Powder Bed Fusion Additive Manufacturing Research Articles

The Printability, Microstructure, and Mechanical Properties of Fe80-xMnxCo10Cr10 High-Entropy Alloys Fabricated by Laser Powder Bed Fusion Additive Manufacturing.

This work investigated the effect of Fe/Mn ratio on the microstructure and mechanical properties of non-equimolar Fe80-xMnxCo10Cr10 (x = 30% and 50%) high-entropy alloys (HEAs) fabricated by laser powder bed fusion (LPBF) additive manufacturing. Process optimization was conducted to achieve fully dense Fe30Mn50Co10Cr10 and Fe50Mn30Co10Cr10 HEAs using a volumetric energy density of 105.82 J·mm-3. The LPBF-printed Fe30Mn50Co10Cr10 HEA exhibited a single face-centered cubic (FCC) phase, while the Fe50Mn30Co10Cr10 HEA featured a hexagonal close-packed (HCP) phase within the FCC matrix. Notably, the fraction of HCP phase in the Fe50Mn30Co10Cr10 HEAs increased from 0.94 to 28.10%, with the deformation strain ranging from 0 to 20%. The single-phase Fe30Mn50Co10Cr10 HEA demonstrated a remarkable combination of high yield strength (580.65 MPa) and elongation (32.5%), which surpassed those achieved in the FeMnCoCr HEA system. Comparatively, the dual-phase Fe50Mn30Co10Cr10 HEA exhibited inferior yield strength (487.60 MPa) and elongation (22.3%). However, it displayed superior ultimate tensile strength (744.90 MPa) compared to that in the Fe30Mn50Co10Cr10 HEA (687.70 MPa). The presence of FCC/HCP interfaces obtained in the Fe50Mn30Co10Cr10 HEA resulted in stress concentration and crack expansion, thereby leading to reduced ductility but enhanced resistance against grain slip deformation. Consequently, these interfaces facilitated an earlier attainment of yield limit point and contributed to increased ultimate tensile strength in the Fe50Mn30Co10Cr10 HEA. These findings provide valuable insights into the microstructure evolution and mechanical behavior of LPBF-printed metastable FeMnCoCr HEAs.

Effect of parameters on quality of IN718 parts using laser additive manufacturing

The objective of this study is to determine the effect of process parameters such as laser power, scanning speed and layer thickness on the mechanical properties, microstructure and fracture mechanism of Inconel 718 using laser powder bed fusion additive manufacturing process. Full factorial design, regression modelling and response surface methodology are used to optimise the process parameters. The highest tensile strength occurs at 350 W laser power, 1300 mm/s scanning speed and 30 µm layer thickness. The optimal parameters for multi-objective optimisation are identical to those used for single-objective optimisation for tensile strength. The difference between the predicted and actual tensile strength is 0.33%. This work shows a successful methodology using IN718 superalloy for various applications in aerospace industry.

Selective Laser Sintering versus Multi Jet Fusion: A Comprehensive Comparison Study Based on the Properties of Glass Beads‐Reinforced Polyamide 12

Selective laser sintering (SLS) and multi jet fusion (MJF) are the most widespread powder bed fusion additive manufacturing techniques for fabricating polymeric parts since they offer great design flexibility, productivity, and geometrical accuracy. However, these technologies differ in the thermal energy source used to melt the powders as well as the innovative use of printing agents featured in the latter one to promote material consolidation and to avoid thermal bleeding at the part contours. The use of a single powder made of glass beads‐reinforced polyamide 12 (PA12/GB) for the fabrication of MJF and SLS samples makes possible a systematic comparison of the printed parts properties. A thoughtful analysis of the microstructure and mechanical properties of the samples reveals differences and peculiarities between the two technologies. SLS exhibits lower porosity and higher mechanical performances when the parts are printed along the build plane thanks to the powerful heating ensured by the laser. In contrast, MJF samples show higher mechanical isotropy with great flexural and tensile behavior for vertically oriented parts. The role of glass beads in the material behavior is defined by their mechanical properties, meaning higher rigidity and lower strength compared to neat PA12, and fracture mechanism.

