Powder Additive Manufacturing Research Articles

Investigating the effect of glass filler on the aging behavior of polymer powders for additive manufacturing

Powdered-polymer additive manufacturing processes, including selective laser sintering, high-speed sintering, and multijet fusion, have seen increasing usage throughout a range of industries, with corresponding requirements for better knowledge of material and component behavior. These processes involve the preheating, and subsequent selective melting, of consecutive layers of powder. Upon completion of the manufacturing process, unmelted powder can be recovered from the build chamber and, depending on its quality, reused for future part manufacture. Powder recovered in such a way can undergo a number of changes as a result of being held at elevated temperatures for extended times during the manufacturing process. Previous research has investigated these effects for polymer powders, with a particular emphasis on the most common polymer additive manufacturing powder, Nylon-12. In this work, we use a variety of characterization techniques, specifically size exclusion chromatography, differential scanning calorimetry, and rotational rheometry, to investigate this behavior for a glass-filled nylon-12 material, in order to identify any effects of the glass filler on material changes and on the properties of parts produced using these materials.

Correlation between particle size, secondary dendrite arm spacing, and local cooling rate in gas-atomized stainless steel powders for additive manufacturing

Correlation between particle size, secondary dendrite arm spacing, and local cooling rate in gas-atomized stainless steel powders for additive manufacturing

Classification of Metallic Powder Morphology Using Traditional and Automated Static Image Analysis: A Comparative Study

Characterizing powder feedstock is crucial for ensuring the quality and reliability of parts produced through metal additive manufacturing (AM). The morphology of particles impacts the flowability, packing density, and spreadability of powders, affecting productivity and part quality. A new methodology has been developed to classify particle morphological features in AM powder feedstocks, such as spherical or elongated shapes, and the presence of satellites and facets. This approach uses multiple descriptors for quantitative evaluation. The results from shape descriptors can vary based on image resolution, gray/color thresholding, and software algorithms. There are various commercial systems available for characterizing particle shape, some of which use images taken of static particles, while others use images of particles in motion. This diversity can lead to differences in powder characterization across laboratories with different equipment and methods. This paper compares results from a particle classification approach using two software programs that work with metallographic images with those from an automated static particle analyzer. While traditional methods offer higher resolution and precision, this study shows that automated systems can achieve similar particle shape classification using different shape descriptors and thresholds.

Investigation of Bamboo Powder Self-Adhesive Molding Method Using Semiconductor Laser for Additive Manufacturing

The purpose of this study is to explore the use of laser additive manufacturing of bamboo powder to produce items with fewer variations than the traditional heat press method using a die. Although metal and resin powders are commonly used in powder additive manufacturing, bamboo powder presents unique challenges owing to its lack of material uniformity, low carbonization temperature, and dependence on pressure for adhesion. To address these issues, the appropriate laser power and irradiation time were determined by irradiating the laser at several power levels and examining its effects on the powder temperature and chemical changes during molding. The results indicated that rapid heating occurred at approximately 150 °C, and carbonization began at approximately 190 °C. As the energy loss for carbonization decreases with increasing laser power, this method is expected to be effective for producing bamboo products with fewer variations. In addition, restriction of continuous oxygen inflow by the glass plate lid makes it feasible to prevent heat generation and carbonization. Furthermore, pressurization by the glass plate makes it feasible to improve adhesion. Future research will focus on the suppression of carbonization by inert gas and heating at low temperatures for long periods of time, as well as the effects of different magnitudes of pressure on the process.

A feasibility study on the circular manufacturing of sustainable metal additive manufacturing powders from machining chips

Abstract The holistic vision of the research is to develop a circular hybrid manufacturing framework to achieve ‘Net Zero’ for additive manufacturing sector by producing sustainable powders from production scrap/machining chips. This paper reports an initial feasibility study results from the foundation stage of the circular hybrid manufacturing framework that centres on generating additive manufacturing powders via solid-state crushing/ball milling of machining chips at room temperature. Here, the viability of the ball milling process to produce additive powders from three readily available chip materials is evaluated, viz. a low carbon steel (AISI 1020), and two aluminium alloy chips (AA6082-T6 and AA5083-H111). The ball-milled powders were characterised in terms of their morphology, size distribution, flowability and phase analysis. The morphology/size distributions were found to be influenced by the chip materials and their length scale. Single-track laser melting of pre-placed AA6082 ball-milled powder particles was subsequently performed to emulate the laser powder bed fusion process. Cross-sectional micrographs demonstrated melting and bonding of the ball-milled particles to the AA6082 substrate. A further feasibility trial was undertaken to fabricate cubes from the ball-milled AA5083 powders via the powder bed fusion process. The microhardness (71–88 HV0.01) and microstructure of the specimens were comparable to rolled AA5083-H111 plates. Graphical Abstract

