Review of Machine Learning Methods for Additive Manufacturing of
  Functionally Graded Materials
Mohammad Karimzadeh Aleksandar Vakanski Fei Xu Xinchang Zhang
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.