Laser powder bed fusion additive manufacturing of molybdenum using a nitrogen build atmosphere

A method was developed to suppress the crack formation during laser powder bed fusion (LPBF) of molybdenum (Mo). The method uses a nitrogen (N2) atmosphere to fabricate crack-free samples without any additional processing. To compare with the effect of the N2 atmosphere, samples were prepared under an argon (Ar) atmosphere as well with the same process parameters. The microstructure analyses by optical and high-resolution electron microscopy revealed that crack-free samples were produced under this condition, whereas grain boundary cracks were observed throughout the sample fabricated in Ar atmosphere. To understand the mechanism of crack prevention by nitrogen (N), the energetics of solute components in the BCC-Mo structure were studied by Density Functional Theory (DFT). The theoretical calculations showed that the introduction of N into the BCC-Mo hinders oxygen (O) diffusion, causing O entrapment in the lattice at interstitial positions. As a result, reduced oxides within grains and at grain boundaries (GB) were observed. The results are consistent with the experimental observation made from the fracture surface investigation. The reduced embrittlement of GB from the reduced GB oxide formation is believed to suppress GB cracking.

In-process monitoring and direct simulation of Argon shielding gas and vapour dynamics to control laser-matter interaction in laser powder bed fusion additive manufacturing

Laser powder bed fusion (L-PBF) additive manufacturing (AM) enables the fabrication of parts with precise dimensional control, freedom of design and material properties similar to or better than those fabricated using traditional manufacturing approaches. AM quality control depends upon the fundamental of the laser-matter interaction during metal AM using L-PBF to exploit the potential use of the materials and process control. In this work, thermal-fluid dynamics in gas chamber experimentally and computationally is used to elucidate the interplay between vapour, liquid, and solid phases in L-PBF. It is revealed that the argon (Ar) shielding gas flow with varied inlet velocities by different nozzles has a pronounced effect to minimise the laser-fume interaction, resulting in the reduction in unstable metal vapour flow and enhancing laser absorptivity. In-process monitoring via high-speed visualisation has been used to understand the simultaneous gas plume dynamics as a result of vapourisation and subsequent laser-fume interaction, backed up by thermal-fluid flow simulation. Unfavourable process dynamics associated with unwanted defects such as lack of fusion can be avoided to improve process design and enhance process stability.

Correlation between surface and processing parameters of an Nb-48Ti alloy produced by laser powder bed fusion additive manufacturing

Correlation between surface and processing parameters of an Nb-48Ti alloy produced by laser powder bed fusion additive manufacturing

Influence of burnishing process on surface integrity of inconel 718 fabricated by laser powder bed fusion additive manufacturing

Influence of burnishing process on surface integrity of inconel 718 fabricated by laser powder bed fusion additive manufacturing

Interfacial characteristics of multi-material SS316L/IN718 fabricated by laser powder bed fusion and processed by high-pressure torsion

This study focuses on investigating the microstructure and mechanical properties across the interface of a multi-material, Austenitic Stainless Steel 316 L (SS316L) and Inconel 718 (IN718), fabricated using laser powder bed fusion (LPBF) additive manufacturing (AM) technique. Challenges such as distortion, porosity, and intermetallic formation are common in such structures. To address these challenges, high-pressure torsion (HPT), a severe plastic deformation (SPD) process, was employed to eliminate porosity and create a homogeneous microstructure. The samples underwent HPT with varying numbers of turns, namely quarter, half, one, five, and ten turns, at room temperature and under 6 GPa of pressure at a speed of 1 rpm. The process aimed to create ultrafine and nano-grains in the microstructure and reduce the defects. A comprehensive characterization approach was adopted to analyze the interface. The results showed reduced porosity, improved bonding, and enhanced material density through HPT. X-ray diffraction (XRD) analysis confirmed the retention of the austenitic face-centered cubic (FCC) phase in both materials. The dislocation density reached a saturation point of 1.85 × 1015 m−2, while the crystallite size decreased to 26 nm. Transmission electron microscopy (TEM) analysis provided insights into the microstructure, showing dislocation networks at lower torsional strain and homogenous nano-grains at higher torsional strain. Vickers microhardness and nanoindentation measurements demonstrated an increase in hardness with increasing torsional strain, with the peripheral region exhibiting rapid hardness saturation and the central region showing lower hardness due to reduced strain. Overall, this study provides valuable insights into the effects of HPT on multi-material SS316L/IN718, contributing to the understanding of their interfacial characteristics and enhanced utilization of multi-materials in various engineering applications.

