Search Results (175)

This study presents a comparative analysis of three metal additive manufacturing processes: selective laser melting (SLM), also known as powder bed fusion (PBF); binder jetting (BJ); and atomic diffusion additive manufacturing (ADAM), a form of Material Extrusion (MEX). It focuses on the geometric and dimensional accuracy of ADAM-fabricated 17-4 PH stainless steel components, while AISI 316L stainless steel is the benchmark material for BJ and SLM technologies. In addition to dimension and geometry inspections, this study also measures the distribution of residual stresses and microstructural features of the printed components. Residual stresses were determined quantitatively to identify the internal state of stress developed because of each processing technology. The results reveal significant differences in dimensional accuracy, residual stress profiles, surface roughness, and microstructural characteristics among the three additive manufacturing technologies. The observed trends and correlations provide valuable guidance for selecting the most appropriate additive manufacturing technique based on required accuracy, mechanical properties, and product complexity. Full article

As power electronic modules are increasingly required to provide improved heat dissipation, aluminum nitride (AlN) stands out against other ceramic materials. At the same time, more cost-efficient production of customized products demands shorter development cycles and innovative manufacturing processes. Conventional process chains in power electronics are usually long and inflexible; thus, innovative ways to reduce process steps and faster prototyping are needed. Therefore, this study investigates the usage of additive manufacturing technology—laser-based powder bed fusion of metal powder (PBF-LB/M)—namely copper (Cu), on AlN substrates for power electronic applications. It is found that specific electrical conductivity values can be achieved up to 31 MS/m, and adhesion measured by shear testing reaches 15 MPa. In reliability testing, the newly produced samples exhibit a 25% decrease in adhesion after 250 cycles, which is comparatively moderate. This study shows the feasibility of PBF-LB/M of Cu powder on AlN, emphasizing its strengths and highlighting remaining weaknesses. Full article

To ensure the sustainability of alloy-based strategies, both compositional design and processing routes must be simplified. Metal additive manufacturing (AM), with its exceptionally rapid, non-equilibrium solidification, offers a unique platform to produce tailored microstructures in simple alloys that deliver superior mechanical properties. In this study, we employ laser powder bed fusion (LPBF) to fabricate 1080 plain carbon steel, a binary alloy comprising only iron and carbon. Deviating from conventional process optimization focusing primarily on density, we optimize LPBF parameters for mechanical performance. We systematically varied key parameters (laser power and scan speed) to produce batches of tensile specimens, which were then evaluated on a high-throughput mechanical testing platform (HTP). Using response surface methodology (RSM), we developed predictive models correlating these parameters with yield strength (YS) and elongation. The RSM models identified optimal and suboptimal parameter sets. Specimens printed under the predicted optimal conditions achieved YS of 1543.5 MPa and elongation of 7.58%, closely matching RSM predictions (1595.3 MPa and 8.32%) with deviations of −3.25% and −8.89% for YS and elongation, respectively, thus validating model accuracy. Comprehensive microstructural characterization, including metallographic analysis and fracture surface examination, revealed the microstructural origins of performance differences and the underlying strengthening mechanisms. This methodology enables rapid evaluation and optimization of LPBF parameters for 1080 carbon steel and can be generalized as an efficient framework for robust LPBF process development. Full article

