Search Results (112)

This study investigates the influence of varying positive–negative polarity ratios (EP:EN) on melt pool evolution during alternating current CMT (VP-CMT) arc additive manufacturing through a combined experimental and numerical approach. A multi-layer single-track droplet-melt pool coupling model was established, revealing the regulatory mechanisms governing melt pool flow, temperature distribution, and dimensional changes. These are driven by differences in arc morphology, heat input, and mechanical forces during EP and EN phases. Results indicate that molten pool flow is primarily governed by wire feed, retraction, and Marangoni forces. During the EP phase, arc divergence and elevated heat input result in significantly higher flow velocities than in the EN phase. Molten pool length increases with rising EP proportion, exhibiting periodic dynamic variations. Lateral flow intensity intensifies as EP ratio increases, directly influencing cladding layer morphology. This study provides theoretical basis for optimising additive manufacturing quality by adjusting the EP:EN ratio. Full article

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication. Full article

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM. Full article

For addressing difficult detail extraction and low operating efficiency in monitoring sea ice in a large area with wide-field-of-view images from the Chinese Gaofen-1 satellite, a lightweight, high-precision sea ice segmentation network adaptive multistatistic fusion attention (AMFA) module using DeepLabV3+ as the base architecture (AMFA-DeepLab) is proposed. First, the module replaces the backbone network with a lightweight MobileNetV2 to ensure feature extraction capability and greatly reduce model computational complexity using inverted residuals and depthwise separable convolution. Second, to solve the problems of fragmented ice texture blurring and speckle noise interference in optical images, an AMFA is designed and introduced into the decoder side. This module innovatively integrates the global median pooling branch and adapts the recalibrated feature weight through a dynamic channel mixing mechanism, effectively enhancing the model’s capability of capturing fine sea ice edge features and its antinoise robustness in complex backgrounds. Experimental results based on the dataset from Liaodong Bay in the Bohai Sea of China show that the intersection over union of AMFA-DeepLab reaches 92.15% and the F1-score reaches 95.91%, increases of 3.06%, and 1.68%, respectively, compared with those of the baseline model. In addition, only 5.85 million model parameters are needed, the training time is shortened to 4.42 h, and the inference speed is 281.76 frames per second. Visualized analysis and generalization test further demonstrates that this model can accurately eliminate clutter interference from coastal land and seawater and extract the fine filamentous structure of drift ice in the scene of complex melting ice. This research overcomes the precision bottleneck while achieving an ultimate lightweight model, providing efficient technical support for operational dynamic monitoring of sea ice disasters based on Chinese GaoFen-1 satellites. Full article

In metal additive manufacturing, the molten pool directly influences the performance of the fabricated components. Therefore, a comprehensive understanding of the molten pool behavior is essential for improving the quality of the parts and mitigating the formation of defects. Selective arc melting (SAM) is a promising additive manufacturing method for fabricating metal matrix composites. However, the melting and solidification process of the powder layer under the arc heat source remains unrevealed. This study aims to elucidate the formation mechanisms of surface morphology during SAM processing and the influence of carbide addition on the melting and solidification behavior of Inconel 718 powder. In this study, thin-walled parts of Inconel 718 and TiC/Inconel 718 composite were fabricated and their microstructures were studied. The melting and solidification behavior of Inconel 718 and TiC/Inconel 718 composite during single-track single-layer deposition was investigated using high-speed photography. Focusing on the differences in the sidewall surface morphology of the Inconel 718 and TiC/Inconel 718 composite parts, the edge feature formation of the deposition track of both materials was studied. Furthermore, the formation mechanism of the differences in forming height at different positions of the deposition track was explored. The results indicate that the melted material in the molten pool of Inconel 718 mainly comes from the mass transport of the beads generated around the molten pool, while the liquid material in the molten pool of TiC/Inconel 718 composite mainly comes from the in situ powder melted under the arc center. During the melting process of Inconel 718 powder, beads at the edge of the heating area come into contact with the boundary of the molten pool and solidify in situ, forming protrusion features. The randomness in the bead size leads to different volumes of molten material at different positions within the same time, thereby causing variations in building height. Full article