A mechanistic explanation of shrinkage porosity in laser powder bed fusion additive manufacturing

This work focuses on the occurrence of shrinkage porosity and its manifestation in Alloy 718 during laser-powder bed fusion (PBF-LB) processing. Shrinkage porosity is a common defect in metal castings that degrades part performance. In traditional metal castings, the Niyama criterion is a reliable heuristic for predicting the formation of shrinkage porosity. However, the Niyama criterion’s applicability in the PBF-LB process remains unexplored, and there is no known and evaluated heuristic to predict shrinkage porosity in the PBF-LB manufactured parts. This work employs microstructure characterization and an analytical heat transfer model to develop a mechanistic explanation for the formation of shrinkage porosity in the PBF-LB process. The results show that the Niyama criterion could not effectively predict the occurrence of shrinkage porosity. Further, the formation of shrinkage porosity is primarily driven by secondary dendrite arm growth in the solidifying microstructure, where the transition to cellular growth at high cooling rates during solidification mitigates porosity by removing locations for pore formation. This means a heuristic based on solidification cooling rate could reliably predict the occurrence of shrinkage porosity. For practical use, shrinkage porosity process maps are presented for use in process design and control to directly aid shrinkage porosity mitigation in process planning. The process-shrinkage porosity relationship results also indicate that trends in PBF-LB manufacturing toward higher deposition temperatures and higher throughput are likely to aggravate the conditions for shrinkage porosity formation and further elevate the importance of the mitigation strategies presented in this work.

Laser Powder Bed Fusion Additive Manufacturing of Mo and TZM Exoskeleton with Cu Infiltration for New Heat Sinks Configuration

This study reports the fabrication and characterization of molybdenum (Mo) metal and titanium‐zirconium‐molybdenum (TZM) alloy exoskeletons with honeycomb cavity structures (HCS) that are infiltrated with oxygen‐free high conductivity (OFHC) Cu under an inert atmosphere as a potential replacement for Cu‐Mo‐Cu laminate in heat sink applications for power electronics semiconductors like GaAs. The thermal expansion behavior and the thermal loading of the starting Mo and TZM structures, and of the Cu‐infiltrated parts are evaluated. The fabricated Mo and TZM structures with density >99% and Mo‐based heat sinks with improved CTE (6.6 × 10−6 K−1) when compared to conventional Cu‐Mo‐Cu laminated heat sinks (CTE = 7.6 × 10−6 K−1). The new Mo and TZM structures promise superior performance due to their closer CTE to that of GaAs and similar semiconductors (CTE = 5.7 × 10−6 K−1). Exposure to temperatures up to 1073 K did not affect the Mo microstructure due to the inherent resistance to recrystallization, while exposure to 1373 K did reduce hardness. In contrast, TZM exoskeletons showed resistance to recrystallization even at 1373 K. The fabricated composite heat sinks showed thermal diffusivity (≈61 × 106 m2 s−1) that is within the upper limits of those reported for commercial laminated heat sinks (45 to 65 × 106 m2 s−1).

Multimodal and multiscale strengthening mechanisms in Al-Ni-Zr-Ti-Mn alloy processed by laser powder bed fusion additive manufacturing