To capture the complex metallic spatter and melt pool behavior during the rapid interaction between the laser and metal material, high-speed cameras are applied to record the laser powder bed fusion process and generate a large volume of image data. In this study, four deep learning algorithms are applied: YOLOv5, Fast R-CNN, RetinaNet, and EfficientDet. They are trained by the recorded videos to learn and extract information on spatter and melt pool behavior during the laser powder bed fusion process. The well-trained models achieved high accuracy and low loss, demonstrating strong capability in accurately detecting and tracking spatter and melt pool dynamics. A stability index is proposed and calculated based on the melt pool length change rate. Greater index value reflects a more stable melt pool. We found that more spatters were detected for the unstable melt pool, while fewer spatters were found for the stable melt pool. The spatter’s size can affect its initial ejection speed, and large spatters are ejected slowly while small spatters are ejected rapidly. In addition, more than 58% of detected spatters have their initial ejection angle in the range of 60–120$°$. These findings provide a better understanding of spatter and melt pool dynamics and behavior, uncover the influence of melt pool stability on spatter formation, and demonstrate the correlation between the spatter size and its initial ejection speed. This work will contribute to the extraction of important information from high-speed recorded videos for additive manufacturing to reduce waste, lower cost, enhance part quality, and increase process reliability. Full article

The lack of new materials with desired processability and functional characteristics remains a challenge for metal additive manufacturing (AM). Therefore, in this work, a new promising AISI 316L-based alloy with better performance compared to the commercially available one is developed via the laser powder bed fusion (L-PBF) process. Moreover, establishing process–structure–properties linkages is a critical point that should be evaluated carefully before adding newly developed alloys into the AM market. Hence, the current study investigates the influences of various process parameters on the as-built quality and microstructure of the newly developed alloy. The results revealed that increasing laser energy density led to reduced porosity and surface roughness, likely due to enhanced melting and solidification. Microstructural analysis revealed a uniform distribution of copper within the austenite phase without forming any agglomeration or secondary phases. Electron backscatter diffraction analysis indicated a strong texture along the build direction with a gradual increase in Goss texture at higher energy densities. Grain boundary regions exhibited higher local misorientation and dislocation density. These findings suggest that changing the process parameters of the L-PBF process is a promising method for developing tailored microstructures and chemical compositions of commercially available AISI 316L stainless steel. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

Invar36 alloy, renowned for its exceptionally low coefficient of thermal expansion and excellent mechanical properties, is widely used in precision instruments, high-accuracy molds, and related fields. Metastructures fabricated via laser powder bed fusion (LPBF) have significantly broadened the application scope of Invar36 alloy, owing to their unique advantages such as lightweight design, high specific strength, and high specific stiffness. However, the structure–property coupling relationship in Invar-based metallic lattice structures remains insufficiently understood, which poses a major obstacle to their further engineering utilization. In this study, 36 lattice structures with varying design parameters were fabricated and experimentally evaluated. The design variables included lattice architecture (body-centered cubic (BCC), diamond (DIA), face-centered cubic (FCC), and octet (OCT)), strut diameter (0.6 mm, 0.8 mm, and 1.0 mm), and inclination angle (35°, 45°, and 55°). The influence of these structural parameters on the mechanical performance was systematically investigated. The results indicate that lattice architecture has a significant impact on mechanical properties, with the OCT structure, characterized by stretch-dominated behavior, exhibiting the best overall performance. Under the conditions of a 35° inclination angle and a strut diameter of 1.0 mm, the elastic modulus, compressive strength, plateau stress, and energy absorption of the OCT structure reaches 2525.92 MPa, 110.65 MPa, 162.26 MPa, and 78.22 mJ/mm³, respectively. Furthermore, increasing the strut diameter substantially improves mechanical performance, while variations in inclination angle primarily influence the dominant deformation mode. These findings demonstrate that the mechanical properties of Invar36 alloy lattice structures fabricated via LPBF can be effectively tuned over a broad range, offering both theoretical insights and practical guidance for customized performance optimization. Full article

