Quality Of Powder Bed Research Articles

Investigation of Effects of Spreading Parameters on Powder Bed Quality in Additive Manufacturing

Powder bed-based additive manufacturing processes such as laser powder bed fusion, binder jetting, and electron beam melting are commonly utilized in various critical areas such as medical, aviation, and energy. Common to all these operations, the powders are first spread onto the built platform in a layer-by-layer fashion and selectively fused or bound with a suitable method. The quality of the process depends on several process parameters, including how the powders are spread onto the built platform. Powder spreading is an important step in these operations and can affect various process outputs. In this study, the powder spreading is numerically investigated using the discrete element method to determine the effects of the layer thickness, rotation, and transition velocities selected as parameters with a powder spreader roller. The relationship between powder spreading parameters and powder volume packing fraction was investigated using the 'Hertz-Mindlin and Johnson-Kendall-Robert’s (JKR) contact model considering the interactions between particle-particle and particle-built plate, has been added to the numerical model. A Design of Experiment combined with analysis of variance (ANOVA) was utilized to gain a broader understanding on the relationship between process parameters and green density and dynamic angle of repose.

Role of gravity magnitude on flowability and powder spreading in the powder bed fusion additive manufacturing process: Towards additive manufacturing in space

Understanding powder spreading under low gravity conditions is essential for optimizing final products using additive manufacturing in space. In this study, we investigated the role of gravity on flowability and spreading mechanisms through combined experimental and discrete element method (DEM) studies. Three powders with different theoretical densities were used to reenact low compressive conditions resembling those in a low-gravity environment. The influence of low compressive conditions on flowability and spreading behavior was examined using the Hall flowmeter, rotating drum, and spreading experiments. In the experimental result, the static flowability was primarily affected by the presence of elongated particles rather than the compressive conditions. The dynamic AoR of TD_4 powder increased compared to that of TD_8 powder, despite the presence of spherical particles with a smooth surface finish. A DEM simulation study was conducted using TD_8 powder to investigate the impact of different gravity levels on dynamic flowability. The DEM studies revealed that the dynamic flowability under rotation was decreased under low gravity owing to the promoted cohesive interactions. The powder spreading experiment was performed using the three powders with different theoretical densities. The in-situ observation with particle image velocimetry analysis revealed that kinetic energy dissipation in the spreading process was accelerated in the TD_8 powder pile, despite its high interparticle friction and cohesive force. The powder spreading simulation was conducted using TD_8 powder to clarify the effect of low gravity on the powder spreading process. In TD_8 powder under 1 G, particle supply was facilitated by a synergistic effect of free-falling and deposited particles. However, increased cohesive interactions under 0.5 G and 0.16 G restricted particle supply via free-falling, consequently reducing the powder bed density by about 2 % and 6.2 %, respectively. These findings prove that the cohesive force predominantly controls dynamic flowability and powder bed quality in the spreading process under low gravity conditions.

Efficient and lightweight layer-wise in-situ defect detection in laser powder bed fusion via knowledge distillation and structural re-parameterization

Powder bed quality is critical in Laser Powder Bed Fusion (LPBF) as defects in the powder bed will affect the quality of the forming parts. Recently, numerous powder bed defect detection methods based on semantic segmentation algorithms have been developed. However, due to the computational resource constraints and real-time requirements in industry scenarios, these heavy models face challenges in deploying into the LPBF process monitoring system. Meanwhile, existing lightweight models struggle to achieve sufficient performance on industrial data due to their limited feature extraction capabilities. To address the challenges in industrial deployment, this paper proposes a lightweight and efficient LPBF layer-wise defect detection paradigm to achieve a balance between performance and efficiency. Aiming at improving the performance of the lightweight model, this paper proposes a structured Knowledge Distillation framework, in which a pre-trained high-performance segmentation model (UNet++) is used to guide the training process of a lightweight model. To address the impact of model discrepancies on the effectiveness of Knowledge Distillation and further improve the inference speed, this paper constructs a specific lightweight model and applies Structural Re-parameterization to deeply compress the model. Moreover, we created a dataset comprising 406 images of powder-bed defects, with each image annotated at the pixel level. Massive experiments based on the dataset demonstrate the effectiveness of the proposed paradigm, with an excellent trade-off in performance and efficiency. The speed of the proposed model is approximately 5 times faster than that of high-performance models while maintaining comparable performance.

