Search Results (69)

Arc-driven powder bed fusion represents a low-cost alternative to beam-based powder bed systems, yet the morphological stability regimes governing single-track formation and the relative influence of process parameters on regime transitions have not been systematically characterised. Manual visual assessment of track morphology is inherently subjective and cannot objectively quantify the parameter hierarchy governing stability boundaries. This study addresses both limitations through two complementary contributions. A deterministic two-stage image-based framework is developed to automatically classify single-track morphology from top-view images of solidified 316L stainless steel tracks, replacing subjective assessment with a reproducible, intervention-free procedure. A gap-based continuity criterion distinguishes discontinuous from continuous melt paths; for continuous tracks, the coefficient of variation in width (CV (coefficient of variation) < 0.15) further separates geometrically stable from transitional morphologies. Building on the image-derived regime labels, two interpretable classifiers—a depth-limited Decision Tree (DT) and a regularised Logistic Regression (LR) —are fitted using applied current, scanning speed, and electrode-to-powder-bed distance as predictors. The classifiers are employed not for predictive generalisation but to extract standardised coefficients and permutation-based feature importance rankings, yielding a model-agnostic, quantitative explanation of which process parameters govern regime transitions. Stable continuous tracks are obtained only within a restricted parameter window. Permutation importance consistently ranks applied current as the dominant predictor, followed by electrode distance and scanning speed, in agreement with the thermophysical interpretation. Logistic Regression coefficients confirm that reduced stand-off distance is a necessary condition for sufficient arc constriction. Supplementary linear regression models indicate that applied current governs melt pool depth, whereas scanning speed is the primary determinant of width variation. The combined framework establishes a reproducible basis for process parameter hierarchy analysis in arc-driven powder bed systems and provides a foundation for regression-based process optimisation. Full article

Powder-bed arc additive manufacturing (PBAAM) may reduce the cost of powder-bed metal additive manufacturing and enable thicker layers than laser powder bed fusion (LPBF), but melt-track stability limits are not well established. Here, 316L stainless steel powder (15–53 µm) was melted by a TIG-based arc in a custom powder-bed system while varying current, travel speed, layer thickness and hatch distance. Single tracks on an inclined bed (≈0–0.4 mm thickness) were used to identify continuity loss and melt-pool width, quantified from top-view images via width profiles, a gap-based continuity metric and the coefficient of variation. Parallel-track tests at 0.15, 0.20 and 0.25 mm layer thickness with hatch distances set to 25%, 50% and 75% of the measured melt-pool width assessed inter-track bonding and lack of fusion, and selected parameters were validated in five-layer builds. Higher current with low-to-moderate travel speeds produced wider, more stable melt pools on the inclined bed. Hatch ratios of 25–50% were the most effective for sustaining fusion in single layers and multi-layer builds, whereas 75% promoted unbonded regions and narrow-track morphologies. Overall, PBAAM can process substantially thicker layers with relatively simple equipment, but requires a narrow, carefully tuned window to balance continuity, fusion and heat accumulation. Full article

Additive manufacturing has emerged as a key enabling technology for in-space manufacturing, offering the potential to reduce logistics mass, enhance mission autonomy, and support long-duration exploration. The suppression of gravity-driven phenomena fundamentally alters melt pool dynamics, heat transfer, surface-tension-dominated flow, and defect formation, limiting the direct transferability of terrestrial AM process knowledge to space applications. This paper reviews the current understanding of metallic additive manufacturing process physics under reduced gravity, with emphasis on melt pool behavior, dimensional stability, and in situ monitoring constraints. Approaches for qualification and certification are critically examined, including the applicability of existing AM standards, the role of digital twins and model-based verification, and emerging strategies for space-based validation. Enabling technologies such as autonomous and AI-assisted fabrication, compact hardware architectures, and alternative energy sources are discussed in the context of reliable in-space operation. By synthesizing current developments and identifying key limitations and open challenges, the review provides a roadmap for advancing additive manufacturing toward operational readiness, supporting sustainable exploration, in-space infrastructure development, and long-duration human presence beyond low Earth orbit. Full article

Titanium carbide has developed into an exceptional reinforcement contender in Aluminium Matrix Composites (AMCs) because of its greater characteristics such as elevated hardness, elevated elastic modulus, low heat conductivity, and constancy at moderately elevated temperatures. Furthermore, it is consequently selected as the reinforcing segment in AMCs because of its good thermodynamic and wettability stability inside the aluminium melt pool. In this work, titanium carbide powder was mixed to distinguish AlSi₁₀Mg strengthening by the additive manufacturing (AM) process in the category of powder bed identified as Powder Bed Fusion (PBF). The objective of the study was to have homogeneously mixed powders for processing on the reinforcement of AlSi₁₀Mg with TiC. Different characterisation procedures were carried out, such as scanning electron microscope energy dispersive X-ray spectroscopy (SEM-EDS), pycnometry, and thermogravimetric analysis (TGA). The advancement of powder density from 2.65 to 2.72 g/cm³ and surface area from 0.02 to 0.14 m²/g was accomplished. The modelling findings concurred that the addition of Ti and C increases the density of the alloy, with Ti contributing more to AlSi than C. It was deduced that with Ti and C added to the system, the bulk modulus increases, with Al₆Si₈TiC having the largest value of 80.34 GPa. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