The unique thermokinetics of laser-powder bed fusion additive manufacturing (L-PBFAM) has been exploited for development of a novel high-strength Al-Ni-Ti-Zr-Mn alloy. The addition of 0.5 wt% Mn leads to extraordinary improvement in ultimate tensile strength (502 MPa) and work hardening due to the activation of two Mn-induced strengthening mechanisms. First, by a bimodal particle strengthening effect due to Al31Mn6Ni12 nano-quasi-crystal particles rejected in inter-dendritic spaces and fibrous Al3Ni eutectic dendritic channels, which predominately contributes to the strength improvement, and second by solid solution strengthening from remaining Mn entrapped in Al. These mechanisms supplement the particle strengthening effect imparted by coherent and incoherent Al3(Ti,Zr) co-precipitates present at melt pool boundaries, dislocation strengthening due to solidification induced strain, and Hall-Petch and backstress strengthening effect due to heterogenous grain size distribution occurring at various length scales. The solidification dynamics and hierarchical heat distribution that are associated with L-PBFAM resulted in complex spatial variations in these strengthening phenomena and were investigated via a high-throughput multiscale structure–property correlation that involved thermokinetic simulation, X-ray diffraction, high-resolution nanoindentation mapping, and site-specific transmission electron microscopy of the alloy.

Effective Spiral Laser Path for Minimizing Local Heating and Anisotropic Microstructures in Powder Bed Fusion Additive Manufacturing

Effective Spiral Laser Path for Minimizing Local Heating and Anisotropic Microstructures in Powder Bed Fusion Additive Manufacturing

Accelerating phase-field simulation of three-dimensional microstructure evolution in laser powder bed fusion with composable machine learning predictions

Phase-field (PF) modeling is a versatile physics-based computational method that has been used to simulate the evolution of microstructures. The PF method can produce accurate microstructures but suffers from a high computational cost, limiting its use in length scales relevant to additive manufacturing. Using small-scale PF simulations as training data, we trained a surrogate machine learning (ML) model as a computationally cheaper alternative. We use a three-dimensional (3D) U-Net convolutional neural network and learn microstructure evolution in a supervised fashion. With initial microstructure and thermal history as inputs, the ML model can predict the resulting grain orientations at a high accuracy compared to the PF model. Computationally, the ML model is orders of magnitudes faster than the direct PF simulation in a GPU implementation, and scales favorably with increasing number of cells. By spatio-temporally composing multiple ML model predictions at the small-scale, we demonstrate a large-scale 64-layer simulation of a 2 mm × 2 mm × 2 mm cube for a powder bed fusion additive manufacturing process. The ML results revealed a mixture of equiaxed, columnar, and curved grains, comparable to experimental observations. Microstructures resulting from different toolpath strategies, and lack of fusion defects are also demonstrated. This approach paves the way for microstructure-driven process design.

Predicting meltpool depth and primary dendritic arm spacing in laser powder bed fusion additive manufacturing using physics-based machine learning

The long-term goal of this work is to predict and control the microstructure evolution in metal additive manufacturing processes. In pursuit of this goal, we developed and applied an approach which combines physics-based thermal modeling with machine learning to predict two important microstructure-related characteristics, namely, the meltpool depth and primary dendritic arm spacing in Nickel Alloy 718 parts made using the laser powder bed fusion (LPBF) process. Microstructure characteristics are critical determinants of functional physical properties, e.g., yield strength and fatigue life. Currently, the microstructure of LPBF parts is optimized through a cumbersome build-and-characterize empirical approach. Rapid and accurate models for predicting microstructure evolution are therefore valuable to reduce process development time and achieve consistent properties. However, owing to their computational complexity, existing physics-based models for predicting the microstructure evolution are limited to a few layers, and are challenging to scale to practical parts. This paper addresses the aforementioned research gap via a novel physics and data integrated modeling approach. The approach consists of two steps. First, a rapid, part-level computational thermal model was used to predict the temperature distribution and cooling rate in the entire part before it was printed. Second, the foregoing physics-based thermal history quantifiers were used as inputs to a simple machine learning model (support vector machine) trained to predict the meltpool depth and primary dendritic arm spacing based on empirical materials characterization data. As an example of its efficacy, when tested on a separate set of samples from a different build, the approach predicted the primary dendritic arm spacing with root mean squared error ≈ 110 nm. This work thus presents an avenue for future physics-based optimization and control of microstructure evolution in LPBF.

