Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. T...

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Autores principales:	Kui Fan, Peng Peng, Hongping Zhou, Lulu Wang, Zhongyi Guo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	defect detection ACGAN sample generation multi-algorithm model fusion Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/0c43a5587b5f46199abe50bd1244f9d6
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id	oai:doaj.org-article:0c43a5587b5f46199abe50bd1244f9d6
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spelling	oai:doaj.org-article:0c43a5587b5f46199abe50bd1244f9d62021-11-11T19:15:21ZReal-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion10.3390/s212173041424-8220https://doaj.org/article/0c43a5587b5f46199abe50bd1244f9d62021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7304https://doaj.org/toc/1424-8220Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.Kui FanPeng PengHongping ZhouLulu WangZhongyi GuoMDPI AGarticledefect detectionACGANsample generationmulti-algorithm model fusionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7304, p 7304 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	defect detection ACGAN sample generation multi-algorithm model fusion Chemical technology TP1-1185
spellingShingle	defect detection ACGAN sample generation multi-algorithm model fusion Chemical technology TP1-1185 Kui Fan Peng Peng Hongping Zhou Lulu Wang Zhongyi Guo Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
description	Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.
format	article
author	Kui Fan Peng Peng Hongping Zhou Lulu Wang Zhongyi Guo
author_facet	Kui Fan Peng Peng Hongping Zhou Lulu Wang Zhongyi Guo
author_sort	Kui Fan
title	Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
title_short	Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
title_full	Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
title_fullStr	Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
title_full_unstemmed	Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
title_sort	real-time high-performance laser welding defect detection by combining acgan-based data enhancement and multi-model fusion
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0c43a5587b5f46199abe50bd1244f9d6
work_keys_str_mv	AT kuifan realtimehighperformancelaserweldingdefectdetectionbycombiningacganbaseddataenhancementandmultimodelfusion AT pengpeng realtimehighperformancelaserweldingdefectdetectionbycombiningacganbaseddataenhancementandmultimodelfusion AT hongpingzhou realtimehighperformancelaserweldingdefectdetectionbycombiningacganbaseddataenhancementandmultimodelfusion AT luluwang realtimehighperformancelaserweldingdefectdetectionbycombiningacganbaseddataenhancementandmultimodelfusion AT zhongyiguo realtimehighperformancelaserweldingdefectdetectionbycombiningacganbaseddataenhancementandmultimodelfusion
_version_	1718431567759540224

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

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