Multi-scale numerical modeling of inductively coupled plasma spheroidization of refractory tungsten powders for additive manufacturing

Multi-scale numerical modeling of inductively coupled plasma spheroidization of refractory tungsten powders for additive manufacturing

Preparation and flow characterization of Al2O3/FeNi composite powders for additive manufacturing

Preparation and flow characterization of Al2O3/FeNi composite powders for additive manufacturing

High-performance spherical W-Cu composite powders for additive manufacturing via spray granulation and cold isostatic pressure sintering

High-performance spherical W-Cu composite powders for additive manufacturing via spray granulation and cold isostatic pressure sintering

A novel methodology for predicting energy and time consumption in additive manufacturing powder bed fusion with electron beam system using neural network

Evaluating the time and energy consumed by the machine during the process is essential for assessing the efficiency and eco-friendliness of the manufacturing method. However, adapting macro models for conventional manufacturing processes to additive manufacturing fails to adequately capture the intricate relationship between energy/time and process design, particularly for complex processes such as powder bed fusion with electron beam melting. This work highlights the limitations of the current modelling approach. It introduces a new methodology that, relying on the beam behaviour at the individual layer level, considers how build design, machine logic, and layer-wise energy consumption influence total energy consumption. Three typical structures, corresponding to three sets of process parameters, are examined: bulk material, overhang support structures, and net/lattice structures. These structures are incorporated into several real components arranged in 25 builds, each with varying nesting degrees, packing densities, build heights, and projections on the build platform. The manufacturing process time for these 25 builds is simulated using software on an Arcam A2X machine, a powder bed fusion with an electron beam system. Data are then analysed using an empirical literature model to highlight the impact of build design and process parameters on total specific energy consumption. Owing to the limitations of the macro empirical model, an artificial neural network based on a multilayer perceptron is employed to develop a model that predicts manufacturing time and energy consumption at the layer level. This model introduces a novel approach to describing the complexity of the build design, making layer-wise energy consumption a complex function of various geometrical features and process parameters. The neural network is trained, tested, and validated using data from six experimental builds, demonstrating its validity and accuracy. This work provides a practical tool for optimising build layouts in EB-PBF. By predicting energy consumption and processing time for different layouts or nesting, the model helps identify the most efficient configuration—minimising the production times and energy demand. Unlike existing models, it incorporates real machine data to accurately capture the impact of complexity, enabling more efficient and sustainable manufacturing.

Investigation of Lattice Geometries Formed by Metal Powder Additive Manufacturing for Energy Absorption: A Comparative Study on Ti6Al4V, Inconel 718, and AISI 316L

Impact absorbers are needed in many different areas in terms of energy absorption and crashworthiness. While the design of these structures is expected to increase mechanical performance, they are expected to be lightweight, and when evaluated in this context, lattice structures come to the fore. In this study, impact absorbers, also known as crash boxes, consisting of lattice structures designed to increase energy absorption performance were fabricated by a new manufacturing method, metal powder additive manufacturing, and their mechanical performance was experimentally investigated under quasi-static axial loading, and energy absorption data were obtained. The specimens were designed from Ti6Al4V, INC 718, and AISI 316L materials by forming 18 matrix structures with square and hexagonal geometries. According to this study, the lattice structures absorbed up to 4.5 times more energy than the shell structures of a similar material group. According to the normalized values among all samples, the hexagonal sample made of Ti6Al4V material showed 4.3 times higher energy absorption efficiency. The AISI 316L material showed the best crushing performance due to its ductile structure.

Process and Properties of Al-Mg-Er-Zr-Sc High-Strength Aluminum Alloy Powder Prepared by Vacuum Induction Melting Gas Atomization.