Qualitative and quantitative characterization of powder bed quality in laser powder-bed fusion additive manufacturing by multi-task learning

Qualitative and quantitative characterization of powder bed quality in laser powder-bed fusion additive manufacturing by multi-task learning

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.

Dynamic simulation of powder spreading processes toward the fabrication of metal-matrix diamond composites in selective laser melting

A novel method for the integrated production of structural and functional metal-matrix diamond composites is provided by selective laser melting (SLM) in the field of diamond tools. However, the uneven distribution of diamond particles and loose packing density in the powder bed can easily result in poor performance of diamond tools. In this work, a discrete element method model of diamond/CuSn composite powders is developed to simulate blade-spreading processes. It investigates how powder bed quality and particle dynamics are affected by diamond powder physical properties and powder spreading process parameters. The findings reveal that coarse diamond particles exhibit strong force chains at low velocities, whereas contact force chains are weak for fine CuSn particles at high velocities. It shows that diamond particles with irregular shapes have poor diffusivity and spreadability due to the large friction and mechanical locking forces, which causes diamond particles segregation. Therefore, the packing density and uniformity of the powder bed are both improved by reducing the volume fraction and particle size of diamond particles. In addition, the powder bed quality is improved by increasing the powder layer thickness and decreasing the translational speed of the blade, which weakens the shear expansion and jamming of diamond particles. The results have some significance for optimizing powder spreading processes toward the production of metal-matrix diamond composites in SLM.

Particle gradations optimization for powder spreading in additive manufacturing

Optimizing particle gradations to improve the powder bed quality is of practical engineering interest for powder spreading in additive manufacturing (AM). The discrete element model of ceramic powder is introduced to simulate blade-spreading and roller-spreading processes. Based on the packing theory, the effect of particle gradations on the powder bed quality and particle microscopic behavior is analyzed. The results show that fine particle gradations weaken the wall effect. The powder shear dilation in blade-spreading strengthens the loosening effect, however, the compaction effect of roller-spreading weakens the loosening effect. As coarse particle gradations increase, contact force chains become loose, however, strong force arches lead to particle jamming, uneven distribution, and voids in the powder bed. Particle segregation occurs because fine particle gradations pass through the gap between the substrate and spreader, and are deposited at the front part of the powder bed, while coarse particle gradations due to jamming are deposited at the end part of the powder bed. When the particle gradation coefficient is in the range of 0.1 to 0.5, the powder bed quality and particle segregation phenomenon are significantly improved compared to Gaussian distribution. The results can provide valuable references for the selection of particle gradations in AM.

Multiphysics modelling of powder bed fusion for polymers

ABSTRACT Polymeric materials for powder bed fusion additive manufacturing have been attracting extensive research interest due to their vast potential for fabricating end-use functional parts. Here, a high-fidelity multiphysics approach combining the discrete element model with the computational fluid dynamics model has been developed to simulate the printing process of polymers in powder bed fusion, involving powder recoating, melting, and coalescence. The developed approach considers particle flow dynamics, the reflection, absorption, and transmission of infrared laser radiation, and the viscous flow of polymer melt. The pore formation mechanisms due to lack of fusion and gas entrapment in polyamide 12 parts printed via selective laser sintering are studied. The simulation results reveal that lower polymer viscosity would be beneficial to the densification rate of the printed parts. Excessively small powder particles would degrade powder bed quality due to the agglomeration of polymer powder, thus leading to high porosity in the printed parts.

In-situ observation of powder spreading in powder bed fusion metal additive manufacturing process using particle image velocimetry

Providing a dense powder bed is necessary to ensure the rationality of the final product prepared by powder bed fusion-based additive manufacturing under broad building parameters. In this study, the powder spreading mechanism was elucidated using particle image velocimetry and discrete element simulations. The particle flow regimes in the spreading process were identified based on the particle displacement: alignment, rotation, and deposition. The alignment regime was dominant in gas-atomized stainless steel 304 powder piles, whereas the rotation regime was dominant in plasma rotating electrode processed stainless steel 304 powder piles. A high-fidelity spreading simulation model was developed to clarify the critical factors resulting in the different flow regimes of different stainless steel 304 powders. The dominant rotational regime of the plasma rotating electrode processed powder was substituted for alignment by increasing the cohesive force. The particle supply in the spreading process was further suppressed by increasing the cohesive force, owing to the formation of a strong force arch and agglomeration. The high cohesive force in the gas-atomized powder was mainly attributed to the electrostatic force caused by the thick oxide film. Therefore, it was proven that the oxide film thickness is a key factor in determining the powder spreading mechanism and powder bed quality in the powder bed fusion additive manufacturing process.

Identification of the influential DEM contact law parameters on powder bed quality and flow in additive manufacturing configurations

The main objective of the present manuscript is to implement DEM simulations of powder spreading in an Additive Manufacturing process. A numerical sensitivity analysis is carried out in order to identify the contact law parameters that impact most on powder bed quality. Tested parameters are surface energy, as well as sliding, rolling and restitution coefficients. It is seen that effective surface energy between powder particles is a key parameter to assess bed-forming ability, but that it is also necessary to consider rolling and sliding frictions for an accurate modeling of the spreading phenomena. In addition, our simulations allow a better understanding of the size segregation issues during the feeding and deposition stages. Finally, we describe the effect of the contact law parameters on the heap profile. We identify various powder flow zones within the heap, and show that heap profile can be a predictor of powder bed quality.

Preparation of granulated powders via freeze drying at different levels of slurry solid loading and comparison of their powder bed quality in roller-compaction-assisted binder jetting

Granulation can improve the flowability of nanopowder and the density of resultant parts printed by binder jetting. Forward-rotating roller compaction has the potential for further improving the density. In this study, for the first time, powder recoating tests of different granulated alumina powders were conducted on a commercially available binder jetting printer equipped with a forward-rotating roller compaction system. Freeze drying was used to prepare the granulated powder. Four different granulated powders were prepared from slurries with different levels of solid loading: 2, 5, 10, and 15 vol%, respectively. Powder bed quality, including defects on powder bed surface and maximum powder bed density without defects, was compared among the granulated powders. When the compaction ratio was too high, granulated powders with low solid loading levels of 2 and 5 vol% showed much fewer compaction-induced defects (including surface ridges and edge ridges) on powder bed surface than those with high solid loading levels of 10 and 15 vol%. As the solid loading level increased from 2 to 15 vol%, the maximum powder bed density without defects decreased from 19.5% to 16.0%. This study showed that a granulated powder prepared from a low level of slurry solid loading could reduce the compaction-induced defects on powder bed surface and enhance the powder bed density in roller-compaction-assisted binder jetting.

Optimising Spread-Layer Quality in Powder Additive Manufacturing: Assessing Packing Fraction and Segregation Tendency

Powder bed fusion (PBF), a subset of additive manufacturing methods, is well known for its promise in the production of fully functional artefacts with high densities. The quality of the powder bed, commonly referred to as powder spreading, is a crucial determinant of the final quality of the produced artefact in the PBF process. Therefore, it is critical that we examine the factors that impact the powder spreading, notably the powder bed quality. This study utilised a newly developed testing apparatus, designed specifically for examining the quality of powder beds. The objective was to analyse the influence of various factors, including the recoater shape, recoater gap size, and the different powder flow properties, on the powder bed relative packing fraction. Additionally, the study aimed to assess the variation in the particle size and shape across the build plate. The results indicated that all of the variables examined had an impact on the relative packing fraction, as well as the size and shape variations observed across the build plate.

Study on the Powder-Spreading Process of Walnut Shell/Co-PES Biomass Composite Powder in Additive Manufacturing.

Powder laying is a necessary procedure during powder bed additive manufacturing (PBAM), and the quality of powder bed has an important effect on the performance of products. Because the powder particle motion state during the powder laying process of biomass composites is difficult to observe, and the influence of the powder laying process parameters on the quality of the powder bed is still unclear, a simulation study of the biomass composite powder laying process during powder bed additive manufacturing was conducted using the discrete element method. A discrete element model of walnut shell/Co-PES composite powder was established using the multi-sphere unit method, and the powder-spreading process was numerically simulated using two different powder spreading methods (rollers/scrapers). The results showed that the quality of powder bed formed by roller laying was better than that formed by scrapers with the same powder laying speed and powder laying thickness. For both of the two different spreading methods, the uniformity and density of the powder bed decreased as spreading speed increased, although the spreading speed had a more important influence on scraper spreading compared to roller spreading. As powder laying thickness increased, the powder bed formed by the two different powder laying methods became more uniform and denser. When the powder laying thickness was less than 110μm, the particles were easily blocked at the powder laying gap and are pushed out of the forming platform, forming many voids, and decreasing the powder bed's quality. When the powder thickness was greater than 140 μm, the uniformity and density of the powder bed increased gradually, the number of voids decreased, and the quality of the powder bed improved.

Simulation Research on the Effect of Spreading Process Parameters on the Quality of Lunar Regolith Powder Bed in Additive Manufacturing

Lunar surface additive manufacturing with lunar regolith is a key step in in-situ resource utilization. The powder spreading process is the key process, which has a major impact on the quality of the powder bed and the precision of molded parts. In this study, the discrete element method (DEM) was adopted to simulate the powder spreading process with a roller. The three powder bed quality indicators, including the molding layer offset, voidage fraction, and surface roughness, were established. Besides, the influence of the three process parameters, which are roller’s translational speed, rotational speed, and powder spreading layer thickness on the powder bed quality indicators was also analyzed. The results show that with the reduction of the powder spreading layer thickness and the increase of the rotational speed, the offset increased significantly; when the translational speed increased, the offset first increased and then decreased, which resulted in an extreme value; with the increase of the layer thickness and the decrease of the translational speed, the values for voidage fraction and surface roughness significantly reduced. The powder bed quality indicators were adopted as the optimization objective, and the multi-objective parameter optimization was carried out. The predicted optimal powder spreading parameters and powder bed quality indicators were then obtained. Moreover, the optimal values were then verified. This study can provide informative guidance for in-situ manufacturing at the moon in future deep space exploration missions.

Ball Milling Treatment of Nb and B Nanoparticles Modified TiAl4822 Composite Powder and Its Effect on Powder Bed Quality and Powder Spreadability in Additive Manufacturing

Ball milling treatment is a low‐cost and achievable large‐scale production method for preparing the composite powder used for the laser powder bed fusion (LPBF) process. For the ball‐milled composite powder, good powder spreadability and composition homogeneity are essential for obtaining the LPBF‐fabricated part with good forming quality. In this article, the Nb and B nanoparticles modified TiAl4822 composite powder with a nominal composition of Ti‐47.53Al‐1.98Cr‐2.81Nb‐0.15B are prepared via ball milling. It is found that the physical properties of the composite powder are more sensitive to the milling time than the milling speed and ball‐to‐powder ratio. Long‐time milling is inclined to cause the formation of broken particles, poor powder flowability, and high oxygen contamination. Besides, the process maps between the ball milling energy E t and particle size distribution width, the static angle of repose, composition inhomogeneity, and oxygen content are established, respectively. Results show that when the applied E t is insufficient or excessive, the degree of sphericity or composition homogeneity always worsens. In these cases, deposition defects such as line or area defects are more prone to occur, due to the enhanced particle rearrangement resistance induced by the addition of nanoparticles.

Raking Process for Powder Bed Fusion of Ti–6Al–4V Alloy Powder Analyzed by Discrete Element Method

In order to avoid the formation of defects in additive manufacturing (AM) by powder bed fusion (PBF) process, it is crucial to understand the relationship between the quality of powder bed and a powder spreading process. In this study, the influences of conditions of powder raking process on the densities and homogeneity of powder bed have been examined by computer simulation using Discrete Element Method (DEM) and experiment of powder bed formation using a blade-type spreader for Ti–6Al–4V powder by way of example. The results were analyzed with a special focus on the effects of the relative size of powder particles with respect to the gap between the blade and the build platform. It has been clearly shown that the gap needs to be larger than the upper bound of the distribution of powder particles diameter to obtain a high-density powder bed. The DEM simulation indicated that the blade can sweep even powder particles that are not in direct contact with the blade when the powder particles are in a close-packed tetragonal configuration. This is the probable reason for the experimental fact that powder particles smaller than half of the gap are suitable for PBF.

