Additive manufacturing is acknowledged as a key enabling technology, although its adoption is still constrained to niche applications. A promising area for this technology is the production of electrical machines (EMs) and/or their main components (e.g. magnetic cores, windings, heat exchangers, etc.) due to the potential of creating lightweight, highly efficient rotating motors, suitable for applications requiring a low moment of inertia. This work investigates the readiness of metal additive manufacturing, specifically Laser Powder Bed Fusion (LPBF), applied to the field of EMs to bridge the gaps of how to use this technological approach in this field. A soft magnetic material featuring high silicon content (Fe-5.0%w.t.Si) has been developed for LPBF and a rotor has been 3D-printed for a switched reluctance machine. The printed rotor was assembled into a conventionally laminated stator and the performance of the whole machine was evaluated. Its performance was compared against an identical machine equipped with a laminated rotor of the same dimensions made of conventional non-oriented silicon steel. A comparative study was carried out through both finite element simulations and experimental tests. The efficiency of the two machines was assessed together with the principal electrical and mechanical quantities under several operating conditions.

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.