## 2 Materials and Methods  
The layer-by-layer construction, shown in Figure 2, depicts rows of melt pools atop one another to form the microstructure typical of an SLM material. Consider the cross-section shown in Figure 2 from [38] of an SLM material, referred to as the RVE from hereon. Figure 2: (a) SEM image of SLM material [38]; (b) enlarged image of a region from (a). The FE mesh used in this analysis was constructed using the conforming to interface structured adaptive mesh refinement (CISAMR) [39; 40] method. The CISAMR is a non-iterative mesh generation algorithm that transforms an initial structured mesh comprising rectangular elements in a high-quality hybrid conforming mesh consisting of rectangular and triangular elements. The meshing process involves three phases: (1) \(h\)-adaptive refinement of background elements intersecting material interfaces to minimize the geometric discretization error and accurately approximate stress concentrations in the regions; (2) performing \(r\)-adaptivity on the nodes of refined elements cut by material interfaces, which on average transforms 50% of these elements into conforming elements; and (3) the sub-triangulation of the remaining non-conforming elements with hanging nodes, as well as elements with hanging nodes that are created during the \(h\)-adaptive refinement phase. The effect of each phase of the CISAMR algorithm on the transformation of a structured mesh into a conforming mesh is visualized in Figure 3 for a smooth interface (see [39] for more algorithmic and implementation details). A unique feature of CISAMR is the ability to generate high-quality meshes for problems with complex morphologies [41]. However, the presence of sharp geometrical features in the microstructure requires the addition of new algorithmic features to the CISAMR algorithm. As shown in Figure 4, in such cases, after the \(h\)-adaptivity (SAMR) phase, we first identify all background mesh elements containing sharp corners of melt pools. Next, the closest mesh node to the sharp corner within this element is identified and snapped to the sharp corner. The subsequent phases of the CISAMR algorithm (\(r\)-adaptivity and sub-triangulation) are then carried out in the same way as described earlier. With this simple modification, we can enable the CISAMR algorithm to preserve sharp features in the resulting mesh. To mesh the microstructure shown in Figure 2a, we first generate segregated meshes for each melt pool using the modified CISAMR algorithm, and then stitch them together (merge node IDs along their boundaries) to generate the conforming mesh for the entire microstructure. Figure 5a shows the geometrical model constructed for the 924 \(\upmu\)m \(\times\) 840 \(\upmu\)m RVE image shown in Figure 2a, in which melt pool boundaries are characterized using non-uniform rational B-splines (NURBS) curves. This domain comprises 239 melt pools and is discretized using the CISAMR algorithms, resulting in a conforming mesh consisting of 1.31 million elements and a maximum aspect ratio of 4.4. The RVE mesh is then partitioned into several smaller SVEs. Four different SVEs are analyzed in this work, i.e., \(L_{SVE}=\) 56, 84, 112, and 140 \(\upmu\)m. These sizes correspond to 240, 110, 56, and 36 SVE counts, respectively. Figure 3: Transformation of a structured mesh composed of 4-node quadrilateral elements into a conforming mesh using the CISAMR algorithm: (**a**) \(h\)-adaptive refinement in the vicinity of material interfaces; (**b**) \(r\)- adaptivity of the nodes of elements intersecting each interface; (**c**) sub-triangulating the elements deformed during the \(r\)-adaptivity process, as well as elements with hanging nodes created after \(h\)-adaptivity. Red, and yellow nodes correspond to hanging nodes created during the SAMR process, and conforming background mesh nodes, respectively. Since the RVE mesh contains 239 melt pools, the smallest to largest SVEs contain an average of about 1, 2.2, 4.2, and 6.6 melt pools in each SVE.