{"title":"Detection and Characterization of Defects in Additive Manufacturing by Polarization-Based Imaging System","authors":"Xing Peng, Lingbao Kong","doi":"10.1186/s10033-023-00943-0","DOIUrl":null,"url":null,"abstract":"Abstract Additive manufacturing (AM) technology such as selective laser melting (SLM) often produces a high reflection phenomenon that makes defect detection and information extraction challenging. Meanwhile, it is essential to establish a characterization method for defect analysis to provide sufficient information for process diagnosis and optimization. However, there is still a lack of universal standards for the characterization of defects in SLM parts. In this study, a polarization-based imaging system was proposed, and a set of characterization parameters for SLM defects was established. The contrast, defect contour information, and high reflection suppression effect of the SLM part defects were analyzed. Comparative analysis was conducted on defect characterization parameters, including geometric and texture parameters. The experimental results demonstrated the effects of the polarization imaging system and verified the feasibility of the defect feature extraction and characterization method. The research work provides an effective solution for defect detection and helps to establish a universal standard for defect characterization in additive manufacturing.","PeriodicalId":10115,"journal":{"name":"Chinese Journal of Mechanical Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":4.2000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s10033-023-00943-0","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Additive manufacturing (AM) technology such as selective laser melting (SLM) often produces a high reflection phenomenon that makes defect detection and information extraction challenging. Meanwhile, it is essential to establish a characterization method for defect analysis to provide sufficient information for process diagnosis and optimization. However, there is still a lack of universal standards for the characterization of defects in SLM parts. In this study, a polarization-based imaging system was proposed, and a set of characterization parameters for SLM defects was established. The contrast, defect contour information, and high reflection suppression effect of the SLM part defects were analyzed. Comparative analysis was conducted on defect characterization parameters, including geometric and texture parameters. The experimental results demonstrated the effects of the polarization imaging system and verified the feasibility of the defect feature extraction and characterization method. The research work provides an effective solution for defect detection and helps to establish a universal standard for defect characterization in additive manufacturing.
期刊介绍:
Chinese Journal of Mechanical Engineering (CJME) was launched in 1988. It is a peer-reviewed journal under the govern of China Association for Science and Technology (CAST) and sponsored by Chinese Mechanical Engineering Society (CMES).
The publishing scopes of CJME follow with:
Mechanism and Robotics, including but not limited to
-- Innovative Mechanism Design
-- Mechanical Transmission
-- Robot Structure Design and Control
-- Applications for Robotics (e.g., Industrial Robot, Medical Robot, Service Robot…)
-- Tri-Co Robotics
Intelligent Manufacturing Technology, including but not limited to
-- Innovative Industrial Design
-- Intelligent Machining Process
-- Artificial Intelligence
-- Micro- and Nano-manufacturing
-- Material Increasing Manufacturing
-- Intelligent Monitoring Technology
-- Machine Fault Diagnostics and Prognostics
Advanced Transportation Equipment, including but not limited to
-- New Energy Vehicle Technology
-- Unmanned Vehicle
-- Advanced Rail Transportation
-- Intelligent Transport System
Ocean Engineering Equipment, including but not limited to
--Equipment for Deep-sea Exploration
-- Autonomous Underwater Vehicle
Smart Material, including but not limited to
--Special Metal Functional Materials
--Advanced Composite Materials
--Material Forming Technology.