Additive manufacturing (AM) has emerged as a pivotal technology in component fabrication, renowned for its capabilities in freeform fabrication, material efficiency, and integrated design-to-manufacturing processes. As a critical branch of AM, metal additive manufacturing (MAM) has garnered significant attention for producing metal parts. However, process anomalies during MAM can pose safety risks, while internal defects in as-built parts detrimentally affect their service performance. These concerns underscore the necessity for robust in-process monitoring of both the MAM process and the quality of the resulting components. This review first delineates common MAM techniques and popular in-process monitoring methods. It then elaborates on the fundamental principles of acoustic emission (AE), including the configuration of AE systems and methods for extracting characteristic AE parameters. The core of the review synthesizes applications of AE technology in MAM, categorizing them into three key aspects: (1) hardware setup, which involves a comparative analysis of sensor selection, mounting strategies, and noise suppression techniques; (2) parametric characterization, which establishes correlations between AE features and process dynamics (e.g., process parameter deviations, spattering, melting/pool stability) as well as defect formation (e.g., porosity and cracking); and (3) intelligent monitoring, which focuses on the development of classification models and the integration of feedback control systems. By providing a systematic overview, this review aims to highlight the potential of AE as a powerful tool for real-time quality assurance in MAM. Full article

This study investigates the effect of Interlayer Dwell Time (IDT) on the thermal behavior of the Wire-Laser Directed Energy Deposition (WLDED) process. A two-dimensional transient thermal model was developed in MATLAB, incorporating temperature-dependent material properties, a moving Gaussian heat source, and melting–solidification phase change to simulate sequential layer deposition. The model was calibrated for thin-walled geometries, numerically validated using ANSYS, and experimentally validated with literature data. Using the validated model, twenty-seven cases were simulated to examine the combined influence of IDT, part length, and layer thickness on melt-pool dimensions and layer-wise temperature distribution. The results show that increasing IDT reduces melt-pool depth and length by limiting heat accumulation, with the magnitude of this effect depending strongly on part length and layer thickness. Shorter parts and thicker layers exhibit the highest sensitivity to IDT variations. Additionally, the Thermal Stability Factor (TSF) is introduced, a dimensionless index that effectively identifies heat-accumulation phenomena and indicates thermal instabilities. Overall, the findings enhance the understanding of the impact of IDT in the thermal profile of WLDED and demonstrate that optimized IDT selection can stabilize melt-pool geometry and reduce thermal buildup, supporting future adaptive IDT strategies in wire-based metal additive manufacturing. Full article

The wire Laser-based Directed Energy Deposition (DED-LB) metal additive manufacturing (AM) process is time- and cost-effective, providing high-quality, dense parts while supporting multi-scale manufacturing, repair, and repurposing services. However, its ability to consistently produce parts of uniform quality depends on process stability, which can be achieved through monitoring and controlling key process phenomena, such as heat accumulation and variations in the distance between the deposition head and the working surface (standoff distance). Part quality is closely linked to achieving predictable melt pool dimensions and stable thermal conditions, which in turn influence the end-part’s cross-sectional stability, overall dimensions, and mechanical properties. This work presents a workflow that correlates process and metrology data, enabling the determination of tunable process parameters and their operating process window. The process data are acquired using a vision-based monitoring system and a load-cell embedded in the deposition head, which together detect variations in melt pool area and standoff distance during the process, while metrology devices assess the part quality. Finally, this monitoring setup and its ability to capture the complete process history are fundamental for developing in-line control strategies, enabling optimized, supervision-free, and repeatable processes. Full article

This study investigates the microstructural evolution, porosity characteristics, and mechanical behavior of LPBF-manufactured Scalmalloy^®, which were investigated in the as-built conditions and after long-term exposure to direct aging of 275, 325, and 400 °C. Optical microscopy, and electron backscatter diffraction (EBSD) analyses were employed to examine the grain morphology, pore distribution, and defect characteristics. In the as-built state, the microstructure displayed the typical fish-scale melt pool morphology with columnar grains in the melt pool centers and fine equiaxed grains along their boundaries, combined with a small number of gas pores and lack-of-fusion defects. After direct aging, coarsening of grains was revealed, accompanied by partial spheroidization of pores, though the global density remained above 99.7%, ensuring structural integrity. Grain orientation analyses revealed a reduction in crystallographic texture and local misorientation after direct aging, suggesting stress relaxation and a more homogeneous microstructure. The hardness distribution reflected this transition: in the as-built state, higher hardness values were found at melt pool edges, while coarser central grains exhibited lower hardness. After direct aging, the hardness differences between these regions decreased, and the average hardness increased from (104 ± 7) HV0.025 to (170 ± 10) HV0.025 due to precipitation of Al₃(Sc,Zr) phases. Long-term aging studies confirmed the stability of mechanical performance at 325 °C, whereas aging at 400 °C induced overaging and hardness loss due to precipitate coarsening. Electrical conductivities increased monotonically at all tested temperatures from ~11.7 MS/m, highlighting the interplay between solute depletion and precipitate evolution. Full article

