In additive manufacturing (AM), the powder properties and laser powder bed fusion (LPBF) process parameters influence the quality of materials and building parts. However, the relationship between the size of the powder, LPBF process parameters, and mechanical properties is not well-established. In addition, Hastelloy X (HX) is attracting attention for its excellent high-temperature properties, but it is difficult to process, such as by cutting and milling, because of its high hardness and high ductility. This can be overcome by applying the AM process. We compared the LPBF window process maps for two HX powders of different sizes. Despite their small difference of 19.7% in particle size, it was confirmed that the difference in laser power was more than 40 W, the difference in scan speed was more than 100 mm/s, and the difference in energy density was more than 20% under the optimal process conditions. The as-built specimen had a larger molten-pool size as the energy density was higher, which resulted in the differences in mechanical properties at room temperature and high temperature (816 °C). We considered the control of the size of the powder to obtain the properties required for each temperature condition. The microstructures and mechanical properties of the as-built LPBF specimens were also investigated and compared with those of cast HX. Because of the rapid melting and solidification processes in LPBF, the as-built HX exhibited nano-sized dendrite structures and large internal strain energy. This resulted in the as-built LPBF exhibiting a higher room-temperature tensile strength than the cast material. Under high-temperature conditions, the grain boundary of the as-built LPBF acts as a sliding path, and the as-built LPBF HX showed significantly better high-temperature tensile strength characteristics than the cast HX.

In metal product manufacturing, additive manufacturing (AM) is a method that has the advantage of fabricating complex shapes and customized production, unlike existing machining methods. However, owing to the characteristics of the AM process, anisotropy of macrostructure occurs because of various causes such as the scan direction, melting, fusion, and cooling of the powdered material. The macrostructure anisotropy is realized from the scan direction, and when a single layer is stacked in one direction, it is expressed as orthogonal anisotropy. Here, the classical lamination theory is applied to simply calculate the individual orthotropic layers by superimposing them. Through this, the authors analyzed whether the mechanical properties of the product are isotropically expressed with a periodic layer rotation strategy. To determine if the mechanical properties can be reasonably considered to be isotropic, a shock absorber mount for a vehicle was manufactured by AM. The tensile and vibration test performed on the product was compared with the finite element analysis and experimental results. As a result of the comparison, it was confirmed that the macroscopically of the product was considered isotropic as the load-displacement diagram and the fracture location coincided, as well as the natural frequency and mode shape.

Recent advances in additive manufacturing have provided more freedom in the design of metal parts; hence, the prototyping of fluid machines featuring extremely complex geometries has been investigated extensively. The fabrication of fluid machines via additive manufacturing requires significant attention to part stability; however, studies that predict regions with a high risk of collapse are few. Therefore, a novel algorithm that can detect collapse regions precisely is proposed herein. The algorithm reflects the support span over the faceted surface via propagation and invalidates overestimated collapse regions based on the overhang angle. A heat exchanger model with an extremely complex internal space is adopted to validate the algorithm. Three samples from the model are extracted and their prototypes are fabricated via laser powder bed fusion. The results yielded by the fabricated samples and algorithm with respect to the sample domain are compared. Regions of visible collapse identified on the surface of the fabricated samples are predicted precisely by the algorithm. Thus, the supporting span reflected by the algorithm provides an extremely precise prediction of collapse.