This study presents a novel numerical framework that elucidates the critical, yet previously underexplored, role of Marangoni vortex dynamics in determining the final surface quality during the laser polishing of Ti6Al4V (TC4). TC4 titanium alloy is widely used in aerospace, biomedicine, and other high-precision applications due to its excellent specific strength, corrosion resistance, and biocompatibility. However, its surface quality directly affects the fatigue life and service performance of parts, and traditional polishing methods suffer from low efficiency and high pollution. As a non-contact, controllable surface treatment technology, nanosecond laser polishing has demonstrated unique advantages in balancing processing efficiency and surface quality. This study systematically discussed the influence of key process parameters (spot overlap rate, laser power, and scanning times) on the nanosecond laser polishing of TC4 titanium alloy. It revealed the internal physical mechanism by analyzing the temperature and velocity fields and vortex dynamics during molten-pool evolution. It is found that the polishing effect is determined by the process parameters, which adjust the thermal–fluid coupling physical field (temperature distribution, melt flow, and vortex structure) in the molten pool. There is an optimal combination of parameters (spot overlap rate of 79%, laser power of 0.8 W, scanning speed of 5 m/min, scanning 3 times) that can place the molten pool in an optimal dynamic balance state and achieve effective flatness. The experimental results show that, under this parameter, the surface roughness of the specimen with an initial roughness of 1.223 μm is reduced by about 32%. The research further clarified the mechanism by which the initial roughness of the base metal influences the molten pool: the greater the initial roughness, the more pronounced the “peak shaving and valley filling” effect. Under the same parameters, the improvement rate of the specimen with the initial roughness of 1.623 μm could reach about 40%. This study not only establishes the optimized process window but also reveals the essential relationship between “process parameters–bath behavior–surface quality” from the level of the physical field of the molten pool. The findings provide a practical guideline for parameter optimization, directly applicable to the high-precision laser finishing of critical titanium components in the aerospace and biomedical industries. 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

Energy density is a common but often inadequate parameter for predicting properties in laser additive manufacturing, as it fails to capture complex energy absorption dynamics. This study introduces energy utilization efficiency as a governing factor for melt pool characteristics in laser directed energy deposition (LDED) of 316L stainless steel. We demonstrate that at a constant energy density, energy utilization efficiency varies significantly with process parameters, ranging from conditions that cause lack-of-fusion to those that promote porosity. Experimentally, increasing energy utilization efficiency under constant energy density (90 J/mm) led to a five-fold increase in melt pool depth and a doubling of its area. This shift in energy utilization efficiency directly influenced tensile properties, with samples at moderate energy utilization efficiency achieving optimal yield strength (~428 MPa), ultimate tensile strength (~583 MPa), and elongation (~51.6%). Quantitative strengthening analysis revealed that dislocation strengthening contributed approximately 60% of the total yield strength, but its contribution decreased with excessive energy utilization efficiency due to grain coarsening. To overcome the limitations of energy density, we propose normalized enthalpy as a predictive design parameter. It shows a strong linear correlation with melt pool width, depth, and area, effectively integrating both process inputs and material thermal response. This work provides a fundamental insight into energy–material interactions and offers a physics-enhanced predictive tool that complements conventional energy density metrics for optimizing the LDED process. Full article

Scrap steel is known to influence the fluid flow characteristics of the melt pool in converter steelmaking. However, few studies have considered the effects of the scrap steel charging structure. In this study, a physical model of a 1:8.8 steel–scrap–gas three-phase flow converter was established to investigate the effects of scrap steel state, distribution, material type and shape on the fluid flow characteristics of the converter melt pool. The velocity distribution within the molten pool was measured using particle image velocimetry, while mixing time under various operating conditions was determined using the stimulus–response method. Considering the melting behaviour of scrap steel and the gas utilisation rate comprehensively, the results indicate that when scrap steel is arranged in a uniform position at the bottom of the converter—comprising 90% medium scrap in rectangular scrap and 10% heavy scrap in thin-plate form—and the gas flow rate is 750 m³/h, the overall dynamic conditions of the melt pool are optimal. At this time, the mixing time is 68.2 s (a reduction of up to 45.4%), average velocity is 0.117 m/s (a maximum increase of 207.9%) and turbulent energy dissipation rate is 0.0266 m²/s³ (a maximum increase of 141.8%). Finally, a relationship was established between stirring power and mixing time at different scrap steel charging structures, providing a methodological reference and data support for optimising the charging structure of scrap steel and efficiently using scrap steel in converters. Full article

