Although applications of additive manufacturing (AM) have been significantly increased in

recent years, its broad application in several industries is still under progress. AM also

known as three-dimensional (3D) printing is layer by layer manufacturing process which

can be used for fabrication of geometrically complex customized functional end-use

products. Since AM processing parameters have significant effects on the performance of

the printed parts, it is necessary to tune these parameters which is a difficult task. Today,

different artificial intelligence techniques have been utilized to optimize AM parameters

and predict mechanical behavior of 3D-printed components. In the present study, applications

of machine learning (ML) in prediction of structural performance and fracture of

additively manufactured components has been presented. This study first outlines an

overview of ML and then summarizes its applications in AM. The main part of this review,

focuses on applications of ML in prediction of mechanical behavior and fracture of 3Dprinted

parts. To this aim, previous research works which investigated application of ML in

characterization of polymeric and metallic 3D-printed parts have been reviewed and discussed.

Moreover, the review and analysis indicate limitations, challenges, and perspectives

for industrial applications of ML in the field of AM. Considering advantages of ML

increase in applications of ML in optimization of 3D printing parameters, prediction of

mechanical performance, and evaluation of 3D-printed products is expected.