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材料制造业中的3D打印:工业4.0领域

3D printing in materials manufacturing industry: A realm of Industry 4.0.

作者信息

Tamir Tariku Sinshaw, Xiong Gang, Shen Zhen, Leng Jiewu, Fang Qihang, Yang Yong, Jiang Jingchao, Lodhi Ehtisham, Wang Fei-Yue

机构信息

State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, 510006, China.

Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

出版信息

Heliyon. 2023 Sep 6;9(9):e19689. doi: 10.1016/j.heliyon.2023.e19689. eCollection 2023 Sep.

Abstract

Additive manufacturing (AM), also known as 3D printing, is a new manufacturing trend showing promising progress over time in the era of Industry 4.0. So far, various research has been done for increasing the reliability and productivity of a 3D printing process. In this regard, reviewing the existing concepts and forwarding novel research directions are important. This paper reviews and summarizes the process flow, technologies, configurations, and monitoring of AM. It started with the general AM process flow, followed by the definitions and the working principles of various AM technologies and the possible AM configurations, such as traditional and robot-assisted AM. Then, defect detection, fault diagnosis, and open-loop and closed-loop control systems in AM are discussed. It is noted that introducing robots into the assisting mechanism of AM increases the reliability and productivity of the manufacturing process. Moreover, integrating machine learning and conventional control algorithms ensures a closed-loop control in AM, a promising control strategy. Lastly, the paper addresses the challenges and future trends.

摘要

增材制造(AM),也被称为3D打印,是一种新的制造趋势,在工业4.0时代随着时间的推移显示出有前景的进展。到目前为止,已经进行了各种研究以提高3D打印过程的可靠性和生产率。在这方面,回顾现有概念并提出新的研究方向很重要。本文回顾并总结了增材制造的工艺流程、技术、配置和监测。它从一般的增材制造工艺流程开始,接着是各种增材制造技术的定义和工作原理以及可能的增材制造配置,如传统的和机器人辅助的增材制造。然后,讨论了增材制造中的缺陷检测、故障诊断以及开环和闭环控制系统。值得注意的是,将机器人引入增材制造的辅助机制可提高制造过程的可靠性和生产率。此外,将机器学习和传统控制算法相结合可确保增材制造中的闭环控制,这是一种有前景的控制策略。最后,本文阐述了挑战和未来趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/766f/10558948/9d2cf6dfc3b8/gr001.jpg

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