The amount of scrap steel and selection of blowing process parameters are known to influence the fluid flow characteristics of the melt pool in converter steelmaking. However, few studies have considered the effects of scrap steel and blowing process parameters together. In this study, a physical model of a converter is established to investigate the influences of the amount of scrap steel and bottom-blowing process parameters on the fluid flow characteristics of the melt pool. Particle image velocimetry is used to measure the velocity distribution in the melt pool, and the stimulus–response method is used to measure the mixing time of the melt pool under different operating conditions. The results show that increasing the scrap steel ratio worsens the dynamic conditions of the melt pool. The best of the explored combinations is achieved at a scrap steel ratio of 20% and with six nozzles. The mixing time decreases as the gas flow rate increases, but the rate of decrease also decreases. Based on the results, the mixing time can be predicted from the gas flow rate and the number of nozzles. A relationship between the stirring power and mixing time of a converter using the bottom-blowing process is established. Full article

Selective Laser Melting (SLM), as a common metal additive manufacturing (AM) technology, achieves high-precision complex part formation by layer-by-layer melting of metal powder using a laser. However, the dynamic behavior of the melt pool during the SLM process is influenced by the heat source model, which is crucial for suppressing porosity defects and optimizing process parameters, directly determining the reliability of numerical simulations. To address the issue of traditional surface heat source models overestimating the melt pool width and volume heat source models underestimating the melt pool depth, this study constructs a three-dimensional transient heat conduction finite element model based on ANSYS Parametric Design Language (APDL) to simulate the evolution of the temperature field and melt pool geometry under different laser parameters. First, the temperature fields and melt pool morphology and dimensions of four heat source models—Gaussian surface heat source, volumetric heat source models (rotating Gaussian volumetric heat source, double ellipsoid heat source), and a combined heat source model—were investigated. Subsequently, a dynamic heat source model was proposed, combining a Gaussian surface heat source with a rotating volumetric heat source. By dynamically allocating the laser energy absorption ratio between the powder surface layer and the substrate depth, the influence of this heat source model on melt pool size was explored and compared with other heat source models. The results show that under the dynamic heat source, the melt pool width and depth are 128.6 μm and 63.13 μm, respectively. The melt pool width is significantly larger compared to other heat source models, and the melt pool depth is about 17% greater than that of the combined heat source model. At the same time, the predicted melt pool width and depth under this heat source model have relative errors of 1.0% and 5.5% compared to the experimental measurements, indicating that this heat source model has high accuracy in predicting the melt pool’s lateral dimensions and can effectively reflect the actual melt pool morphology during processing. 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

Laser local forming is an effective method for reshaping optical glass, yet the deformation of the material during the cooling phase remains poorly understood. This study investigates the dynamic evolution of the molten pool, specifically focusing on the transition from an initial convex shape to a final “M-shaped” profile. A combined approach using thermal-fluid simulation and high-speed imaging experiments was employed to track the surface changes throughout the heating and cooling cycles. The results show that while the surface bulges outward during laser irradiation, the material redistributes after the laser is switched off due to non-uniform cooling and volumetric shrinkage. The specific roles of viscosity and surface tension in driving this reverse flow were identified. Furthermore, the study established a quantitative model linking laser parameters to the final surface dimensions, providing a reliable tool for predicting and controlling the precision of glass forming. 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

Electron beam powder bed fusion (EB-PBF) Ti6Al4V often exhibits high vertical surface roughness, limiting its use in high-end applications. In this study, an infrared continuous-wave laser was applied to precisely polish the vertical surface. An orthogonal design identified the optimal condition as 10,400 kW/cm² power density, 800 mm/s scanning speed, and one pass, achieving a minimum Sa of 0.24 μm and a 98.03% reduction compared with the as-built surface. To address the adhered semi-molten particle characteristics of EB-PBF sidewalls, a molten-pool-dynamics-based polishing model was developed and validated, yielding an error as low as 1.24%. Simulations indicate that power density governs the final morphology by controlling molten pool coverage, scanning speed affects polishing efficiency via thermocapillary force, and polishing time influences surface quality by triggering or avoiding melt splashing. Full article