Overhang structures are essential geometries in metal additive manufacturing for realizing complex shapes. However, achieving stable, support-free overhang structures requires precise control of process parameters, and securing shape fidelity becomes particularly challenging as overhang length increases due to thermal deformation. To address this challenge, this study proposed a Bayesian optimization framework for efficiently identifying optimal process parameters to fabricate high-difficulty overhang structures. An image-based scoring method was developed to quantitatively evaluate shape defects. Experimental data were collected by fabricating 3, 6, and 9 mm overhang structures with various process parameters. Based on collected data, Gaussian Process Regression (GPR) models were trained. A physics-informed soft penalty term based on energy density was incorporated to construct a surrogate model capable of making physically plausible predictions even in extrapolated regions. Using this model, Bayesian optimization was applied to overhang lengths of 12, 15, and 18 mm, for which no prior experimental data existed. Recommended parameters enabled stable, support-free fabrication of overhang structures. This study demonstrates that reliable optimization of process parameters for complex geometries can be achieved by combining minimal experimental data with physics-informed modeling, highlighting the framework’s potential extension to a wider range of geometries and processes

Additive manufacturing (AM) technology, also known as 3D printing, is a highly promising technology that can drive innovation in various industrial areas, including the nuclear industry. Although the nuclear industry is traditionally conservative when it comes to adopting new technologies, it is crucial that AM technology is eventually applied for a variety of reasons. To overcome the barriers that currently hinder the adoption of AM in the nuclear industry, it is essential to ensure the reliability of AM products. One key factor is ensuring that AM products have mechanical properties equivalent to those of traditionally manufactured products. This paper presents the results of mechanical property tests conducted on additive manufactured specimens of stainless steel 316 L after heat treatment. We performed tensile tests, hardness tests, and microstructure analysis on specimens produced using two types of metal AM technologies: powder bed fusion (PBF) and directed energy deposition (DED). The results of the tests indicate that certain weaknesses, such as anisotropy and brittleness, in AM products can be improved through three types of heat treatments. In particular, AM products produced using the PBF method and subjected to heat treatments show potential for application in the nuclear industry in terms of materials.

Hybrid additive manufacturing (AM) refers to a combination of two metal AM techniques: material deposition by powder bed fusion (PBF) and additional building by directed energy deposition (DED). This study focused on different characteristics in accordance with relative deposition directions of PBF and DED during hybrid AM production. Characteristics of the sample fabricated by hybrid AM (i.e., hybrid sample) were compared with those of the sample fabricated by PBF or DED. Ferrite was dominant in the microstructure of PBF deposits with very fine retained austenite observed locally. In contrast, lath martensite and retained austenite were formed uniformly in the microstructure of DED deposits. Different microstructures in the two processes were attributed to differences of cooling rate. In DED deposits, microhardness was significantly decreased owing to a high retained austenite fraction. However, in the hybrid sample, microhardness was rapidly increased in the HAZ owing to aging heat treatment for long-term deposition. Principal wear mechanisms of PBF and DED samples were oxidative wear and plastic deformation, respectively.

In the selective laser melting (SLM) process, a three-dimensional part is manufactured based on the formation of numerous molten tracks. Consequently, the generated melt pool in the scanning process of each track exhibits close relation to the internal defect formation and the quality of the fabricated part. In this study, a numerical model of single-track scanning of the SLM process is presented to analyze the melt pool characteristics for various process conditions. The presented model considers the thermal behavior of the powder material including the phase change and densification during the SLM process. The temperature-dependent energy absorption and the increase in effective energy absorptivity due to the keyhole mode melting are also incorporated in the heat flux model to evaluate the process conditions in the presence of high energy density. Moreover, the single-track specimens were manufactured under various process conditions for validation of the proposed model. The predicted melt pool dimensions, as well as the melting modes (Conduction/Keyhole), demonstrated good agreement with the experimental measurements. Based on the analysis results, the process boundaries (Keyhole/Lack-of-Fusion) for the SLM process of AlSi10Mg are provided and the potential application of the proposed model for exploring the process window is discussed.