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Journal of Materials Engineering and Performance

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23-01-2022 | Technical Article

Classification of Weld Seam Width Based on Detrended Fluctuation Analysis, t-Distributed Stochastic Neighbor Embedding, and Support Vector Machine

Authors: Yong Huang, Dongqing Yang, Lei Wang, Gu Jieren, Zhang Xiaoyong, Kehong Wang

Published in: Journal of Materials Engineering and Performance | Issue 5/2022

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Abstract

The droplet behavior of welding is chaotic and fractal, and thus is significant for diagnosis of weld quality. To study the long-range correlation of fractals, detrended fluctuation analysis (DFA) is introduced for current and voltage signals. The DFA curve obviously has crossover and can be expressed by a two exponent model, including a short-term exponent (α1) at small scale and a long-term exponent (α2) at large scale. However, the relationship between the weld seam width and the two exponent model is not obviously linear. A high-dimensional feature is generated on basis of all points of the DFA curve, t-distributed stochastic neighbor embedding is used for dimension reduction. Then, a low-dimensional feature combined with support vector machine is used to predict weld seam width, which achieves higher classification accuracy than the two exponent model. This study provides a new attempt about the chaotic and fractal characteristics in welding.

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Title: Classification of Weld Seam Width Based on Detrended Fluctuation Analysis, t-Distributed Stochastic Neighbor Embedding, and Support Vector Machine
Authors: Yong Huang
Dongqing Yang
Lei Wang
Gu Jieren
Zhang Xiaoyong
Kehong Wang
Publication date: 23-01-2022
Publisher: Springer US
Published in: Journal of Materials Engineering and Performance / Issue 5/2022
Print ISSN: 1059-9495
Electronic ISSN: 1544-1024
DOI: https://doi.org/10.1007/s11665-021-06458-w

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