The Er-Zr-Sc-modified Al-Mg alloys produced by additive manufacturing (AM) exhibit good formability and excellent mechanical properties, and present great potential for applications in the fields of aerospace and automotive fields. In this work, the preparation process of Al-4.5Mg-0.7Er-0.5Zr-0.3Sc high-strength aluminum alloy powder for additive manufacturing by vacuum induction melting gas atomization (VIGA) was investigated. With the goal of obtaining excellent sphericity and higher powder yield in the particle size range of 15~53 μm, a new type atomizer with optimized convergence angle and tube extension length was designed based on finite element numerical simulation and experimental research, and the optimal atomization processing parameters were determined. The results revealed that when the convergence angle was 32° and the extension length was 5 mm, the large negative pressure and suction force at the tube outlet could facilitate the smooth flow of the melt and a refined powder particle size; when the melt temperature was 800 °C and the atomization pressure was 3.25 Mpa, the melt had low viscosity and the atomization gas could fully interact with the melt. Meanwhile, the melt droplets had suitable cooling conditions, avoiding the generation of irregular powders and improving the powder sphericity. Under the above optimal processing parameters, the prepared powders were spherical or nearly spherical with fine particle size and a high yield of about 39.45%.

The Changes of Rheological Behavior of Ti-6Al-4V Alloy Powders for Additive Manufacturing by Powder Oxidation

The Changes of Rheological Behavior of Ti-6Al-4V Alloy Powders for Additive Manufacturing by Powder Oxidation

On the use of the Fourier transform to determine contact curvature distributions in additive manufacturing powders

On the use of the Fourier transform to determine contact curvature distributions in additive manufacturing powders

Laser spheroidization combined with coaxial powder feeding: A novel strategy for preparing spherical powders for additive manufacturing

Laser spheroidization combined with coaxial powder feeding: A novel strategy for preparing spherical powders for additive manufacturing

State-enhanced attention network for optimisation of energy and yield in gas atomised metal powder production

Gas atomisation is a widely used technique for producing spherical metal powder feedstock for additive manufacturing. However, the process parameters suffer from variability and inefficiency in balancing powder yield, energy consumption, and particle size distribution. Optimising these complex, interdependent parameters pose a significant challenge. This work proposes a novel State-Enhanced Attention Network architecture in a framework that simultaneously optimises yield and energy consumption during nitrogen gas atomisation for sustainable metal powder production. The novelty lies in integrating processed long-term memory states with the attention mechanism, enabling nuanced attention weighting. This allows the model to leverage global sequence context and recent state information for improved yield and energy predictions. The proposed network is trained and integrated into a non-dominated sorting genetic algorithm to enable multi-objective optimisation. This framework evolves a set of Pareto-optimal solutions that balance trade-offs between maximising yield and minimising energy consumption. The approach is evaluated using augmented real-world data from an industrial gas atomisation plant. The proposed model demonstrates significantly improved predictive accuracy on real-world datasets, compared with baseline deep learning models. Results highlight the capabilities of the proposed technique for automated, data-driven optimisation of gas atomisation, simultaneously improving yield, energy efficiency, quality control, and sustainability. The integrated deep learning and evolutionary optimisation framework also provides an innovative solution for enhanced control of additive manufacturing powder production processes.

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing—Part II: Processability by Powder Bed Fusion of Metals Using a Laser Beam

Powder bed fusion of metals using a laser beam (PBF‐LB/M) is an additive manufacturing process for the direct production of metallic components using metal powder as a starting material. In order to improve the properties of the PBF‐LB/M components and increase the process efficiency by improving the laser absorption and the flowability of the metal powders used, powders are additivated with nanoparticles prior to processing by PBF‐LB/M in the literature. In the present work, X2CrNiMo17‐12‐2 (1.4404/316L) powder coated with silicon carbide (SiC), silicon (Si), and silicon nitride (Si3N4) nanoparticles in volume fractions of 0.25–1 vol% is processed by PBF‐LB/M. Initially, multilayer single tracks (MST) are produced. In the next step, dynamic hatch (DH) samples are manufactured based on the MST tests. The coating of the powder feedstock with nanoparticles leads to an enlargement of the process window in the DH test and an increase in the maximum build rate.

