Search Results (224)

Laser powder bed fusion (LPBF) enables the fabrication of geometrically complex metallic components, but sharp-edged features remain susceptible to localized heat accumulation because bidirectional meander scanning produces progressively shortened scanning vectors near acute apices. The resulting imbalance between repeated energy input and limited heat dissipation destabilizes melt-pool behavior and degrades surface integrity, densification and microstructural uniformity. In this study, a geometry-informed active energy modulation strategy was proposed for LPBF of Inconel 718 sharp-edged components. A dimensionless Power Reduction Factor (PRF) was formulated to couple laser power attenuation with scan-vector shortening. Coupled DEM–CFD simulations showed that power reduction in heat-accumulation-prone regions decreased peak temperature and suppressed excessive melt-pool expansion caused by adjacent-track thermal history. In situ coaxial photodiode monitoring revealed that apical heat accumulation intensified with i build height under insufficient power reduction, whereas increasing the PRF reduced the overheated zone. Among the tested conditions, PRF = 0.3 provided the most favorable balance between overheating and energy deficiency, minimizing apical surface roughness and maintaining a relatively uniform microhardness distribution above 290 HV0.1. Lower PRF values resulted in swelling and hardness degradation, while higher PRF values promoted track instability and lack-of-fusion defects. Microstructural characterization revealed a persistent intra-track hierarchy in a cellular dendritic scale, while location-dependent PDAS variations along the sharp-edged region were comparatively modest. The observed trends indicate that spatially graded power attenuation modifies local cellular-scale heterogeneity. These results demonstrate that PRF-based modulation provides a quantitative route for mitigating scan-vector-shortening-induced heat accumulation in sharp-edged geometries without additional cooling delays. Full article

Titanium alloys such as Ti-6Al-4V are widely used in orthopedic implants due to their strength and biocompatibility. Laser Surface Remelting (LSR) offers a promising approach to modify surface properties without altering bulk characteristics. This study investigates the effects of varying melt pool depths (MPDs) from 0 μm to 30 μm in 10 μm steps on the mechanical behavior of Ti-6Al-4V femoral components in Total Knee Replacement (TKR) using a comprehensive computational approach combining Finite Element Analysis (FEA) and computational algorithm-based automated evaluation. A three-dimensional FEA model was developed and tested under four physiological loading conditions: compression, axial distraction, medial bending, and lateral flexion at 1300 N. Results show that increasing MPD from 0 μm to 30 μm increases the maximum von Mises stress by 4.2% under compression but reduces displacement by up to 51.7% under distraction. An MPD of 20 μm reduces displacement by 48% while increasing stress by only 2.7%, representing an optimal balance. The computational algorithm framework identifies 15–25 μm as the optimal range for balancing surface enhancement with mechanical integrity. Experimental validation shows good agreement between simulated and measured results, confirming the reliability of the proposed framework for optimizing surface modification parameters in orthopedic implants. Full article

Laser powder bed fusion (LPBF) of high-strength aluminum alloy 7075 (AA7075) is severely limited by hot cracking. However, the underlying mechanisms, particularly the coupling between thermal fields, solidification microstructure, and cracking behavior, remain insufficiently clarified. This study elucidates these mechanisms by integrating experimental characterization with thermal simulation to investigate the temperature field, microstructure, and cracking relationships in both AA7075 and a crack-resistant 7075-Er-Zr alloy. Results show that coarse hot crack morphology is highly dependent on linear energy density E_L. In AA7075, E_L < 450 J/m promotes laterally inclined cracks (short, narrow cracks extending from the melt pool boundary toward the track center), whereas E_L higher than that value leads to the continuous centerline cracks (long, wide cracks along the track center). Fine microcracks are also observed at melt pool boundaries. The 7075-Er-Zr alloy demonstrates superior crack resistance. At E_L = 600 J/m, longitudinal centerline cracks still penetrate along the track, but the alloy achieves crack-free tracks at 200 W with scanning speeds above 1000 mm/s, otherwise exhibiting only short discontinuous cracks. Microcracks at melt pool boundaries are markedly suppressed in the modified alloy. The enhanced crack resistance is attributed to Er/Zr-induced grain refinement and a transition to an equiaxed grain structure, which disrupts intergranular gaps. Critically, thermal simulations identify an annular region with a peak temperature gradient. In AA7075, this region develops aligned columnar grains that facilitate both microcracks and centerline cracks. In the 7075-Er-Zr alloy, microcracks are fully eliminated within this region. However, a residual crystallographic texture persists in the annular region, which promotes the continued occurrence of centerline cracks under high energy density (e.g., E_L = 600 J/m). The annular region remains a critical weak link, and its microstructural control determines the prevailing crack type. This work provides a fundamental understanding of the thermal-microstructural origins of cracking and offers a theoretical foundation for developing crack-resistant aluminum alloys via LPBF. Full article

