Additive manufacturing as a tool for high-throughput experimentation

Abstract

Additive manufacturing (AM) is a disruptive technology with a unique capability in fabricating parts with complex geometry and fixing broken supply chains. However, many AM techniques are complicated with their processing features due to complex heating and cooling cycles with the melting of feedstock materials. Therefore, it is quite challenging to directly apply the materials design and processing optimization method used for conventional manufacturing to AM techniques. In this viewpoint paper, we discuss some of the ongoing efforts of high-throughput (HT) experimentation, which can be used for materials development and processing design. Particularly, we focus on the beam- and powder-based AM techniques since these methods have demonstrated success in HT experimentation. In addition, we propose new opportunities to apply AM techniques as the materials informatic tools contributing to materials genome.

New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

“…It is likely that there are mature AM parameter packets provided by commercial 3D printing companies for conventional alloys, such as steel, Al alloys, Ti alloys and Ni-based superalloys. However, it is still quite difficult to obtain optimal AM parameters for newly designed alloys, such as HEAs, from experimental [118] . The proposed model was proven to be applicable to single-phase HEAs (e.g., FeCoCrNiMn) [118] .…”

“…However, it is still quite difficult to obtain optimal AM parameters for newly designed alloys, such as HEAs, from experimental [118] . The proposed model was proven to be applicable to single-phase HEAs (e.g., FeCoCrNiMn) [118] . Compared with the calculation method, ML can describe the relationship between the descriptors and desired properties without considering the complex physical metallurgy process of AM.…”