Additive manufacturing techniques, such as laser-based powder bed fusion of metals (PBF-LB/M), have now gained high industrial and academic interest. Despite its design flexibility and the ability to fabricate intricate components, LPBF has not yet reached its full potential, partly due to the challenges associated with microstructure control. The precise manipulation of the microstructure in LPBF is a formidable yet highly rewarding endeavor, offering the capability to engineer components at a local level. This work introduces an innovative parallelized Cellular Automaton (CA) framework for modeling the evolution of the microstructure during the LPBF process. LPBF involves remelting and subsequent nucleation followed by crystal growth during solidification, which complicates and burdens microstructure simulations. In this research, a novel approach to nucleation seeding and crystal growth is implemented, focusing exclusively on the final stages of melting and solidification, enhancing the computational efficiency by 30%. This approach streamlines the simulation process, making it more efficient and effective. The developed model was employed to simulate the microstructure of an austenitic advanced high-strength steel (AHSS). The model was validated by comparing the simulation results qualitatively and quantitatively with the experimental data obtained under the same process parameters. The predicted microstructure closely aligned with the experimental findings. Simulations were also conducted at varying resolutions of CA cells, enabling a comprehensive study of their impact on microstructure evolution. Furthermore, the computational efficiency was critically evaluated. Full article

Laser-based powder bed fusion of metals (PBF-LB/M) is one of the most promising additive manufacturing technologies to fabricate complex-structured metal parts. However, its corresponding applications have been limited by technical bottlenecks and increasingly strict industrial requirements. Process optimization, a scientific issue, urgently needs to be solved. In this paper, a three-phase transient model based on the level-set method is established to examine the heat transfer and melt pool behavior in PBF-LB/M. Surface tension, the Marangoni effect, and recoil pressure are implemented in the model, and evaporation-induced mass and thermal loss are fully considered in the computing element. The results show that the surface roughness and density of metal parts induced by heat transfer and melt pool behavior are closely related to process parameters such as laser power, layer thickness, scanning speed, etc. When the volumetric energy density is low, the insufficient fusion of metal particles leads to pore defects. When the line energy density is high, the melt track is smooth with low porosity, resulting in the high density of the products. Additionally, the partial melting of powder particles at the beginning and end of the melting track usually contributes to pore formation. These findings provide valuable insights for improving the quality and reliability of metal additive manufacturing. Full article

In this work, we address the task of monitoring Powder Bed Fusion–Laser Beam processes for metal powders (PBF-LB/M). Two main contributions with practical merit are presented. First, we consider the comparison between a large deep neural network (VGG-19) and a small model consisting of, among others, four convolutional layers. Our study shows that the small model can compete favorably with the big model, which takes advantage of transfer learning techniques. Secondly, we present a filtering method using a semantic segmentation approach to preselect a region for the classification algorithm. The region is selected based on post-exposure images, and preselection can be easily adopted for any machine independently of the software used for the translation of process input files. To consider the task, a master dataset with over 260,000 samples was prepared, and a detailed process of preparing the training datasets was described. The study demonstrates that the classification time can be reduced by a factor of 4.51 while still maintaining the model’s necessary performance to detect errors in a PBF-LB process. Full article

The laser-based powder bed fusion of metal (PBF-LB/M) process of 316L stainless steel (SS) was systematically investigated under varying scanning spacings to assess its microstructural and mechanical properties. Optimized laser parameters were employed, and the resulting microstructure and mechanical performance were thoroughly characterized through surface and cross-sectional scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) analysis, fracture surface examination, and tensile testing. The results indicated that a scanning spacing of 0.11 mm produced the most favorable mechanical properties, characterized by a dense microstructure and refined grain morphology. These findings provide critical insights for the optimization of PBF-LB/M process parameters, contributing to the advancement of additive manufacturing techniques for 316L SS. Full article

