Search Results (1,074)

It has become a trend to precisely control the additive manufacturing process parameters within the high-density process window to obtain high-performance metal parts. However, there are few reports on this topic currently, leaving this research without sufficient references. This study took 316L austenitic stainless steel as a case study. In total, 36 groups of specimens were manufactured by Laser powder bed melting (LPBF), and then, two highly dense specimens were selected to study the variation in their microstructure and properties. The densities of the selected specimens, S1 (VED = 81 J/mm³) and S2 (VED = 156.3 J/mm³), are 99.68% and 99.99%, respectively. The results indicated that, compared with the S1 specimen, the S2 specimen significantly decreased in terms of yield strength (YS), ultimate tensile strength (UTS), and elongation (EL), which are 7.28%, 6.34%, and 19.15%, respectively. The differences in mechanical properties were primarily attributed to differences in their microstructures. Further, compared with the S1 specimen, the fitted ellipse aspect ratio and average grain size of the S2 specimen increased by 79.88% and 53.45%, respectively, and the kernel average misorientation (KAM) value and geometric necessary dislocation (GND) density increased by 36.00% and 58.43%, respectively. Furthermore, the S1 specimen exhibited a strong texture in the <101>//Z direction, whereas no obvious texture was observed in the S2 specimen. Obviously, the reason why precise regulation within the dense parameter range can achieve better performance is that the microstructure and mechanical properties of the specimens prepared within the dense range are different. More importantly, this study provides a feasible framework for optimizing alloys with broad and dense parameter ranges, demonstrating the potential to achieve high-performance components through precise parameter control. Furthermore, the results reveal that even within a wide range of high-density forming parameters, significant variations in microstructure and mechanical properties can arise depending on the selected parameter combinations. These findings underscore the critical importance of meticulous process parameter optimization and microstructural regulation in tailoring material properties. Full article

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

Stainless steel is essential in high-performance industries due to its strength, corrosion resistance, and biocompatibility. However, conventional manufacturing methods limit material efficiency, design complexity, and customization. Additive manufacturing (AM) has emerged as a powerful alternative, enabling the production of stainless-steel components with complex geometries, tailored microstructures, and integrated functionalities. Key AM methodologies, including laser powder bed fusion (L-PBF), binder jetting, and directed energy deposition (DED), are evaluated for their effectiveness in producing stainless-steel components with optimal performance characteristics. This review highlights innovations in stainless-steel AM, focusing on microfabrication, multi-material approaches, and post-processing strategies such as heat treatment, hot isostatic pressing (HIP), and surface finishing. It also examines the impact of process parameters on microstructure, mechanical anisotropy, and defects. Emerging trends include AM-specific alloy design, functionally graded structures, and AI-based control. Applications span biomedical implants, micro-tooling, energy systems, and automotive parts, with emphasis on microfabrication for biomedical micromachines and precision microforming. 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

Laser powder bed fusion (L-PBF) is a unique technology that enables manufacturing geometrically complex metal alloys, including Ti-6Al-4V parts. The microstructure of Ti-6Al-4V is determined by its localized thermal history, which is affected by not only the L-PBF process but also the geometry of the part. Understanding the microstructure at specific locations in complex geometries is of great importance in predicting the mechanical performance of Ti-6Al-4V parts. This work investigates the effects of geometric features on the local microstructure. Three geometries, namely, holes, overhangs, and penholders, were designed and used for this study. Three different laser powers, namely 150 W, 250 W, and 350 W, were set to print those geometries. The use of a lower laser power results in improved print quality. While the martensite phase dominates the bulk of the L-PBF Ti-6Al-4V parts, a fine α+β lamellar structure can form at down-skin regions of printed horizontal holes and overhangs. Moreover, the direction of the columnar prime β grain can shift due to directional heat dissipation. The local microstructural evolution after heat treatment is investigated as well. Full article

Fracture toughness anisotropy is a key concern in IN718 components produced by Laser Powder Bed Fusion (LPBF), due to their strong crystallographic texture and characteristic lamellar microstructure. In this study, the effect of grain orientation on fracture toughness was evaluated by testing two LPBF IN718 builds with the same laser scanning strategy (R0), but with two different orientations: vertical (R0-0) and 45° inclined (R0-45) relative to the build direction. The mechanical response was assessed through compact tension (CT) tests following ASTM E399 and ASTM E1820 standards. Results show that the R0-45 specimens exhibited a fracture toughness nearly 2.5 times higher than R0-0 specimens. Detailed microstructural analysis, supported by EBSD and SEM, reveals that the higher toughness in the R0-45 orientation is linked to a combination of smaller effective grain size along the crack path, higher levels of geometrically necessary dislocations (GND), and increased kernel average misorientation (KAM), which collectively enhance plastic accommodation and crack-tip shielding. These findings support and reinforce the established understanding of the relationship between microstructure and anisotropic fracture behavior in LPBF IN718, facilitating its practical application in the design and orientation of additively manufactured components. Full article

