Additive manufacturing has revolutionized the manufacturing of complex parts by enabling direct material joining and offers several advantages such as cost-effective manufacturing of complex parts, reducing manufacturing waste, and opening new possibilities for manufacturing automation. One group of materials for which additive manufacturing holds great potential for enhancing component performance and properties is Functionally Graded Materials (FGMs). FGMs are advanced composite materials that exhibit smoothly varying properties making them desirable for applications in aerospace, automobile, biomedical, and defense industries. Such composition differs from traditional composite materials, since the location-dependent composition changes gradually in FGMs, leading to enhanced properties. Recently, machine learning techniques have emerged as a promising means for fabrication of FGMs through optimizing processing parameters, improving product quality, and detecting manufacturing defects. This paper first provides a brief literature review of works related to FGM fabrication, followed by reviewing works on employing machine learning in additive manufacturing, Afterward, we provide an overview of published works in the literature related to the application of machine learning methods in Directed Energy Deposition and for fabrication of FGMs.

通过机器学习优化处理参数、提高产品质量和检测制造缺陷的方式，提供了有关功能分级材料 (FGMs) 和3D打印的相关文献综述，并概述了机器学习方法在定向能量沉积和FGMs制造中的应用。

功能梯度材料的增材制造机器学习方法综述