural network , spot welding , welding , hamming code , computer science , reliability (semiconductor) , software , artificial intelligence , algorithm , engineering , mechanical engineering , decoding methods , programming language , power (physics) , physics , quantum mechanics , block code
The article deals with utilizing the method of qualitative assessment of welding zone dynamic resistance based on a Hamming neural network to increase versatility and reliability of computer diagnostics for resistance spot welding. We propose a mechanism for encoding information on dynamic resistance into bipolar signals required for the neural network tuning and operation. The algorithm of welding diagnostics was developed and implemented with specialized software. The results of the neural network training and testing are presented. As the analysis shows, the relative error in predicting destruction force does not exceed 10%. The approach proposed in this article complies with the requirements of ISO 9000:2015 standard for continuous monitoring and documentation of each welded connection and allows for increased accuracy of computer diagnostics of welds.