Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches
Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches
复制标题
基于金属的增材制造状态监测：基于机器学习的方法综述
DOI：
10.1109/tmech.2021.3110818
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Lin Xin
中科院分区：
文献类型：
--
作者：
Zhu Kunpeng;Fuh Jerry Ying Hsi;Lin Xin
金属基增材制造（MAM）工艺具有制造具有复杂几何形状和内部特征的致密金属零件的能力，具有广泛的工业应用潜力。然而，MAM过程中的各种缺陷极大地影响了最终零件的精度、力学性能和可重复性。这些缺陷限制了其作为可靠制造工艺的应用，特别是在高质量和可靠性至关重要的航空航天和医疗行业。MAM过程监控为避免和消除缺陷以提高构建质量提供了技术基础。基于MAM构建缺陷的性质，本文对监控方法进行了深入的研究，提出了一种用于过程状态监控的机器学习（ML）框架。根据机器学习模型的结构，分为基于浅层机器学习的方法和基于深度学习的方法。讨论了最先进的机器学习监测方法，以及它们的算法实现的优点和缺点。最后，对基于机器学习的过程监控研究前景进行了总结和展望。
The metal-based additive manufacturing (MAM) processes have great potential in wide industrial applications, for their capabilities in building dense metal parts with complex geometry and internal characteristics. However, various defects in the MAM process greatly affect the precision, mechanical properties and repeatability of final parts. These defects limit its application as a reliable manufacturing process, especially in the aerospace and medical industries where high quality and reliability are essential. MAM process monitoring provides a technical basis for avoiding and eliminating defects to improve the build quality. Based on of the nature of the MAM build defects, this article conducts a thorough investigation of monitoring methods, and proposes a machine learning (ML) framework for process condition monitoring. According to the structure of ML models, they are divided into shallow ML-based and deep learning-based methods. The state-of-the-art ML monitoring approaches, as well as the advantages and disadvantages of their algorithmic implementations, are discussed. Finally, the prospects of ML based process monitoring researches are summarized and advised.
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DOI：
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影响因子：
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