Research Highlights: Senthamilaruvi Moorthy
a) 605 compression samples with nine unique orientations in various locations on a build plate; b) 56 tensile samples with nine unique
orientations in various locations on a build plate; c) experimental setup; d) digital image correlation
Senthamilaruvi’s research is about characterizing the mechanical properties of additively printed (selective laser melting, SLM) inconel 718 samples. Mechanical properties are studied using a noncontact strain measurement technique called digital image correlation (DIC).
His study has three main goals: The first is to study the variation of mechanical properties of additively printed samples in as-built, machined, and heat treated conditions. Shape and porosity content of samples vary in as-built, machined, and heat treated conditions; hence, it is expected that mechanical properties also vary between these conditions. Senthamilaruvi's work quantifies the difference caused by these processing conditions on mechanical properties of as-built samples. The second goal is to study the variation of the mechanical properties of samples with respect to their thicknesses, as well as the variation of the surfaces and bulk properties. The time difference between melting one layer of powder and the next layer varies based on the thickness of the samples printed. Thicker samples have higher interlayer times, which would result in different thermal histories of the printed samples and, hence, different mechanical properties. So, the differences in the mechanical properties will be quantified. The third goal is to use machine learning methods to model the variation of mechanical properties with respect to the printing orientation and the location in the build plate. There are many possible variations in orientation and location for printing samples, and the orientation and location affect the mechanical properties nonlinearly; therefore, it is important to model the variation in mechanical properties of the samples with respect to printing orientation and location so that properties can be reasonably predicted for any given orientation. Senthamilaruvi's research will help industrial users print additively manufactured parts with desired properties while reducing uncertainty.