A critical review of process monitoring for laser-based additive manufacturing

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Advanced Engineering Informatics Pub Date : 2024-10-01 DOI:10.1016/j.aei.2024.102932

Ankit Das , Debraj Ghosh , Shing-Fung Lau , Pavitra Srivastava , Aniruddha Ghosh , Chien-Fang Ding

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Abstract

Additive manufacturing (AM) is a versatile, primary manufacturing method widely employed in aerospace, medical, and automotive industries. This environmentally friendly process involves complex phenomena, necessitating comprehensive monitoring for process insights. This review examines AM process monitoring systems, including optical cameras, thermography, and radiography. These technologies generate substantial data, enabling soft computing and machine learning applications for efficiency enhancement and process optimization. Focusing on laser-based AM, the review discusses existing monitoring methods, their limitations, and potential solutions. It explores intelligent AM systems and in-situ X-ray synchrotron techniques, highlighting the transformative potential of efficient process monitoring. The review briefly introduces AM classification, outlines current monitoring methods and their constraints, and proposes smart laser-based AM systems with an overview of applicable machine learning techniques. Finally, it presents plausible solutions to identified limitations and discusses future prospects, emphasizing the revolutionary impact of effective process monitoring on laser AM processes.

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激光快速成型制造过程监控评述

快速成型制造（AM）是一种多功能的初级制造方法，广泛应用于航空航天、医疗和汽车行业。这种环境友好型工艺涉及复杂的现象，需要进行全面监控以深入了解工艺。本综述探讨了 AM 工艺监控系统，包括光学相机、热成像和射线照相术。这些技术可生成大量数据，从而实现软计算和机器学习应用，以提高效率和优化工艺。本综述以基于激光的 AM 为重点，讨论了现有的监控方法、其局限性以及潜在的解决方案。它探讨了智能 AM 系统和原位 X 射线同步加速器技术，强调了高效流程监控的变革潜力。综述简要介绍了 AM 分类，概述了当前的监控方法及其限制因素，并提出了基于激光的智能 AM 系统，同时概述了适用的机器学习技术。最后，文章针对已发现的局限性提出了可行的解决方案，并讨论了未来前景，强调了有效过程监控对激光 AM 过程的革命性影响。

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来源期刊

Advanced Engineering Informatics 工程技术-工程：综合

CiteScore

12.40

自引率

18.20%

发文量

292

审稿时长

45 days

期刊介绍： Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.