Ti-6Al-4V alloy is widely utilized in aerospace and medical sectors due to its high specific strength, corrosion resistance, and biocompatibility. However, its low machinability makes it difficult to manufacture complex-shaped products. Advancements in additive manufacturing have focused on producing high-performance, complex components using the laser powder bed fusion (LPBF) process, which is a specialized technique for customized geometries. The LPBF process exposes materials to extreme thermal conditions and rapid cooling rates, leading to residual stresses within the parts. These stresses are intensified by variations in the thermal history across regions of the component. These variations result in differences in microstructure and mechanical properties, causing distortion. Although support structure design has been researched to minimize residual stress, few studies have conducted quantitative analyses of stress variations due to different support designs. This study investigated changes in the residual stress and mechanical properties of Ti-6Al-4V alloy fabricated using LPBF, focusing on support structure design.

Ni-based superalloys are widely used for critical components in aerospace, defense, industrial power generation systems, and other applications. Clean superalloy powders and manufacturing processes, such as compaction and hot isostatic pressing, are essential for producing superalloy discs used in turbine engines, which operate under cyclic rotating loads and high-temperature conditions. In this study, the plasma rotating electrode process (PREP), one of the most promising methods for producing clean metallic powders, is employed to fabricate Ni-based superalloy powders. PREP leads to a larger powder size and narrower distribution compared to powders produced by vacuum induction melt gas atomization. An important finding is that highly spheroidized powders almost free of satellites, fractured, and deformed particles can be obtained by PREP, with significantly low oxygen content (approximately 50 ppm). Additionally, large grain size and surface inclusions should be further controlled during the PREP process to produce high-quality powder metallurgy parts.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

In this study, the effect of build orientation on the mechanical properties of Hastelloy X fabricated by laser powder bed fusion (LPBF) process was investigated. Initial microstructural analysis revealed an equiaxed grain structure with random crystallographic orientation and annealing twins. Intragranular precipitates identified as Cr-rich M23C6 and Mo-rich M6C carbides were observed, along with a dense dislocation network and localized dislocation accumulation around the carbides. Mechanical testing showed negligible variation in yield strength with respect to build orientation; however, both ultimate tensile strength and elongation exhibited a clear increasing trend with higher build angles. Notably, the specimen built at 90° exhibited approximately 22% higher tensile strength and more than twice the elongation compared to the 0° specimen.

This study investigated the effect of the hatch spacing parameter on the microstructure and mechanical properties of SA508 Gr.3 steel manufactured by laser powder bed fusion (L-PBF) for a nuclear pressure vessel. Materials were prepared with varying hatch spacing (0.04 mm [H4] and 0.06 mm [H6]). The H4 exhibited finer and more uniformly distributed grains, while the H6 showed less porosity and a lower defect fraction. The yield strength of the H4 material was higher than that of the H6 material, but there was a smaller difference between the materials in tensile strength. The measured elongation was 5.65% for the H4 material and 10.41% for the H6 material, showing a significantly higher value for H6. An explanation for this is that although the H4 material had a microstructure of small and uniform grains, it contained larger and more numerous pore defects than the H6 material, facilitating stress concentration and the initiation of microcracks.

A cold roll-bonding (CRB) process is applied to fabricate an AA1050/AA5052 layered sheet. In the process, commercial AA1050 and AA5052 sheets of 1 mm thickness, 40 mm width and 300 mm length are stacked onto each other, and then reduced to a thickness of 0.5 mm through a 2-pass cold rolling process without lubricant. The roll-bonded AA1050/AA5052 layered sheet is then annealed for 1 h at various temperatures from 200 to 400 °C. The specimens annealed at temperatures below 250 °C showed a typical deformation structure in which the grains were elongated along the rolling direction. However, the specimens annealed at temperatures higher than 300 °C exhibited recrystallization structures in both the AA1050 and AA5052 regions. All the roll-bonded and subsequently annealed specimens showed an inhomogeneous distribution of hardness in the thickness direction, in which the hardness in the AA5052 regions was higher than that in the AA1050 regions. As the annealing temperature increased, the tensile and yield strengths decreased and the elongation increased gradually. The mechanical properties were compared to those of commercial AA1050 and AA5052 materials and CRBed AA5052-2L materials from a previous study.

As environmental concerns escalate, the increase in recycling of aluminum scrap is notable within the aluminum alloy production sector. Precise control of essential components such as Al, Cu, and Si is crucial in aluminum alloy production. However, recycled metal products comprise various metal components, leading to inherent uncertainty in component concentrations. Thus, meticulous determination of input quantities of recycled metal products is necessary to adjust the composition ratio of components. This study proposes a stable input determination heuristic algorithm considering the uncertainty arising from utilizing recycled metal products. The objective is to minimize total costs while satisfying the desired component ratio in aluminum manufacturing processes. The proposed algorithm is designed to handle increased complexity due to introduced uncertainty. Validation of the proposed heuristic algorithm's effectiveness is conducted by comparing its performance with an algorithm mimicking the input determination method used in the field. The proposed heuristic algorithm demonstrates superior results compared to the field-mimicking algorithm and is anticipated to serve as a useful tool for decision-making in realistic scenarios.