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing – Part I: Surface, Flowability, and Optical Properties of SiC, Si, and Si3N4 Coated Metal Powders

The coating of metal powders with nanoparticles renders the possibility to improve powder bed fusion of metals using a laser beams (PBF‐LB/M) from the powder side of view. The literature indicates that nanoparticle coatings not only improve mechanical properties of final parts but also decrease manufacturing costs through higher process efficiencies. Herein, coatings of X2CrNiMo17‐12‐2 with silicon, silicon carbide, and silicon nitride are presented using different particle sizes of 65–200 nm and different coating surface coverages, which lead to homogeneous and stable coatings. A scale‐up and process parameter variations demonstrate the reproducibility of the process and also the relevance of the used processes for commercial applications. The main finding of this study is that the surface roughness due to nanoparticle coating is related directly to the powder flowability measured with the ring shear tester as a property function. In addition, for similar coating structures, the nanoparticle material influences the flowability due to the nature of its Hamaker constant. However, the reflectance of coated metal powders is dependent on the reflection of the coating material, the induced surface structure, and the amount of coated particles. These relationships might be transferred to improvements in the PBF‐LB/M process.

Metal Powder Production by Atomization of Free-Falling Melt Streams Using Pulsed Gaseous Shock and Detonation Waves

A new method of producing metal powders for additive manufacturing by the atomization of free-falling melt streams using pulsed cross-flow gaseous shock or detonation waves is proposed. The method allows the control of shock/detonation wave intensity (from Mach number 4 to about 7), as well as the composition and temperature of the detonation products by choosing proper fuels and oxidizers. The method is implemented in laboratory and industrial setups and preliminarily tested for melts of three materials, namely zinc, aluminum alloy AlMg5, and stainless steel AISI 304, possessing significantly different properties in terms of density, surface tension, and viscosity. Pulsed shock and detonation waves used for the atomization of free-falling melt streams are generated by the pulsed detonation gun (PDG) operating on the stoichiometric mixture of liquid hydrocarbon fuel and gaseous oxygen. The analysis of solidified particles and particle size distribution in the powder is studied by sifting on sieves, optical microscopy, laser diffraction wet dispersion method (WDM), and atomic force microscopy (AFM). The operation process is visualized by a video camera. The minimal size of the powders obtained by the method is shown to be as low as 0.1 to 1 μm, while the maximum size of particles exceeds 400–800 μm. The latter is explained by the deficit of energy in the shock-induced cross-flow for the complete atomization of the melt stream, in particular dense and thick (8 mm) streams of the stainless-steel melt. The mass share of particles with a fraction of 0–10 μm can be at least 20%. The shape of the particles of the finest fractions (0–30 and 30–70 μm) is close to spherical (zinc, aluminum) or perfectly spherical (stainless steel). The shape of particles of coarser fractions (70–140 μm and larger) is more irregular. Zinc and aluminum powders contain agglomerates in the form of particles with fine satellites. The content of agglomerates in stainless-steel powders is very low. In general, the preliminary experiments show that the proposed method for the production of finely dispersed metal powders demonstrates potential in terms of powder characteristics.

CT-scan, SEM, EDX, flowability, rheology, permeability and tensile test analysis of recycled Ti6Al4V powders for 3D printing

This study investigates the use of three different recycled powder sets for 3D printing biomedical components, addressing the growing need for sustainable manufacturing solutions. Despite the widespread use of virgin powder in additive manufacturing, there is limited research on the performance of reconditioned powders. This study fills that gap by analyzing the performance of virgin powder, reconditioned powder, a mixed powder containing 50% fresh and 50% 7-times recycled powder, and a 7-times recycled powder (C7-only). The powders were systematically sampled, sieved, and used to print test samples such as cubes and tensile bars. The chemical composition, powder size distribution, and flowability of the powders were examined and correlated with mechanical tensile tests and porosity levels in the printed parts. Results indicate that reconditioned powder exhibits superior flowability compared to the mixed or C7-only powders. Notably, the tensile strength and strain of parts made from C7-only and mixed powders surpassed those made from virgin powder, despite a slightly higher porosity in the recycled powder samples. This study highlights the potential advantages of using reconditioned powder in the additive manufacturing of biomedical components, offering a less costly and environmentally friendly alternative for producing sensitive medical parts.

Ready-to-Use Composite Fused Deposition Modeling Filaments Produced With Polylactic Acid and Recycled Nd–Fe–B Nanocrystalline Powder for Additive Manufacturing of Bonded Magnets

Ready-to-Use Composite Fused Deposition Modeling Filaments Produced With Polylactic Acid and Recycled Nd–Fe–B Nanocrystalline Powder for Additive Manufacturing of Bonded Magnets