The aim of this paper was to compare duplex (DSS) and super duplex stainless steel processed by laser powder bed fusion (LPBF) based on the process parameters and microstructure–nanomechanical property relationships. Each alloy was investigated with respect to its feedstock powder characteristics. Optimum process parameters including scanning speed, laser power, beam diameter, laser energy density, and layer thickness were defined for each alloy, and near-fully dense parts (>99.9%) were produced. Microstructural analysis was performed via optical (OM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The samples were subjected to stress relief and high-temperature annealing. EBSD revealed the crystallographic orientation and quantified the phases in the as-built and annealed sample conditions. The as-built samples revealed a fully ferritic microstructure with a small amount of grain boundary austenite in the SDSS microstructure. High-temperature solution annealing resulted in the desired duplex microstructure for both alloys. There were no secondary phases present in the microstructure after both heat treatments. Nanoindentation generated nanomechanical (modulus) mapping grids and quantified the nanomechanical (both hardness and modulus) response; plasticity and stress relief were also assessed in all three conditions (as-built, stress-relieved, and annealed) in both DSS and SDSS. Austenite formation in the annealed condition contributed to lower hardness levels (~4.3–4.8 Gpa) and higher plastic deformation compared to the as-built (~5.7–6.3 Gpa) and stress-relieved conditions (~4.8–5.8 Gpa) for both alloys. SDSS featured a ~60% austenite volume fraction in its annealed and quenched microstructure, attributed to its higher nickel and nitrogen contents compared to DSS, which exhibited a ~30% austenite volume fraction.

A methodology for the automated analysis of metal powder scanning electron microscope (SEM) images towards material characterization is developed and presented. This software-based tool takes advantage of a combination of recent artificial intelligence advances (mask R-CNN), conventional image processing techniques, and SEM characterization domain knowledge to assess metal powder quality for additive manufacturing applications. SEM is being used for characterizing metal powder alloys, specifically by quantifying the diameter and number of spherical particles, which are key characteristics for assessing the quality of the analyzed powder. Usually, SEM images are manually analyzed using third-party analysis software, which can be time-consuming and often introduces user bias into the measurements. In addition, only a few non-statistically significant samples are taken into consideration for the material characterization. Thus, a method that can overcome the above challenges utilizing state-of-the-art instance segmentation models is introduced. The final proposed model achieved a total mask average precision (mAP50) 67.2 at an intersection over union of 0.5 and with prediction confidence threshold of 0.4. Finally, the predicted instance masks are further used to provide a statistical analysis that includes important metrics such as the particle size distinction.

Additive manufacturing of Duplex Stainless Steels (DSS) and Super Duplex Stainless Steels (SDSS) has been successfully demonstrated using LPBF in recent years, however, both alloys feature an almost fully ferritic microstructure in the as-built condition due to the fast cooling rates associated with the Laser Powder Bed Fusion (LPBF) process. Blends of DSS and SDSS powders were formulated with austenitic stainless-steel 316L powder, aiming to achieve increased austenite formation during in the LPBF as-built condition to potentially minimize the post heat treatments (solution annealing and quenching). Powder characteristics were investigated and process parameters were optimized to produce near fully dense parts. Nanoindentation (NI) tests were conducted to measure, not only the local mechanical properties and correlate them with the as-built microstructure, but also to gain a deeper understanding in the deformation behavior of individual phases that cannot be studied directly by macroscopic tensile tests. Scanning Electron Microscopy (SEM) and Electron Backscatter Diffraction (EBSD) were employed for microstructural analysis and phase quantification. The microstructural analysis and EBSD phase maps revealed an increase in austenite in the as-built microstructures. Blend 1 resulted in a duplex microstructure consisting of 10% austenite at the XY plane and 20% austenite at the XZ plane. The austenite content increased with increasing proportion of 316L stainless steel in the powder blends. The DSS blend required a much higher volumetric energy density for the fabrication of near fully dense parts. This imposed a slower solidification and a higher melt pool homogeneity, allowing for adequate diffusion of the austenite stabilizing elements. The presented workflow and findings from this study provide valuable insights into powder mixing for the development of custom alloys for rapid material screening in LPBF.

This study investigates the impact of build orientation on thermal distortion, residual stress behaviour, and process efficiency in LPBF. Four orientation strategies, optimized for surface area, support volume, print time, and overheating, were generated in Siemens NX and evaluated using Atlas 3D to predict build-stage and post-support removal distortion. Experimental validation through 3D scanning enabled detailed surface deviation comparisons with simulation outputs. Results showed that support volume and print time optimizations led to the lowest in-process distortion but exhibited higher deformation after support removal, driven by residual stress relaxation. In contrast, the surface area-optimized orientation displayed greater distortion during printing but more stable post-processing behaviour. The overheating-optimized build resulted in the largest total distortion. Atlas 3D predictions aligned closely with scan data, particularly in identifying critical zones on sloped and unsupported surfaces. Sustainability and cost analysis revealed that the surface area strategy had the highest impact in reducing CO₂ emissions and production costs (~€832 and ~900 g CO₂/part), while support volume and print time orientations reduced cost by more than 20% and halved emissions. Energy consumption followed the same trend, with support volume and print time optimisations requiring only ~2 kWh/part compared to nearly 5 kWh/part for surface area, and overheating minimisation. These findings underscore the importance of integrating distortion simulation, cost, and environmental criteria into orientation selection to achieve balanced, high-performance LPBF manufacturing.

This study investigates the physicochemical and toxicological properties of Scalmalloy powder emissions generated during Laser Powder Bed Fusion (LPBF), focusing on the impact of particle morphology, oxidation, and size distribution on biological responses. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analyses revealed significant variations in particle characteristics, with the highest oxidation levels and irregular morphologies observed in exhaust-derived powders. In vitro cytotoxicity evaluations using A549 lung epithelial cells showed significant reductions in cell viability (~60 to 69%) and increased oxidative stress (p < 0.05) upon exposure to virgin sieved (85%). Indirect exposure through media incubation resulted in minimal cytotoxicity, suggesting that metal dissolution plays a limited role in toxicity under the studied conditions. The findings highlight the influence of particle morphology and oxidation on cytotoxic responses and underscore the need for controlled powder handling to mitigate occupational exposure risks in LPBF environments.

Laser Powder Bed Fusion (LPBF)-processed AlSi10Mg produces highly heterogeneous microstructures, where fine α-Al cells, Si-rich networks, and melt-pool boundaries govern local mechanical behavior. Nanoindentation has emerged as a key tool for probing these variations, yet systematic understanding of the links between processing parameters, microstructure, and nano-mechanical response remains limited. This critical review examines how laser processing parameters influence local mechanical response through their impact on microstructural features. Key challenges in interpreting nanoindentation are highlighted, alongside inconsistencies in experimental protocols and reporting practices that hinder cross-study comparisons. Beyond summarizing existing findings, underexplored aspects of nanoindentation in LPBF AlSi10Mg are identified, including spatially correlated microstructure-mechanical mapping, depth-resolved measurements, and integration with advanced characterization and data-driven approaches. By synthesizing current knowledge and clarifying methodological constraints, this review positions nanoindentation not merely as a descriptive tool, but as a mechanistically informed approach for linking processing conditions, microstructural heterogeneity, and local mechanical response. These insights aim to support more rigorous interpretation of small-scale mechanical data and to guide future studies toward predictive understanding and rational process optimization in additively manufactured aluminum alloys.