Achieving superelastic shape recoverability in smart flexible CuAlMn metamaterials via 3D printing

Despite the remarkable advancements in the additive manufacturing of metamaterials, tradeoffs remain between functionality and mechanical performance owning to static configuration, which limits their application, particularly in areas that require efficient multifunctionality. In this paper, we present a novel approach for fabricating multifunctional smart flexible metal metamaterials using laser powder bed fusion technology. This approach enables the reversible recovery superelastic strain exceeding 20 % with a 100 % recovery rate—ten times higher than that observed in the printed alloy. This is achieved by utilising an innovative metamaterial structural design and a novel shape memory alloy powder. To achieve the aforementioned purpose, the metamaterial unit cells were initially designed to ensure flexible deformation ability with a Poisson's ratio of zero. Then, we prepared a novel shape memory alloy composition of Cu-18at%Al-l0at%Mn-0.3 at%Si, which exhibited excellent printability and adaptability within the laser powder bed fusion additive manufacturing process. Additionally, the printed SMA exhibited superelasticity, one-way and two-way shape memory effect under varying parameters. Furthermore, the combination of multifunctionality into the flexible CuAlMn metamaterials was achieved by manipulating process parameters. Remarkably, the printed metamaterial demonstrates exceptional flexibility deformation, and presents superelasticity or shape memory effect, ensuring the recovery of its original shape after experiencing deformation. This work not only demonstrates the vast potential of utilising additive manufacturing technology for fabricating functional and adaptable metal metamaterials but also presents an innovative approach for creating smart metal metamaterial.

Effect of hard and soft re-coater blade on porosity and processability of thin walls and overhangs in laser powder bed fusion additive manufacturing

AbstractSpreading powder into thin layers is a fundamental step in the laser powder bed fusion (PBF-LB) additive manufacturing process. This step is called re-coating and it is typically performed using either a hard, soft or brush-type re-coater blade or a rotating roller, depending on the machine brand and model. With such variety in powder spreading approaches, the question arises whether the used re-coater type has a significant effect on the quality of parts produced? In this study, an industrial contact image sensor integrated to the re-coater of a PBF-LB system was used for powder bed quality monitoring. Powder bed images at 21 µm/pixel resolution, 184 mm scanning width and 95 mm/s re-coating speed were acquired. With this, the effect of using either soft (rubber) or hard (steel) re-coater blade on the processability of challenging features such as thin walls and steep overhangs was studied. In addition, porosity and dimensional accuracy of parts produced using either the soft or hard blade was analyzed with X-ray computed tomography. It is shown that when building bulk material without any complex features, both the hard and soft re-coating blade results in extremely low porosity ≤ 0.001% without any issues in the processability. However, when thin walls and overhangs are produced, differences in processability, porosity and dimensional accuracy are observed as a function of re-coater blade and part orientation. This is an important factor in understanding all the significant sources contributing to the variability on quality of parts produced using different PBF-LB machines.

Cracking mechanism of GH5188 alloy during laser powder bed fusion additive manufacturing

GH5188 cobalt-based superalloy components fabricated using laser powder bed fusion (LPBF) additive manufacturing technique possess promising application prospects in aviation industry, but high cracking susceptibility of the alloy remains an important concern. In this paper, cracking mechanism of the LPBFed GH5188 alloy was investigated by analyzing microstructure, fracture surfaces, composition distribution and grain boundary characteristics of the prepared sample. It was found that cracks consistently propagate intergranular at grain boundaries with high misorientation angle. The cracks were confirmed to be solidification cracks, due to the existence of fracture surface with dendritic morphology, as well as the absence of eutectic structure, carbide and precipitated phase. Lanthanum oxide particles were observed in the as-built alloy, which may hamper feeding of molten material at interdendritic region, promoting tearing of liquid films. Hence, it could be suggested that low O and La contents help in avoiding crack formation. This work could provide guidance for fabrication of crack-free LPBFed GH5188 alloy components.

