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Powder bed fusion of copper has been extensively investigated using both laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) additive manufacturing technologies. Each technique offers unique benefits as well as specific limitations. Near-infrared (NIR) laser-based LPBF is widely accessible; however, the high reflectivity of copper limits energy absorption, thereby resulting in a narrow processing window. Although optimized parameters can yield relative densities above 97%, issues such as keyhole porosity, incomplete melting, and anisotropy remain concerns. Green lasers, with higher absorptivity in copper, offer broader process windows and enable more consistent fabrication of high-density parts with superior electrical conductivity, often reaching or exceeding 99% relative density and 100% International Annealed Copper Standard (IACS). Mechanical properties, including tensile and yield strength, are also improved, though challenges remain in surface finish and geometrical resolution. In contrast, Electron Beam Powder Bed Fusion (EB-PBF) uses high-energy electron beams in a vacuum, eliminating oxidation and leveraging copper’s high conductivity to achieve high energy absorption at lower volumetric energy densities (~80 J/mm3). This results in consistently high relative densities (>99.5%) and excellent electrical and thermal conductivity, with additional benefits including faster scanning speeds and in situ monitoring capabilities. However, EB-PBF processes in general face their own limitations, such as surface roughness and powder smoking. This paper provides a comprehensive review of the current state of laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) powder bed fusion processes for the additive manufacturing of copper, summarizing key trends, material properties, and process innovations. Both approaches continue to evolve, with ongoing research aimed at refining these technologies to enable the reliable and efficient additive manufacturing of high-performance copper components.

The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. The model is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) µm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.

Links between dynamical Frenkel defects and collective diffusion of fluorides in β -PbF 2 are explored using Born–Oppenheimer molecular dynamics. The calculated self-diffusion coefficient and ionic conductivity are 3.2 × 10 −5 cm 2 s −1 and 2.4 Ω −1 cm −1 at 1000 K in excellent agreement with pulsed field gradient and conductivity measurements. The calculated ratio of the tracer-diffusion coefficient and the conductivity-diffusion coefficient (the Haven ratio) is slightly less than unity (about 0.85), which in previous work has been interpreted as providing evidence against collective ‘multi-ion’ diffusion. By contrast, our molecular dynamics simulations show that fluoride diffusion is highly collective. Analysis of different mechanisms shows a preference for direct collinear ‘kick-out’ chains where a fluoride enters an occupied tetrahedral hole/cavity and pushes the resident fluoride out of its cavity. Jumps into an occupied cavity leave behind a vacancy, thereby forming dynamic Frenkel defects which trigger a chain of migrating fluorides assisted by local relaxations of the lead ions to accommodate these chains. The calculated lifetime of the Frenkel defects and the collective chains is approximately 1 ps in good agreement with that found from neutron diffraction. This article is part of the Theo Murphy meeting issue ‘Understanding fast-ion conduction in solid electrolytes’.

Designers are continuously searching for materials or meta-structures, also inspired by nature, that exhibit favourable strength-to-weight ratios, substantial heat transfer capabilities, and efficient energy absorption. One particular example includes fractal geometries, which usually consist of intricate three-dimensional geometrical structures and are challenging to produce through traditional manufacturing methods. In this regard, this study analyses the performance of a three-dimensional cross-based fractal structure (3D-CFS) designed for energy absorption and manufactured using polymeric materials. Mathematically, the geometry is obtained using a 3D Greek cross repeated in the 3D space according to the fractal principle. Owing to the intricate final structure, samples are fabricated using an Additive Manufacturing system based on powder bed fusion with a laser beam and infrared light. The study is carried out using two polymeric materials, polyamide and thermoplastic polyurethane, and the mechanical response of the structure is analysed under dynamic compression tests. The tested geometries consisted of samples with a single 3D-CFS cell, various volume fractions and a configuration with multiple cells that emulated a possible layout for linear helmet application. The findings indicate that the 3D-CFS is a promising geometry for eventual implementation into shock absorption applications, specifically in personal protective equipment (PPE) usage.

This review explores the critical role of powder quality in metal 3D printing and the importance of effective powder recycling strategies. It covers various metal 3D printing technologies, in particular Selective Laser Melting, Electron Beam Melting, Direct Energy Deposition, and Binder Jetting, and analyzes the impact of powder characteristics on the final part properties. This review highlights key challenges associated with powder recycling, including maintaining consistent particle size and shape, managing contamination, and mitigating degradation effects from repeated use, such as wear, fragmentation, and oxidation. Furthermore, it explores various recycling techniques, such as sieving, blending, plasma spheroidization, and powder conditioning, emphasizing their role in restoring powder quality and enabling reuse.

