Zhao Lun, Sen Lin, YunLong Pang, HaiBo Wang, Zeshan Abbas, ZiXin Guo, XiaoLe Huo, Seng Wang
{"title":"基于深度卷积神经网络的自穿孔铆钉接头缺陷检测方法","authors":"Zhao Lun, Sen Lin, YunLong Pang, HaiBo Wang, Zeshan Abbas, ZiXin Guo, XiaoLe Huo, Seng Wang","doi":"10.1115/1.4063748","DOIUrl":null,"url":null,"abstract":"Abstract The self-pierce riveting process for alloy materials has a wide range of applications in the automotive manufacturing industry. This will not only affect the operation performance, but also cause accidents in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing riveting process. Firstly, a backbone network for extracting riveting defect features is constructed based on the ResNet network. Secondly, the center area of each riveting defect is located preferentially by the center point detection algorithm. Finally, the bounding box of riveting defects is regressed to achieve defect detection based on this central region. Among them, atrous convolution is used in the external network to increase the receptive field of the model, which combined with an active convolution so that a dynamic atrous convolution module is designed. This module is used to enhance the correlation between feature points of individual pixel in the image, which helps to identify defects with incomplete image edges and suppress background interference. Ablation experiments show that the proposed method achieves the highest accuracy of 95.7%, which is 3.6% higher than the original method. It is found that the proposed method is less affected by the background interference from the qualitative comparison. Moreover, it can also effectively identify the riveting defects on the surface of each area.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep convolutional neural network-based method for self-piercing rivet joint defect detection\",\"authors\":\"Zhao Lun, Sen Lin, YunLong Pang, HaiBo Wang, Zeshan Abbas, ZiXin Guo, XiaoLe Huo, Seng Wang\",\"doi\":\"10.1115/1.4063748\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The self-pierce riveting process for alloy materials has a wide range of applications in the automotive manufacturing industry. This will not only affect the operation performance, but also cause accidents in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing riveting process. Firstly, a backbone network for extracting riveting defect features is constructed based on the ResNet network. Secondly, the center area of each riveting defect is located preferentially by the center point detection algorithm. Finally, the bounding box of riveting defects is regressed to achieve defect detection based on this central region. Among them, atrous convolution is used in the external network to increase the receptive field of the model, which combined with an active convolution so that a dynamic atrous convolution module is designed. This module is used to enhance the correlation between feature points of individual pixel in the image, which helps to identify defects with incomplete image edges and suppress background interference. Ablation experiments show that the proposed method achieves the highest accuracy of 95.7%, which is 3.6% higher than the original method. It is found that the proposed method is less affected by the background interference from the qualitative comparison. Moreover, it can also effectively identify the riveting defects on the surface of each area.\",\"PeriodicalId\":54856,\"journal\":{\"name\":\"Journal of Computing and Information Science in Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computing and Information Science in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4063748\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing and Information Science in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4063748","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A deep convolutional neural network-based method for self-piercing rivet joint defect detection
Abstract The self-pierce riveting process for alloy materials has a wide range of applications in the automotive manufacturing industry. This will not only affect the operation performance, but also cause accidents in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing riveting process. Firstly, a backbone network for extracting riveting defect features is constructed based on the ResNet network. Secondly, the center area of each riveting defect is located preferentially by the center point detection algorithm. Finally, the bounding box of riveting defects is regressed to achieve defect detection based on this central region. Among them, atrous convolution is used in the external network to increase the receptive field of the model, which combined with an active convolution so that a dynamic atrous convolution module is designed. This module is used to enhance the correlation between feature points of individual pixel in the image, which helps to identify defects with incomplete image edges and suppress background interference. Ablation experiments show that the proposed method achieves the highest accuracy of 95.7%, which is 3.6% higher than the original method. It is found that the proposed method is less affected by the background interference from the qualitative comparison. Moreover, it can also effectively identify the riveting defects on the surface of each area.
期刊介绍:
The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications.
Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping