Laser Powder Bed Fusion Process Research Articles

Investigation on the characteristics of porosity, melt pool in 316L stainless steel manufactured by laser powder bed fusion

Pores are among the primary defects in 316L stainless steel when fabricated through laser powder bed fusion (L-PBF). To better understand and precisely control the formation behavior of pores in the L-PBF-manufactured 316L stainless steel, effects of L-PBF process parameters on the melt pool and pore characteristics have been investigated. A method utilized for extreme values statistics analysis was also applied to investigate and predict the sizes of pores. Relationships among the count of measurements, the laser energy density, the reduced variate, and the maximum pore size were revealed. The findings showed an overall increase in melt pool depth as the laser energy densities was raised, yet the magnitude of this growth weakened as the laser energy density continued to increase. Conversely, it was discovered that the connection between the width of the melt pool and the density of laser energy exhibited a markedly less distinct correlation. As laser energy density escalated, there was a shift in the melt pool mode from conduction-based to a transitional phase, and upon further elevation of the laser energy density, it transitioned to keyhole mode. To obtain a reliable statistical analysis of pores, at least 40 measurements should be acquired for the estimation of the pore sizes by taking comprehensive consideration of time cost. Beside, in all three melt pool modes, at a fixed observation area, the maximum size of pores generally decreased with the escalation in laser energy densities.

Process optimization, interfacial characteristics, and mechanical properties of titanium-tantalum multi-material structures fabricated via laser powder bed fusion

To fully exploit the biomechanical properties of titanium (Ti) and biocompatibility of tantalum (Ta), Ti-Ta multi-material structures with different printing sequences were fabricated using laser powder bed fusion (LPBF). The interfacial characteristics, mechanical properties, formation mechanisms, and influence of process parameters on bonding strength at the Ti/Ta (Ta printed on Ti) and Ta/Ti (Ti printed on Ta) interfaces were investigated. At the Ti/Ta interface, a wide diffusion zone led to microstructure evolution along the building direction: coarsened lath α′ grain → refined α′ + β grains → columnar β + cellular β grains. This evolution was attributed to keyhole formation and differences in material density that promoted element diffusion. On the other hand, at the Ta/Ti interface with a narrow diffusion zone, direct microstructure evolution occurred from columnar β + cellular β grains to lath α′ grains due to limited solid-state diffusion and compromised element diffusion caused by gravity and heat dissipation. Optimal bonding strengths of 548.11 MPa and 285.42 MPa were achieved for the Ti/Ta and Ta/Ti interfaces, respectively. During LPBF processing of Ti-Ta multi-materials, reducing scanning speed significantly enhanced bonding strength, indicating its significant impact on interfacial bonding. These findings offer novel insights and references to fabricate Ti-Ta multi-material structures with great potential for biomedical implants through LPBF.

Laser powder bed fusion of 2507 duplex stainless steel: Microstructure, mechanical properties, and corrosion performance

Duplex stainless steels offer a unique combination of advantages derived from both ferritic and austenitic structures, providing excellent mechanical properties and corrosion resistance. Nevertheless, conventional metallurgical techniques such as casting and rolling encounter challenges when it comes to the rapid fabrication of intricate components. In this study, we explore the microstructure, mechanical properties, and corrosion performance of 2507 duplex stainless steel manufactured through laser powder bed fusion (L-PBF). Through meticulous refinement of L-PBF parameters, we successfully achieved a dense sample featuring a hardness exceeding 400 HV, a relative density surpassing 98 %, and a surface roughness below 15 μm. This was obtained using a scanning speed of 700 mm/s and a laser power of 210 W. Due to the high cooling rate of L-PBF process, L-PBFed 2507 exhibits a ferritic microstructure distinct from that of rolled 2507 duplex stainless steel. Additionally, a considerable presence of internal dislocations can be observed in the L-PBFed 2507 material. The 2507 stainless steel exhibited an outstanding yield strength of 1198 MPa and an ultimate tensile strength of 1269 MPa, surpassing the performance of rolled 2507 duplex stainless steel. The corrosion current density was nearly negligible (approximately 10−6 A cm−2), with a passivation window expanding 1.2 V relative to the corrosion potential and a passivation platform below 10−4 A cm−2. These corrosion characteristics were comparable to those observed in rolled 2507 duplex stainless steel. This research demonstrates the suitability of L-PBFed 2507 stainless steel in environments requiring high strength and corrosion resistance, thereby opening up avenues for high-performance components in advanced engineering applications.

A Comprehensive Review of Laser Powder Bed Fusion in Jewelry: Technologies, Materials, and Post-Processing with Future Perspective

In recent years, additive manufacturing (AM) has played a significant role in various fashion industries, especially the textile and jewelry manufacturing sectors. This review article delves deeply into the wide range of methods and materials used to make intricately designed jewelry fabrication using the additive manufacturing (AM) process. The Laser Powder Bed Fusion (L-PBF) process is examined for its suitability in achieving complex design and structural integrity in jewelry fabrication even with respect to powder metallurgy methods. Moreover, the review explores the use of precious materials, such as gold, silver, copper, platinum, and their alloys in additive manufacturing. Processing precious materials is challenging due to their high reflectivity and thermal conductivity, which results in poor densification and mechanical properties. To address this issue, the review article proposes three different strategies: (i) adding alloying elements, (ii) coating powder particles, and (iii) using low-wavelength lasers (green or blue). Finally, this review examines crucial post-processing techniques to improve surface quality, robustness, and attractiveness. To conclude, this review emphasizes the potential of combining additive manufacturing (AM) with traditional craftsmanship for creating jewelry, exploring the potential future directions and developments in the field of additive manufacturing (AM) for jewelry fabrication.

Inhomogeneous deformation in melt-pool structure of Al-Fe-Cu alloy manufactured by laser powder bed fusion

Abstract The laser powder bed fusion (L-PBF) processed Al–2.5Fe–2Cu (mass%) alloy exhibited a high tensile strength above 340 MPa and pronounced directional dependence. The sample exhibited a characteristic inhomogeneous microstructure (in melt-pool structure) arising from the local melting and rapid solidification in the L-PBF process. The coarsened cellular structure localized along the melt pool boundary resulted in the local soft regions affected by the melt pool structure. The local vulnerability contributed to the direction dependence of the tensile ductility of the specimen.

Laser polishing of cobalt chrome alloy fabricated by laser powder bed fusion process: design of experiment-based approach for reducing surface roughness

Laser polishing of cobalt chrome alloy fabricated by laser powder bed fusion process: design of experiment-based approach for reducing surface roughness

Mesoscale multilayer multitrack modeling of melt pool physics in laser powder bed fusion of lattice metal features

This paper reports on the development of a mesoscale multiphysics numerical model for predicting the dimensions of melt pool zones in Laser Powder Bed Fusion process, in a multilayer and multitrack application dedicated to the manufacturing of lattice metal features. In such context, a clear need emerges to study laser-matter interaction regarding the persisting questions surrounding the comprehension of melt pool. An experimental campaign involving thin pillars made of the nickel-based superalloy IN718 is presented, highlighting the complexity introduced by the thin tracks superposition. Then, a continuous mesoscale numerical model, considering heat transfer, melt pool flow, and vaporization phenomena, and its extension to multilayer-multitrack simulation is detailed. Some discussions about the numerical approach and its ability to predict the global morphology and dimensions of melt pool zones and resulting tracks after solidification are proposed. Finally, comparisons between the experiments and the numerical model show good agreement, with a maximum relative error of 8 % observed for remelted zone depth. This study demonstrates the capability of the present approach to help in understanding the influence of process parameters on melt pool shape and, thereafter, to determine process parameters to optimize for lattice features building.

Effect of the building orientation on additively manufactured copper alloy: Hydraulic performance of different surface roughness channels

The additive manufacturing capabilities open new frontiers in the design of complex geometries in many different application fields, especially thermal science, in which multi-functional, efficient, compact components with internal cooling or heating channels are becoming more and more requested. Among the possible additive manufacturing technologies, the laser powder bed fusion process has recently been proven to manufacture high-conductivity metals, with good mechanical and thermal properties (i.e. pure copper and copper alloys), attracting the attention of the heat transfer community. However, depending on the material, design, and process parameters, the surface roughness of the components can remarkably change and become a critical issue in cooling applications. Currently, the surface texture characteristics and the final channel size cannot be accurately estimated a priori, and thus the initial hypothesis of the current design methods may lead to unpredictable and undesired results. This work experimentally and numerically explores the effects of the building orientation on the fluid dynamic behaviour in CuCrZr alloy channels, which is considered one of the most promising additive manufacturing materials for thermal applications. By coupling the experimental hydraulic results with surface characterization and X-ray computer tomography dimensional measurements of the channels, a novel simplified methodology to properly correlate the pressure drop to the surface texture of a single wall is proposed and numerically validated.

Multi-physics simulation for predicting surface roughness of laser powder bed fused parts after laser polishing

Laser powder bed fusion (L-PBF) uses a controlled laser beam to melt specific regions of a metal powder bed in a layer-by-layer fashion to fabricate parts with an intricate geometry. However, due to the stochastic nature of the L-PBF process, many defects may occur during the build process, including distortion, porosity, and high surface roughness. A poor roughness of the upper surface is frequently associated with impaired mechanical properties and a lower corrosion resistance. Thus, laser polishing (LP) is commonly employed to smooth the surface of the component following the build process. The surface finish of the polished part is dependent not only on the initial morphology of the surface, but also the processing conditions employed in the polishing process (i.e., the laser power, scanning speed, and hatching space). The surface profile is also influenced by physical phenomena such as the surface tension force, recoil pressure, and Marangoni force. The present study thus proposes an integrated framework based on discrete element method (DEM) and computational fluid dynamics (CFD) simulations which takes account of all of these factors to predict the final surface morphology and roughness of L-PBF components following LP processing. The validity of the simulation model is confirmed by comparing the calculated mean surface roughness of the polished components (Sa)with the experimental values. It is found that the maximum error of the simulation results for different initial surface morphologies and LP processing conditions is less than 6.8 %.

Unveiling the layer-wise dynamics of defect evolution in laser powder bed fusion: Insights for in-situ monitoring and control

Lack of fusion (LOF) defects can significantly affect the mechanical properties of components manufactured by laser powder bed fusion (LPBF). The layer-wise evolution of LOF defects is complex and not yet thoroughly understood. This work explores the spatiotemporal variations of LOF defects under various conditions using in-situ monitoring, ex-situ surface topography and μ-CT porosity characterization, and high-fidelity multi-physics numerical simulation. The results show that LOF defects could exhibit typical self-healing characteristics for over five printing layers. The specific self-healing behaviors depend on the initial sizes of the defects and the LPBF process conditions. The evolution of LOF defects can be detected by in-situ monitoring using light intensity. However, the in-situ monitoring may miss detecting LOF defects buried below the healed layers, which were alternatively observed via μ-CT. For the defective area with a depth of 150 μm, the relative density increased from 61.7 % to 95.7 % for the first to the fifth printing layer. The optimization of process parameters demonstrated that the application of a 45° scanning angle could significantly enhance surface flatness and repair internal pores to a minimum of 36.0 μm. The findings highlight the ability of in-situ monitoring in detecting LOF defects and the potential of the controlled printing process to accelerate defect repair. These outcomes offer valuable insights for the industrial applications of in-situ monitoring and control during LPBF.

Unraveling the interrelation between process parameters and defects to enhance the strength of laser additively manufactured CuZrAlTi bulk metallic glass

Laser powder bed fusion (LPBF) enables the production of large and intricately shaped bulk metallic glasses (BMGs); however, the current strength of LPBF-fabricated BMGs is significantly lower than their cast counterparts due to defects generated during the LPBF process in the as-built BMGs. In this study, considering the viscosity characteristics of BMG melts and their impact on melt pool characteristics, we investigated the relationship between LPBF parameters, structural characteristics, and defects in a selected Cu45Zr45Al6Ti4 BMG. Our findings revealed a transition of the melt pool from conduction mode to keyhole mode and subsequently to extended mode with increasing laser power. Furthermore, we explored how melt pool characteristics influence defects such as pores, heat affected zone (HAZ), and crystalline phase in the as-built Cu45Zr45Al6Ti4 BMG. Under optimized LPBF parameters, we successfully fabricated nearly fully amorphous Cu45Zr45Al6Ti4 BMG samples with high density (>99%) and absence of cracks. The printed samples exhibited a compressive strength of 1839 MPa comparable to their cast counterparts while achieving an impressive tensile strength value of 920 MPa, currently the highest reported for CuZr-based BMGs. This study enhances our understanding of the intrinsic relationship between LPBF parameters, melt pool characteristics, defects, and mechanical properties of LPBF-fabricated BMGs while providing a new parameter optimization strategy for reducing defects and achieving high strength in BMGs.

An integrated fuzzy logic and machine learning platform for porosity detection using optical tomography imaging during laser powder bed fusion

Traditional methods such as mechanical testing and x-ray computed tomography (CT), for quality assessment in laser powder-bed fusion (LPBF), a class of additive manufacturing (AM), are resource-intensive and conducted post-production. Recent advancements in in-situ monitoring, particularly using optical tomography (OT) to detect near-infrared light emissions during the process, offer an opportunity for in-situ defect detection. However, interpreting OT datasets remains challenging due to inherent process characteristics and disturbances that may obscure defect identification. This paper introduces a novel machine learning-based approach that integrates a self-organizing map, a fuzzy logic scheme, and a tailored U-Net architecture to enhance defect prediction capabilities during the LPBF process. This model not only predicts common flaws such as lack of fusion and keyhole defects through analysis of in-situ OT data, but also allows quality assurance professionals to apply their expert knowledge through customizable fuzzy rules. This capability facilitates a more nuanced and interpretable model, enhancing the likelihood of accurate defect detection. The efficacy of this system has been validated through experimental analyses across various process parameters, with results validated by subsequent CT scans, exhibiting strong performance with average model scores ranging from 0.375 to 0.819 for lack of fusion defects and from 0.391 to 0.616 for intentional keyhole defects. These findings underscore the model’s reliability and adaptability in predicting defects, highlighting its potential as a transformative tool for in-process quality assurance in AM. A notable benefit of this method is its adaptability, allowing the end-user to adjust the probability threshold for defect detection based on desired quality requirements and custom fuzzy rules.

Tailoring Laser Powder Bed Fusion Process Parameters for Standard and Off-Size Ti6Al4V Metal Powders: A Machine Learning Approach Enhanced by Photodiode-Based Melt Pool Monitoring

An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using machine learning and melt pool monitoring data collected with photodiode sensors, the goal of this research was to efficiently customize LPBF process parameters. A novel aspect of this study is the application of standard and off-size powder feedstocks. Ti6Al4V (Ti64) powder was used in three size ranges of 15–53 µm, 15–106 µm, and 45–106 µm to print the samples. This facilitated the development of a process parameters tailoring system capable of handling variations in powder size ranges. Ultimately, per each part, the associated set of light intensity statistical signatures along with the powder size range and the parts’ density, surface roughness, and hardness were used as inputs for three regressors of Feed-Forward Neural Network (FFN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The laser power, laser velocity, hatch distance, and energy density of the parts were predicted by the regressors. According to the results obtained on unseen samples, RF demonstrated the best performance in the prediction of process parameters.

