基于机器学习的传感器监测在金属激光增材制造中的应用

doi:10.11896/cldb.23080174

材料导报

2025, Vol. 39

Issue (2): 23080174-16 https://doi.org/10.11896/cldb.23080174

金属与金属基复合材料

基于机器学习的传感器监测在金属激光增材制造中的应用

田根¹, 朱甫宏², 王文宇^1,*, 王晓明¹, 赵阳¹, 韩国峰¹, 任智强¹, 朱胜¹

1 陆军装甲兵学院装备再制造技术国防科技重点实验室,北京 100072
2 31606部队,浙江湖州 313000

Application of Sensor Monitoring Based on Machine Learning in Metal Laser Additive Manufacturing

TIAN Gen¹, ZHU Fuhong², WANG Wenyu^1,*, WANG Xiaoming¹, ZHAO Yang¹, HAN Guofeng¹, REN Zhiqiang¹, ZHU Sheng¹

1 National Key Laboratory for Remanufacturing, Army Academy of Armored Forces, Beijing 100072, China
2 31606 Unit, Huzhou 313000, Zhejiang, China

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摘要金属增材制造是一种通过逐层沉积金属材料实现成形的先进制造技术。在制造过程中,由于物理环境、设备状态以及工艺参数的综合影响,成形件可能会出现各种缺陷。通过传感器对成形过程中的信号进行监测,并结合机器学习算法,不仅可以识别成形件的缺陷,还能对其质量和性能进行评估。本文综述了声音传感器、热传感器、可见光相机、光谱传感器以及多传感器融合技术在增材制造原位监测、特征提取、数据融合及机器学习算法应用方面的研究进展。同时,结合当前机器学习技术在实际应用中的问题,探讨了基于物理信息驱动的机器学习研究现状。最后,对未来需要解决的关键问题及研究方向进行了总结和展望。

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	任智强
	朱胜

关键词: 增材制造机器学习传感器监测特征提取数据融合物理信息

Abstract: Metal additive manufacturing is an advanced manufacturing technology that forms components by depositing metal materials layer by layer. During the manufacturing process, factors such as the physical environment, machine conditions, and processing parameters can lead to various defects in the fabricated parts. By employing sensors to monitor process signals and integrating machine learning algorithms, it is possible to identify defects in the fabricated parts and evaluate their quality and performance. This paper reviews the research progress on the application of acoustic sensors, thermal sensors, visible light cameras, spectroscopic sensors, and multi-sensor fusion in in-situ monitoring, feature extraction, data fusion, and machine learning algorithms for additive manufacturing. Additionally, the challenges associated with the application of machine learning technologies are discussed, with a focus on the current research status of physics-informed machine learning approaches. Finally, key issues and future research directions are summarized and outlined.

Key words: additive manufacturing machine learning sensor monitoring feature extraction data fusion physical information

出版日期: 2025-01-25 发布日期: 2025-01-21

ZTFLH:

TP181

基金资助: 国家重点研发计划(2022YFF0609000)

通讯作者: ^*王文宇,陆军装甲兵学院装备再制造技术国防科技重点实验室助理研究员。2010 年毕业于北京化工大学,工学硕士,主要从事增材再制造领域的研究工作。kaolawwy@qq.com

作者简介: 田根,陆军装甲兵学院装备保障与再制造系博士研究生,目前主要研究领域为金属增材制造技术。

引用本文:

田根, 朱甫宏, 王文宇, 王晓明, 赵阳, 韩国峰, 任智强, 朱胜. 基于机器学习的传感器监测在金属激光增材制造中的应用[J]. 材料导报, 2025, 39(2): 23080174-16.
TIAN Gen, ZHU Fuhong, WANG Wenyu, WANG Xiaoming, ZHAO Yang, HAN Guofeng, REN Zhiqiang, ZHU Sheng. Application of Sensor Monitoring Based on Machine Learning in Metal Laser Additive Manufacturing. Materials Reports, 2025, 39(2): 23080174-16.

链接本文:

https://www.mater-rep.com/CN/10.11896/cldb.23080174 或 https://www.mater-rep.com/CN/Y2025/V39/I2/23080174

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