Metal additive manufacturing is a disruptive technology that is changing how various alloys are processed. Although this technology has several advantages over conventional manufacturing, it is still necessary to standardize its properties, which are dependent on the relative density (RD). In addition, since experimental designs are costly, one solution is using machine learning algorithms that allow the effects of variations in the processing parameters on the resulting density of the additively manufactured components to be anticipated. This work assembled a database based on data from 673 observations and 10 predictors to forecast the relative density of 316L stainless steel and AlSi10Mg components produced by laser powder bed fusion (L-PBF). LazyPredict was employed to select the algorithm that best models the variability of the inherent data. Ensemble boosting regressors offer higher accuracy, providing hyperparameter fitting and optimization advantages. The predictions’ precision for aluminum and stainless steel obtained an R² value greater than 0.86 and 0.83, respectively. The results of the SHAP values indicated that laser power and energy density are the parameters that have the greatest impact on the predictability of the relative density of Al-Si10-Mg and SS 316L materials processed by L-PBF. This study presents a compendium of data for the additive fabrication of stainless steel and aluminum alloys, offering researchers a guide to understanding how processing parameters influence RD. Full article

Parts fabricated by laser powder bed fusion (LPBF) contain oxide inclusions, which can be detrimental to fatigue resistance. Under typical LPBF conditions, the atmosphere contains enough oxygen to oxidize reactive elements such as aluminum and titanium, forming oxides in the parts. In this work, mechanisms of oxide formation and oxide alteration were studied by laser-remelting the surfaces of bulk specimens of IN718 and AlSi10Mg, without the addition of metal powder. Calculations based on the mass transfer of oxygen to the melt pool surface indicated that direct oxidation of the melt pool did not play a major role. Rather, both the oxidation of hot spatter and reworking of the pre-existing oxide affected the concentration and morphology of oxides on the metal surface. Full article

This study presents a comprehensive numerical investigation of the Laser Powder Bed Fusion (LPBF) process for pure molybdenum, focusing on high-precision modeling and process optimization. The powder spreading behavior is simulated using the Discrete Element Method (DEM), while molten pool dynamics are analyzed through Computational Fluid Dynamics (CFD). Optimization of process parameters is performed using FLOW-3D Release 7 software in conjunction with the HEEDS-SHERPA algorithm. A total of 247 simulations are conducted to assess the effects of four critical parameters: laser power (50–400 W), scanning speed (80–300 mm/s), laser spot diameter (40–100 µm), and powder layer thickness (50–100 µm). The optimal parameter set—350 W laser power, 120 mm/s scanning speed, 50 µm spot diameter, and 50 µm layer thickness—results in an 80% laser absorption rate, a 60% reduction in micro-porosity, and over a 30% enhancement in both molten pool volume and surface area. Utilizing a fine 10 µm mesh resolution enables detailed insights into temperature gradients and phase transition behavior. The findings highlight that optimized parameter selection significantly improves the structural integrity of Mo-based components while minimizing manufacturing defects, thus offering valuable guidance for advancing industrial-scale additive manufacturing of refractory metals. Full article

CuCrZr/GH4169 multi-material structures combine the high thermal conductivity of copper alloys with the high strength of nickel-based superalloys, making them suitable for aerospace components that require efficient heat dissipation and high strength. However, additive manufacturing of such dissimilar metals faces challenges, with each laser powder bed fusion (LPBF) and laser directed energy deposition (LDED) process having its limitations. This study employed an LPBF-LDED integrated additive manufacturing (LLIAM) approach to fabricate CuCrZr/GH4169 components. CuCrZr segments were first produced by LPBF, followed by LDED deposition of GH4169 layers using optimized laser parameters. The microstructure, composition, and mechanical properties of the fabricated components were analyzed. Results show a sound metallurgical bond at the CuCrZr/GH4169 interface with minimal porosity and cracks (typical defects at the interface), achieved by exceeding a threshold laser energy density. Elemental interdiffusion forms a 100–200 μm transition zone, with a smooth hardness gradient (97 HV0.2 to 240 HV0.2). Optimized specimens exhibit tensile failure in the CuCrZr region (234 MPa), confirming robust interfacial bonding. These findings demonstrate LLIAM’s feasibility for CuCrZr/GH4169 and underscore the importance of balancing thermal conductivity and mechanical strength in multi-material components. These findings provide guidance for manufacturing aerospace components with both high thermal conductivity and high strength. Full article