In binder jetting additive manufacturing (BJAM), parts are fabricated layer by layer by depositing a liquid binder on selected regions of the powder bed. Powder particles in the hopper of the printer are dispensed onto the powder bed to form a layer of powder. Powder dispensing rate affects material usage and print quality. Too high dispensing rates can cause excessive powder dispensing, increasing powder waste, while too low dispensing rates may result in incomplete layer formation, leading to reduced density of printed parts. The present study investigates how ultrasonic vibration intensity and initial powder amount in the hopper affect powder dispensing rate in BJAM when using a bimodal powder. A set of experiments with full factorial design were conducted using three levels of ultrasonic vibration intensity (50%, 75%, and 100%) and three levels of initial powder amount (600 g, 1000 g, and 1400 g) in the hopper. The results show that both ultrasonic vibration intensity and initial powder amount, as well as their interaction, significantly influence powder dispensing rate. Powder dispensing rate was higher when ultrasonic vibration intensity was higher or initial powder amount was smaller. Increasing initial powder amount from 600 to 1400 g, resulted in a much bigger decrease in powder dispensing rate when ultrasonic vibration intensity was 50% than when ultrasonic vibration intensity was 100%. 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

In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R² test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage. Full article

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

Laser powder bed fusion (LPBF) is an advanced additive manufacturing technology that is gaining increasing interest for biomedical implants because it can produce dense, patient-specific metallic components with controlled microstructures. This study investigated the LPBF fabrication of 316L stainless steel, which is widely used in orthopedic and dental implants, and examined the effects of laser power and scanning speed on the microstructure and mechanical properties relevant to biomedical applications. The study achieved 99.97% density and refined columnar and cellular austenitic grains, with optimized molten pool morphology. The optimal LPBF parameters, 190 W laser power and 700 mm/s, produced a tensile strength of 762.83 MPa and hardness of 253.07 HV_0.2, which exceeded the values of conventional cast 316L stainless steel. These results demonstrated the potential of optimized LPBF 316L stainless steel for functional biomedical applications that require high mechanical integrity and biocompatibility. Full article

This work expands the processing window of the laser powder bed fusion (LPBF) processing of WE43 magnesium alloy by evaluating laser powers and scanning speeds up to 400 W and 1200 mm/s, and their effect on densification, microstructure, and electrochemical performance. Relative density of 99.9% was achieved for 300 W and 800 mm/s, showing that the use of high laser power is not a limitation for the manufacturing of Mg alloys, as has been usually considered. Microstructural characterisation revealed refined grains and the presence of RE-rich intermetallic particles, while microhardness increased with height due to thermal gradients. Electrochemical testing in 3.5 wt.% NaCl solution, a more aggressive media than those already used, indicated that the corrosion of samples with density values below 99% is conditioned by the porosity; however, above this value, in the WE43, the corrosion evolution is more related to the microstructure of the samples, according to electrochemical evaluation. This study demonstrates the viability of high-energy LPBF processing for WE43, offering optimised mechanical and corrosion properties for biomedical and structural applications. Full article

Due to the high potential of shot peening to improve the surface quality of additively manufactured components, in this work, the influence on surface morphology and, thus, the surface topography and selected properties of the surface and subsurface regions of additively manufactured parts is analysed. For this, cubic specimens made of stainless steel 316L and AlSi10Mg were manufactured via powder bed fusion laser beam metal (PBF-LB/M), and subsequently, their “as-built” surfaces were shot peened. Shot peening was conducted with stainless steel or ceramic beads using pressures of 3 and 5 bar. The resulting morphologies were analysed regarding topography, microstructure and mechanical properties (hardness and cyclic deformation behaviour) in the subsurface region and the residual stresses. The results demonstrate a strong plastic deformation due to shot peening, resulting in a decreased surface roughness as well as an increased hardness and compressive residual stresses near the surface. These effects were generally more pronounced after using higher peening pressure and/or ceramic beads. Note that two sets of PBF-LB/M parameters were used to produce the AlSi10Mg specimens. The investigation of these specimens reveals an interrelation between the parameters used in shot peening and PBF-LB/M on the resulting surface morphology. Full article

Laser beam powder bed fusion of metals (PBF-LB/M, or simply L-PBF) has emerged as one of the most competitive additive manufacturing technologies for producing complex metallic components with high precision, design freedom, and minimal material waste. Among the various categories of additive manufacturing processes, L-PBF stands out, paving the way for the execution of part designs with geometries previously considered unfeasible. Despite offering several advantages, parts with overhang features require the use of support structures to provide dimensional stability of the part. Support structures achieve this by resisting residual stresses generated during processing and assisting heat dissipation. Although the scientific community acknowledges the role of support structures in the success of L-PBF manufacturing, they have remained relatively underexplored in the literature. In this context, the present work investigated the impact of laser power and scanning speed on the dimensioning, integrity and tensile strength of single-walled block type support structures manufactured in AISI 316L stainless steel. The method proposed in this work is divided in two stages: processing parameter exploration, and mechanical characterization. The results indicated that support structures become more robust and resistant as laser power increases, and the opposite effect is observed with an increment in scanning speed. In addition, defects were detected at the interfaces between the bulk and support regions, which were crucial for the failure of the tensile test specimens. For a layer thickness corresponding to 0.060 mm, it was verified that the combination of laser power and scanning speed of 150 W and 500 mm/s resulted in the highest tensile resistance while respecting the dimensional deviation requirement. 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