{"title":"Imaging systems and techniques for fusion-based metal additive manufacturing: a review","authors":"Himanshu Balhara, Adithyaa Karthikeyan, Abhishek Hanchate, Tapan Ganatma Nakkina, S. Bukkapatnam","doi":"10.3389/fmtec.2023.1271190","DOIUrl":null,"url":null,"abstract":"This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.","PeriodicalId":330401,"journal":{"name":"Frontiers in Manufacturing Technology","volume":"63 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Manufacturing Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fmtec.2023.1271190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.
本研究概述了各种成像技术在智能制造领域的变革能力,并介绍了一些案例研究,主要侧重于各种原位和非原位成像方法,用于监测基于熔融的金属增材制造(AM)工艺,如定向能沉积(DED)、选择性激光熔化(SLM)和电子束熔化(EBM)。包括高速相机、热像仪和数码相机在内的原位成像技术越来越经济实惠、互补性强,对于实时监控、持续评估制造质量至关重要。例如,高速相机可以捕捉到激光与材料之间的动态相互作用,迅速检测出缺陷,而热像仪则可以识别熔池的热分布和潜在的异常情况。然后,利用现场成像收集的数据提取相关特征,以便有效控制工艺参数,从而优化 AM 工艺并最大限度地减少缺陷。另一方面,原位成像技术在综合部件分析中发挥着至关重要的作用。扫描电子显微镜 (SEM)、光学显微镜和三维纤度仪可以详细描述微结构特征、表面粗糙度、孔隙率和尺寸精度。利用人工智能(AI)算法,可以融合来自不同成像和其他多模态数据源的信息,从而更全面地了解制造过程。通过这种整合,人工智能算法可以分析综合数据,提取相关的见解和模式,从而为流程优化和质量保证做出明智的决策。最终,成像技术在增材制造中的威力在于它能够提供实时监控、精确控制和全面分析,使制造商能够在部件生产中实现最高水平的精度、可靠性和生产率。