In-situ monitoring of powder bed fusion of polymers using laser profilometry

Process monitoring allows for unique opportunities to capture information at intermediate layers during fabrication for part qualification, but also to improve process robustness by identifying issues before yield or quality is reduced. An industrial-grade laser profilometer has been integrated for the first time in a commercial polymer laser sintering machine and used to monitor powder bed quality and potentially qualify parts during production. Powder layer quality was measured in different conditions and changed drastically as a function of recoating speed, preheating temperature but also particle shape. The effective layer thickness was obtained for the first time in PBF of polymers, and gradually increased with the layer count until reaching a plateau at the nominal layer thickness. Powder layer density could be calculated as well and lies between 34 and 65 %, depending on the powder type and layer count. Finally, curling increased with decreasing preheating temperature, ranging from 70 µm to 350 µm i.e. until collision with the recoater. Based on the measured degree of curling, mitigation strategies could be integrated using a closed-loop feedback control. Effective process monitoring such as the one provided with laser profilometry may enable the development of new materials featuring more complex processing conditions, but also larger machines where thermal uniformity might be an issue.

Effect of Powder Shear Property on PBF-LB/M Powder Bed Quality of Tool Steels

The Additive Manufacturing (AM) die & mold, which has conformal cooling channel manufactured by PBF-LB/M process, will be deliver high performance in terms of improvement of thermal fatigue. In tool steel SKD61 for AM die & mold often occurs surface crack due to abnormal surface roughness that occur during the build process. In the process, powder bed (PB) quality influenced by powder properties is one of the key factors to determine the quality of built parts. However, in terms of quantitative relationship between PB quality and powder flow-ability, there are few reports. In this study, SKD61 powders with different flow-ability (powder shear properties) due to different particle shapes were prepared and the effect of shear properties on PB roughness was investigated using the evaluation machine specialized in powder recoat process. As a result, PB roughness has a good correlation with angle of internal friction, PB roughness and PB roughness stability corresponding to powder recoat speed change showed increase with increasing angle of internal friction. It was found that it is important for PB quality to control angle of internal friction influenced by particle shapes of mean particle diameter d50 or over.

A literature review on powder spreading in additive manufacturing

Powder-bed additive manufacturing, including powder bed fusion and binder jetting, has a wide range of applications in various industries. Powder spreading is a critical step of powder-bed additive manufacturing and has a determinative impact on powder bed quality and thus final part quality. This paper provides a literature review on powder spreading, focusing on the effects of influencing factors on powder bed quality. Three groups of influencing factors are discussed: spreaders, spreading parameters, and feedstock powder properties. Besides the effects of individual factors, the interaction effects between these factors are also discussed where applicable. Powder bed quality is discussed in terms of powder bed density and powder bed surface condition. Furthermore, knowledge gaps and research opportunities are presented as concluding remarks.

Powder-bed-fusion additive manufacturing of molybdenum: Process simulation, optimization, and property prediction

In this paper, the whole process of laser powder-bed-fusion (LPBF) additive manufacturing (AM) in the fabrication of molybdenum (Mo) material was numerically reproduced. Firstly, parameters of Mo powder with continuous size distribution utilized in actual AM production were calibrated and input into discrete element model (DEM) for parametric study on powder spreading. Then, the laser melting of the spread powder bed was simulated by computational fluid dynamics (CFD). On this basis, a back propagation neural network (BPNN) model was built for powder bed quality and molten track property prediction and evaluation. Results show that properly reducing the spreading velocity V or increasing the gap height H can contribute to the optimal structure of the powder bed. Corresponding mechanism analyses reveal that the residual velocity of particles and force chain block are the main reasons for the decrease of powder bed quality. And the molten track performance is not positively correlated with the packing density of the powder bed due to the defects such as balling and porosity caused by the over-thickness of powder bed. The selection rule of powder bed should satisfy as higher as possible the packing density within the bed thickness threshold. The BPNN model can accurately predict the powder bed quality and the molten track property and develop a reasonable map for the appropriate choice of operating parameters in real processes.