Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network

doi:10.1007/s10921-021-00823-4

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 水下机器人

	Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network
	Yang, Lei 1,2; Fan, Junfeng 3; Huo, Benyan 1,2; Liu, Yanhong 1,2
刊名	JOURNAL OF NONDESTRUCTIVE EVALUATION
	2021-12-01
卷号	40 期号:4 页码:11
关键词	Welding defect X-ray detection Transfer learning Multi-feature fusion Dempster-Shafer evidence theory
ISSN号	0195-9298
DOI	10.1007/s10921-021-00823-4
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
英文摘要	Robot welding is a basic but indispensable technology for many industries in modern manufacturing. However, many welding parameters affect welding quality. During the real welding process, welding defects are inevitably generated that affect the structural strengths and comprehensive performances of different welding products. Therefore, an accurate welding defect recognition algorithm is necessary for automatic robot welding to assess the effects of defects on structural properties and system maintenance. Much work has been devoted to welding defect recognition. It can be mainly divided into two categories: feature-based and deep learning-based methods. The detection performances of feature-based methods rely on effective image features and strong classifiers. However, faced with weak-textured and weak-contrast welding images, the realization of strong image feature expression still faces a certain challenge. Deep learning-based methods can provide end-to-end detection schemes for welding robots. Nevertheless, an effective deep network model relies on much training data that are not easily collected during real manufacturing. To address the above issues regarding defect detection, a novel welding defect recognition algorithm is proposed based on multi-feature fusion for accurate defect detection based on X-ray images. To improve network training, an effective data augmentation process is proposed to construct the dataset. Combined with transfer learning, the multi-scale features of welding images are acquired for effective feature expression with the pre-trained AlexNet network. On this basis, based on multi-feature fusion, a welding defect recognition algorithm fused to a support vector machine with Dempster-Shafer evidence theory is proposed for multi-scale defect detection. Experiments show that the proposed method achieves a better recognition performance in terms of detecting welding defects than those of other related recognition algorithms.
WOS关键词	NEURAL-NETWORKS ; RECONSTRUCTION ; IDENTIFICATION ; PREDICTION ; TRACKING ; SYSTEM
WOS研究方向	Materials Science
语种	英语
出版者	SPRINGER/PLENUM PUBLISHERS
WOS记录号	WOS:000708883300001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46218]
专题	复杂系统管理与控制国家重点实验室_水下机器人
通讯作者	Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China 2.Robot Percept & Control Engn Lab Henan Prov, Zhengzhou 450001, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Fan, Junfeng,Huo, Benyan,et al. Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network[J]. JOURNAL OF NONDESTRUCTIVE EVALUATION,2021,40(4):11.
APA	Yang, Lei,Fan, Junfeng,Huo, Benyan,&Liu, Yanhong.(2021).Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network.JOURNAL OF NONDESTRUCTIVE EVALUATION,40(4),11.
MLA	Yang, Lei,et al."Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network".JOURNAL OF NONDESTRUCTIVE EVALUATION 40.4(2021):11.