A highly efficient computational approach for fast scan-resolved simulations of metal additive manufacturing processes on the scale of real parts

This article proposes a novel high-performance computing approach for the prediction of the temperature field in powder bed fusion (PBF) additive manufacturing (AM) processes. In contrast to many existing approaches to part-scale simulations, the underlying computational model consistently resolves physical scan tracks without additional heat source scaling, agglomeration strategies or any other heuristic modeling assumptions. A growing, adaptively refined mesh accurately captures all details of the laser beam motion. Critically, the fine spatial resolution required for resolved scan tracks in combination with the high scan velocities underlying these processes mandates the use of comparatively small time steps to resolve the underlying physics. Explicit time integration schemes are well-suited for this setting, while unconditionally stable implicit time integration schemes are employed for the interlayer cool down phase governed by significantly larger time scales. These two schemes are combined and implemented in an efficient fast operator evaluation framework providing significant performance gains and optimization opportunities. The capabilities of the novel framework are demonstrated through realistic AM examples on the centimeter scale including the first scan-resolved simulation of the entire NIST AM Benchmark cantilever specimen, with a computation time of less than one day. Apart from physical insights gained through these simulation examples, also numerical aspects are thoroughly studied on basis of weak and strong parallel scaling tests. As potential applications, the proposed thermal PBF simulation approach can serve as a basis for microstructure and thermo-mechanical predictions on the part-scale, but also to assess the influence of scan pattern and part geometry on melt pool shape and temperature, which are important indicators for well-known process instabilities.

Robust Superhydrophobicity through Surface Defects from Laser Powder Bed Fusion Additive Manufacturing.

The robustness of superhydrophobic objects conflicts with both the inevitable introduction of fragile micro/nanoscale surfaces and three-dimensional (3D) complex structures. The popular metal 3D printing technology can manufacture robust metal 3D complex components, but the hydrophily and mass surface defects restrict its diverse application. Herein, we proposed a strategy that takes the inherent ridges and grooves' surface defects from laser powder bed fusion additive manufacturing (LPBF-AM), a metal 3D printing process, as storage spaces for hydrophobic silica (HS) nanoparticles to obtain superhydrophobic capacity and superior robustness. The HS nanoparticles stored in the grooves among the laser-melted tracks serve as the hydrophobic guests, while the ridges' metal network provides the mechanical strength, leading to robust superhydrophobic objects with desired 3D structures. Moreover, HS nanoparticles coated on the LPBF-AM-printed surface can inhibit corrosion behavior caused by surface defects. It was found that LPBF-AM-printed objects with HS nanoparticles retained superior hydrophobicity after 150 abrasion cycles (~12.5 KPa) or 50 cycles (~37.5 KPa). Furthermore, LPBF-AM-printed ships with superhydrophobic coating maintained great water repellency even after 10,000 cycles of seawater swashing, preventing dynamic corrosion upon surfaces. Our proposed strategy, therefore, provides a low-cost, highly efficient, and robust superhydrophobic coating, which is applicable to metal 3D architectures toward corrosion-resistant requirements.

Microstructural feature-driven machine learning for predicting mechanical tensile strength of laser powder bed fusion (L-PBF) additively manufactured Ti6Al4V alloy

The rapid solidification inherent in laser powder bed fusion (L-PBF) additive manufacturing (AM) introduces segregation phenomena and formation of non-equilibrium phases in duplex titanium alloy components, thereby impeding their suitability for high-reliability engineering applications. Consequently, heat treatment becomes indispensable for optimizing both the microstructure and mechanical properties to meet application requirements. This study aims to investigate the influence of varied annealing temperatures on the evolution of L-PBF-built Ti6Al4V alloy microstructure, subsequently elucidating their impact on tensile properties by analyzing of L-PBF process parameters, building orientations, and annealing temperatures. The findings reveal that annealing at 850 °C for 2 h facilitates the transformation of brittle martensite into a ductile lamellar (α + β) microstructure, thereby conferring excellent tensile properties upon the L-PBF-built Ti6Al4V alloy. Furthermore, to accurately predict the tensile strengths of the Ti6Al4V, we take into account the L-PBF process parameters and the as-built microstructures in a comprehensive manner, extracting the pertinent microstructural features. A machine learning (ML)-based model is built to facilitate accurate predictions. Accurate and reliable predictions are demonstrated by this model when applied to Ti6Al4V. This data-driven approach establishes a novel avenue for AM material property prediction and process parameter optimization.