Additive manufacturing allows for a great degree of design freedom and is rapidly becoming a mainstream manufacturing process. However, as in all manufacturing processes, it has its limitations and specificities. Equipping engineers with this knowledge allows for a higher degree of optimization, extracting the most out of this technology. Therefore, a specific part design was devised and created via L-PBF (Laser Powder Bed Fusion) using AlSi10Mg powder. Certain parameters were varied to identify the influence on material density, hardness, roughness, residual stress and microstructures. It was found that on heat treated parts laser pattern strategy is one of the most influential aspects, showing that chessboard and stripes 67° improved outcome; average Ra roughness varied between 8–12 µm, residual stress was higher on vertical surfaces than horizontal surfaces, with the combination of support structures and stripes 67° strategies generating the lowest residual stress (205 MPa on a lateral/vertical face), hardness was non-orientation dependent and larger on samples with chessboard fabrication strategies, while microstructures were composed of α–Al dendrites surrounded by Si particles. The distribution and grain size of the microstructure is dependent on location regarding melt pool and HAZ area. Furthermore, Al–Mg oxides were encountered on the surface, along with pores generating from lack of fusion.

The powder bed fusion (PBF) process using Ti–6Al–4V powder has the specific application in additive manufacturing of a high-performance structural parts in the aerospace and medical industries. The PBF involves the repeated accumulation of laser melted layers. Consequently, high anisotropic residual stresses and local temperature accumulation occur during the rapid melting and cooling in the process. These factors affect the mechanical properties of the as-built structure. In particular, we revealed the effective interval island laser-scanning strategy with less grain size, thermal effect and anisotropic residual stresses of the additively manufactured structure, compared to those of the strip and continuous laser-scanning strategies. Through the cantilever experiment, it was confirmed that the interval island laser-scanning strategy reduced deformation by up to 7.7% compared to that of the conventional strip laser-scanning strategy due to the reduction of anisotropic residual stresses.

Designers have been fascinated by exploring new geometries made by high-performance structures. In more specific terms, biological systems have always been proven to be characterised by sophisticated structures with adapting properties to nature challenges. Insightful analyses have shown how these natural structures are dominated by characteristics such as high energy absorption and elevated strength-weight proportion. Fractal geometries are examples of bio-inspired mathematical objects whose complex 3D structures can be obtained only by advanced manufacturing systems, such as additive manufacturing (AM). This study investigates the feasibility and energy absorption properties of a novel fractal structure based on a 3D Greek cross (3D-CFS). The structure was designed with different volume fractions and produced by powder bed fusion (PBF) AM processes in polyamide (PA12) and thermoplastic polyurethane (TPU). The 3D-CFS properties are investigated under quasi-static and dynamic compression tests. The analysis revealed that for certain geometrical parameters, the manufacturing of the structures is constrained by the sintered powder entrapped in the structure. However, in the case of powder-free structures, the results showed a high impact resistance and cushioning capability. Overall, in terms of specific energy absorption (SEA), the TPU structures showed values between 2.5 and 3.5 kJ/kg, while PA12 ones are between 7.5 and 17.4 kJ/kg, making the 3D-CFS structure compatible with personal protective equipment (PPE) applications. Compared to the literature data on cellular structures made by AM, 3D-CFS performs considerably better. Also, PA12 3D-CFS is better, with a SEA value up to 170% higher than that of a typical material employed for head PPE (e.g. EPS-60 SEA equal to 2.76 kJ/kg). In contrast, TPU 3D-CFS looks more promising in the case of multiple impact conditions.

Additive manufacturing (AM) technologies have gained considerable attention in recent years as an innovative method to produce high entropy alloy (HEA) components. The unique and excellent mechanical and environmental properties of HEAs can be used in various demanding applications, such as the aerospace and automotive industries. This review paper aims to inspect the status and prospects of research and development related to the production of HEAs by AM technologies. Several AM processes can be used to fabricate HEA components, mainly powder bed fusion (PBF), direct energy deposition (DED), material extrusion (ME), and binder jetting (BJ). PBF technologies, such as selective laser melting (SLM) and electron beam melting (EBM), have been widely used to produce HEA components with good dimensional accuracy and surface finish. DED techniques, such as blown powder deposition (BPD) and wire arc AM (WAAM), that have high deposition rates can be used to produce large, custom-made parts with relatively reduced surface finish quality. BJ and ME techniques can be used to produce green bodies that require subsequent sintering to obtain adequate density. The use of AM to produce HEA components provides the ability to make complex shapes and create composite materials with reinforced particles. However, the microstructure and mechanical properties of AM-produced HEAs can be significantly affected by the processing parameters and post-processing heat treatment, but overall, AM technology appears to be a promising approach for producing advanced HEA components with unique properties. This paper reviews the various technologies and associated aspects of AM for HEAs. The concluding remarks highlight the critical effect of the printing parameters in relation to the complex synthesis mechanism of HEA elements that is required to obtain adequate properties. In addition, the importance of using feedstock material in the form of mix elemental powder or wires rather than pre-alloyed substance is also emphasized in order that HEA components can be produced by AM processes at an affordable cost.

Modern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of melt-pool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction of melt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.