Laser wire additive manufacturing (LWAM) offers high deposition efficiency and excellent material utilization. However, manufacturing thin-walled structures with large inclination angles and no support remains a challenge. In this study, the influence of laser tilt angle on the formability of multi-layer inclined parts was systematically investigated. Results reveal that tilting the laser redistributes energy input along the inclination direction, stabilizing the melt pool and reducing angular deviation. Under a 20° tilt condition, thin-walled structures with inclination up to 70° were successfully fabricated, overcoming the limitation of conventional vertical deposition. Furthermore, a multi-inclination arch bridge structure was fabricated under optimized conditions, demonstrating good morphological appearance, dimensional accuracy (deviation within ±0.3 mm), and surface waviness (W < 0.12 mm). The findings provide new insights into the mechanism of energy redistribution in tilted LWAM and establish a promising strategy for manufacturing complex overhanging structures in aerospace and automotive industries. Full article

316L austenitic stainless steel is an ideal candidate for high-temperature applications. However, the relatively low strength and poor wear resistance at high temperatures significantly limit its application in high-temperature environments. In this study, we address this challenge by tracing TiB₂ microalloying austenitic steel via L-PBF (laser powder bed fusion), a micro-melting pool metallurgy method. The results show that adding 2.5 wt.% TiB₂ significantly refines the austenite grain size from ~19 μm to ~1 μm. The austenite grain size characterizes thermal stability at 300 °C and 600 °C. The fabricated TiB₂-reinforced steel shows extraordinarily high-temperature tensile strength, achieving 740 MPa and 636 MPa at 300 °C and 600 °C, respectively. The high tensile strength under high temperature is attributed to the TiB₂ phase strengthening and ultrafine austenite grain sizes. Regarding the high-temperature wear friction coefficient of 0.69 at 300 °C and 0.47 at 600 °C, the predominant wear mechanism is abrasive wear, accompanied by adhesive and oxidative wear mechanisms. The present study provides new insight for the development of L-PBF austenitic steels that combine high-temperature strength with superior wear resistance. Full article

To develop a cost-effective titanium alloy tailored for laser powder bed fusion (LPBF), a novel Ti-5.2Al-5Fe (wt.%) dual-phase alloy was designed and fabricated in this study. The composition was optimized for low density (4.4 g/cm³), high yield strength (1052 MPa), and suitable β-phase stability ([Mo]_eq = 9.3%). The alloy demonstrated excellent formability, achieving high densification (porosity ≤ 2%) and hardness (>400 HV) over a wide volumetric energy density range (48–204 J/mm³). The Al element inhibited balling by improving melt pool wettability, while the Fe element synergistically promoted densification by lowering the liquidus temperature. The as-built microstructure comprised α and β phases, with the α-phase content increasing significantly from 25.4% to 60.8% with higher energy density. While all samples exhibited high tensile strength (>1290 MPa), ductility was limited (<2.6%). EBSD analysis identified the α-phase as the primary carrier of micro-residual stress, with a high density of “zero-solution” points, low-angle grain boundaries, and KAM values. This indicates severe stress concentration from rapid solidification and phase transformation, elucidating the fundamental reason for the low ductility. This study provides systematic insights from composition design to microscopic mechanisms for designing LPBF-dedicated titanium alloys with a wide process window. Full article

Effective thermal management is essential in metal additive manufacturing to ensure process stability and desirable material properties. Directed energy deposition using a laser beam and wire (DED-LB/w) enables the production of large, high-performance components but remains sensitive to adverse thermal effects during multi-layer deposition due to heat accumulation. While prior studies have investigated interlayer temperature control and substrate preheating in DED modalities, including laser-powder and arc-based systems, the influence of substrate preheating in DED-LB/w has not been thoroughly examined. This study employs substrate preheating to simulate heat accumulation and assess its effects on melt pool geometry, wire–melt pool interaction, and the microstructural evolution of Alloy 718. Experimental results demonstrate that increased substrate temperatures lead to a gradual expansion of the melt pool, with a notable transition occurring beyond 400 °C. Microstructural analysis reveals that elevated preheat temperatures promote coarser secondary dendrite arm spacing and the development of wider columnar grains. Moreover, Nb-rich secondary phases, including the Laves phase, exhibit increased size but relatively unchanged area fractions. Observations from electrical conductance measurements and coaxial visual imaging show that preheat temperature significantly affects the process dynamics and microstructural evolution, providing a basis for advanced process control strategies. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. 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