| 激光选区熔化增材制造缺陷智能监测与过程控制综述 |
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| 引用本文： | 曹龙超,周奇,韩远飞,宋波,聂振国,熊异,夏凉. 激光选区熔化增材制造缺陷智能监测与过程控制综述[J]. 航空学报, 2021, 42(10): 524790-524790. DOI: 10.7527/S1000-6893.2020.24790 |
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| 作者姓名： | 曹龙超 周奇 韩远飞 宋波 聂振国 熊异 夏凉 |
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| 作者单位： | 华中科技大学航空航天学院,武汉 430074;华中科技大学材料科学与工程学院材料成形与模具技术国家重点实验室,武汉 430074;华中科技大学航空航天学院,武汉 430074;上海交通大学材料科学与工程学院金属基复合材料国家重点实验室,上海 200240;华中科技大学材料科学与工程学院材料成形与模具技术国家重点实验室,武汉 430074;清华大学机械工程系,北京 100084;南方科技大学系统设计与智能制造学院,深圳 518005;华中科技大学机械科学与工程学院数字制造装备与技术国家重点实验,武汉 430074 |
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| 基金项目： | 国家自然科学基金（51805179）；中国博士后基金（2020M682397，2020M682396） |
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| 摘 要： | 激光选区熔化（SLM）技术被认为是最有应用前景的增材制造技术之一，已应用于航空航天、医疗器械等领域。然而，如何确保构件质量的可靠性和制造的可重复性是SLM面临的最大挑战，已被认为是限制SLM及其他金属增材制造技术发展和工业应用的最大壁垒。其中，主要原因是SLM过程中会产生难以控制的缺陷。因此，对SLM进行过程监测和实时反馈控制是解决这一挑战的重要研究方向，也已成为学术界和工业界的研究热点之一。通过对近十年该领域的文献调研，综述了金属激光增材制造中常见的冶金缺陷及其产生机理，对金属增材制造过程产生的信号及其监测手段，如声信号、光信号及热信号等进行了详细描述；总结了信号数据的处理方法，包括传统的统计处理方法和新兴的基于机器学习的智能监测方法；随后，综述了金属增材制造过程的质量控制方法，包括非闭环控制和闭环控制，并对全文进行了总结，展望了未来SLM智能监测和控制领域值得深入的研究方向。
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| 关 键 词： | 激光选区熔化(SLM) 增材制造 过程监测 机器学习 质量控制 |
| 收稿时间： | 2020-09-23 |
| 修稿时间： | 2020-11-07 |
Review on intelligent monitoring of defects and process control of selective laser melting additive manufacturing |
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| CAO Longchao,ZHOU Qi,HAN Yuanfei,SONG Bo,NIE Zhenguo,XIONG Yi,XIA Liang. Review on intelligent monitoring of defects and process control of selective laser melting additive manufacturing[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(10): 524790-524790. DOI: 10.7527/S1000-6893.2020.24790 |
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| Authors: | CAO Longchao ZHOU Qi HAN Yuanfei SONG Bo NIE Zhenguo XIONG Yi XIA Liang |
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| Abstract: | Selective Laser Melting (SLM) is considered to be one of the most promising additive manufacturing technologies and has been applied in aerospace, medical equipment and other fields. However, how to ensure the reliability of component quality and the repeatability of manufacturing is the largest challenge faced by SLM, which has been regarded as the biggest barrier to the development and industrial application of SLM and other metal additive manufacturing technologies. The main reason is that the defects generated during the SLM process are difficult to control. Therefore, process monitoring and real-time feedback control of SLM is an important research direction to solve this problem, which has become one of the research hotspots in academia and industry. Based on a literature survey in this field in the past ten years, the common metallurgical defects and their generation mechanisms in metal laser additive manufacturing are reviewed, and the signals generated during the metal additive manufacturing process and their monitoring methods, such as acoustic signal, optical signal, and thermal Signal, are described in detail. Signal data processing methods are summarized, including traditional statistical processing methods and emerging intelligent monitoring methods based on machine learning. Then, the quality control methods of metal additive manufacturing processes, including non-closed loop control and closed-loop control, are reviewed. Directions worthy of future research of SLM intelligent monitoring and control are also discussed. |
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| Keywords: | Selective Laser Melting(SLM) additive manufacturing process monitoring machine learning quality control |
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