Effect of powder composition on WE43 magnesium alloy fabricated by laser powder bed fusion

Laser powder bed fusion (L-PBF) of WE43 magnesium alloy has a promising application prospect in the fields of lightweight parts and biodegradable implants. Grain refinement in these components is expected for high strength and controlled biodegradation, however, grain structure of WE43 is sensitive to composition and processing. L-PBF WE43 exhibited significant diversity on the microstructure and properties with the fluctuation of powder composition, and the influencing mechanism remained unclear. In this study, L-PBF samples were fabricated with high formation quality using four different WE43 powders, in which the content of Nd and Zr were purposely adjusted. Their microstructure, tensile properties and corrosion resistance were characterized. L-PBF specimens composed of fine equiaxed grains were obtained from Mg-3.56Y-2.45Nd-1.14Gd-0.40Zr powder, achieving ultimate strength of 316 MPa and elongation of 15%. With the minor decrease of Nd and Zr content, which was still in the range of present standards of WE43, L-PBF specimens respectively exhibited abnormal grain growth and coarse columnar grains, leading to tremendous deterioration of mechanical properties and corrosion resistance. During the multiple layers of fusion and solidification, Zr promoted the columnar-to-equiaxed transition and generated finer grains, while Nd exerted pinning effect in the heat affected zone to keep the fine grains from abnormal grain growth. The results firstly demonstrate the significant influence of the minor change of alloying elements on the performance, and indicate that a stricter compositional standard of WE43 powder is necessary to achieve reliable properties for the L-PBF process.

Probabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation

Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach toward inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to the literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar–Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack-of-fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.

Finite element modeling and simulation of IN738 laser powder bed fusion process assisted via machine learning

Finite element modeling and simulation are considered one of the effective methods for optimizing additive manufacturing process windows, which can better capture metallurgical features under the interaction of multiple physical fields. However, correctly selecting or obtaining heat source parameters, boundary conditions, and thermophysical parameters is the biggest challenge of finite element modeling. In this work, the laser semi-ellipsoid heat source parameters and powder thermophysical parameters of the laser powder bed fusion process was effectively determined by machine learning method, and an accurate laser powder bed fusion process model of IN738 alloy was established. The simulation error of the size of single-track molten pool under different process conditions is less than 2.0 %. The simulation results of stress field by multi-layer and multi-track under different process conditions are consistent with the micro crack and density test results of deposited samples. The relevant research results provide a new idea for correctly establishing the model of additive manufacturing process and realizing reliable finite element simulation.

Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

Hot deformation behavior of laser powder bed fusion newly developed MS400 grade maraging steel

AbstractIn this work, the hot deformation mechanism of as-printed laser powder bed fusion process (LPBF) newly developed MS400 grade maraging steel was investigated. The optimization processes allowed for obtaining samples with an average density of 8.200 ± 0.002 g cm−3 and hardness of 417 ± 5 HV. The hot compression procedure of maraging steel was carried out with the DIL 805 A/D dilatometer at different temperatures in the range of 1050 °C-1200 °C and strain rates of 0.01 s−1–1 s−1 in an inert gas atmosphere. The measured melt flow stress data were used to develop a constitutive model to determine the behavior of the alloy during hot deformation. The proposed equation can be used as an input to the finite element analysis to obtain the flow stress at a given strain, strain rate and temperature, useful for predicting flow localization or fracture during thermomechanical simulation. The activation energy for hot deformation was calculated to be 388.174 kJ mol−1, which corresponds to that of M350 grade. The proposed equation can be used during finite element analysis to calculate the flow stress at any strain, strain rate and temperature to determine the location of a flow or crack during a thermomechanical simulation.

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. In this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

In-situ monitoring of the small changes in process parameters with multi-sensor fusion during LPBF

Abstract The small changes in process parameters have significant influences on the stability of laser powder bed fusion (LPBF). Therefore, monitoring the small changes in process parameters is particularly important. This paper proposed a machine learning (ML)-based multi-sensor fusion approach to monitor the LPBF processing state by combining photodiode, acoustic, and visual signals. In order to extract the motion features of the melt pool more accurately and describe its transient changes, an ellipse adjustment algorithm is proposed to segment the melt pool images, eliminating the interference of spatters. The motion features combined with preprocessed acoustic signals and photodiode signals to identify melting states during small changes in process parameters. The proposed ML-based multi-sensor fusion approach achieves impressive prediction accuracies of 99.9% for identifying the fluctuations in the process parameters. The results demonstrate that the proposed method can accurately identify small changes in process parameters, which is of great significance for improving the process stability and providing reliable guidance in subsequent work.