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.

翻译：增材制造通过实现直接材料连接，革新了复杂零件的制造方式，并展现出诸多优势，如复杂零件的经济高效制造、减少制造废料以及为制造自动化开辟新可能。在增材制造中，具有提升部件性能与特性巨大潜力的一类材料是梯度功能材料（FGMs）。FGMs是一种先进复合材料，其性能呈现平滑变化特性，使其在航空航天、汽车、生物医学和国防工业等领域具有广泛应用前景。这种成分结构与传统复合材料不同，因为FGMs中位置依赖的组分是逐渐变化的，从而赋予材料更优性能。近年来，机器学习技术已成为通过优化工艺参数、提高产品质量和检测制造缺陷来实现FGMs制备的有前景手段。本文首先对FGM制备相关研究进行简要文献综述，随后评述了机器学习在增材制造中应用的研究工作，进而概述了已发表文献中关于机器学习方法在定向能量沉积技术中的应用及FGMs制备的相关工作。