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2019 | OriginalPaper | Chapter

Using a Hamming Neural Network to Predict Bond Strength of Welded Connections

Authors : Vitaliy Klimov, Alexey Klimov, Sergey Mkrtychev

Published in: Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

The paper deals with using a Hamming neural network to predict the limiting destruction force under load of a welded connection in shear.

We propose an algorithm for encoding information on dynamic resistance into bipolar signals required for a Hamming neural network tuning and operation.

A computer program that implements a neural network based on the proposed algorithm has been developed. The results of the neural network training and testing are presented. The method proposed in the paper complies with the requirements of ISO 9000:2015 standard for continuous monitoring and documentation of each welded connection. The analysis showed that relative prediction error of the destruction force of a weld does not exceed 10%. Thus, the possibility of using a Hamming neural network to predict bond strength of welded connections based on dynamic resistance of the welding zone has been confirmed. The work was supported by the Russian Foundation of Basic Research (Grant Agreement 15-08-03125 A).

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Title: Using a Hamming Neural Network to Predict Bond Strength of Welded Connections
Authors: Vitaliy Klimov
Alexey Klimov
Sergey Mkrtychev
Publisher: Springer International Publishing
Book: Artificial Intelligence
Print ISBN: 978-3-030-30762-2

Electronic ISBN: 978-3-030-30763-9

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30763-9_6

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