Review on intelligent monitoring of defects and process control of selective laser melting additive manufacturing

被引：0
作者：
Cao L. [1 ,2 ]
Zhou Q. [1 ]
Han Y. [3 ]
Song B. [2 ]
Nie Z. [4 ]
Xiong Y. [5 ]
Xia L. [6 ]
机构：
[1] School of Aerospace Engineering, Huazhong University of Science & Technology, Wuhan
[2] State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science & Technology, Wuhan
[3] State Key Laboratory of Metal Matrix Composites, School of Mechanical Science and Engineering, Shanghai Jiaotong University, Shanghai
[4] Department of Mechanical Engineering, Tsinghua University, Beijing
[5] School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen
[6] State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2021年 / 42卷 / 10期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Additive manufacturing; Machine learning; Process monitoring; Quality control; Selective Laser Melting(SLM);
D O I：
10.7527/S1000-6893.2020.24790
中图分类号：
学科分类号：
摘要：
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. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.
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