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

Lack of fusion (LOF) is a dominant defect in Laser Powder-Bed Fusion (PBF-LB/M) caused by insufficient overlapping between adjacent melt pools. This study introduces a rapid, first-principles model based on Rosenthal’s analytical solution for a moving point heat source to predict melt pool geometry. Using geometric criteria, the model evaluates whether the melt pool width exceeds the hatching distance and whether the melt pool depth exceeds the layer thickness. Based on these conditions, LOF-based process windows are constructed by plotting laser power against scanning speed and classifying each parameter combination as either LOF or no LOF. The process developed here for constructing LOF process windows can be applied to metallic PBF-LB/M systems. As PBF-LB/M of copper is commonly associated with LOF defects, the approach is examined for pure copper by evaluating a range of laser powers and scanning speeds for both near-infrared (NIR) (1064 nm) and green (515 nm) lasers using copper-specific absorptivity values. The resulting process windows are validated against literature-reported relative density data for pure copper, using high relative density values as indicators of full fusion and lower relative density values reported with LOF characteristics as indicators of lack of fusion. For a 30 µm layer thickness, the predicted LOF boundary agreed with 43 of 46 literature-reported copper PBF-LB/M data points when the data were classified using relative density and reported defect morphology. Sensitivity analysis showed that the agreement changed modestly when the relative-density threshold was reduced from 99% to 98.5% and 98% and that near-boundary classifications were sensitive to the selected absorptivity within the reported NIR range. The agreement supports the use of the framework as a preliminary screening tool for identifying LOF-prone parameter regions. By providing a fast, physics-based screening tool for LOF-limited process windows, this framework offers a computationally efficient alternative to high-fidelity numerical simulations commonly used in PBF-LB/M process development. Full article

The influence of internal substrate geometry on thermal stability during Laser Directed Energy Deposition Repair (DED-R) remains insufficiently understood, particularly for components containing internal cavities and cooling channels. This study investigates the thermal response of solid (Alpha), blind-hole (Bravo), and channeled (Charlie) AISI 316L substrates using dual infrared thermography, transient finite element modeling, and Short-Time Fourier Transform (STFT)-frequency-domain analysis. Despite substantial differences in internal heat-dissipation pathways, all substrate configurations exhibited similar peak surface temperatures (~1700–2100 °C), indicating that conventional temperature monitoring alone is insufficient to distinguish geometry-dependent melt-pool behavior. To address this limitation, a Spectral Entropy Index (SEI) derived from STFT analysis was proposed to quantify thermal stability. The channeled substrate exhibited the lowest entropy value (H_s = 0.172), compared with the solid (H_s = 0.181) and blind-hole (H_s = 0.183) configurations, indicating a more ordered and predictable thermal response. Furthermore, distinct variations in the spectral stability shadow revealed geometry-dependent oscillatory behavior that was not observable from thermal histories. Finite element simulations showed good agreement with experimental measurements in conduction-dominated regions (RMSE ≈ 46 °C), whereas deviations were observed within the melt-pool region (~250–310 °C), highlighting the increasing influence of fluid-flow phenomena not captured by the conduction-based model. The results demonstrate that internal substrate architecture primarily influences melt-pool stability through frequency-domain thermodynamics rather than significant changes in peak temperature. The proposed STFT method provides a quantitative approach for monitoring thermal stability and assessing the feasibility of L-DED repair over complex internal geometries. Full article

The present work compares the corrosion performance of additively manufactured (AM) Ti-6Al-4V ELI (Extra-Low Interstitials) alloy manufactured by Laser-Powder Bed Fusion (L-PBF) using virgin powder (Cycle 1/C1 sample) and reused powder feedstock after up to 34 cycles (Cycle 34/C34 sample) of manufacturing. The effect of powder reuse is also evaluated for anodizing and Flash-PEO-coated specimens in Harrison’s (25 °C) and Hanks’ solutions (37 °C), representing simulated atmospheric precipitation and physiological conditions, respectively. Specimens were characterized using common metallographic techniques, X-ray diffraction, scanning electron microscopy and optical profilometry. Corrosion resistance was evaluated using cyclic potentiodynamic polarization (PDP) tests. The oxygen content in the Ti-6Al-4V reaches 0.14 wt.% after 34 cycles (C34) of powder reuse, enhancing its passivity in both Harrison’s and Hanks’ solutions. Both virgin and reused powder builds are susceptible to localized corrosion in Hanks’ solution at potentials above 1.75 V. Melt pool borders are thought to be the preferential sites for localized corrosion, as indicated by Volta potential measurements (ΔV = 100 mV). The number of cycles does not significantly affect the current–voltage responses for anodizing and flash-Plasma Electrolytic Oxidation (Flash-PEO) treatments, although anodizing is slightly more responsive to variations in surface roughness (i.e., real specimen area). Anodizing and Flash-PEO reduce the passive current density by nearly two orders of magnitude. Even after surface treatment, the alloy printed with reused powder revealed better passivity. Flash-PEO coatings yielded significant protection against localized corrosion. This unlocks Flash-PEO processing as a successful protection approach for AM biomedical components. Full article

Laser powder bed fusion (LPBF) poses significant simulation challenges due to its highly nonlinear thermo-fluid-solid coupling. To address this, we propose an adaptive framework coupling the edge-based smoothed finite element method (ES-FEM) and smoothed particle hydrodynamics (SPH) via a bidirectional element-particle transformation algorithm. This integration leverages ES-FEM for modeling solid thermo-mechanical responses and SPH for resolving melt pool dynamics, enabling fully coupled simulation of temperature, fluid flow, and stress within a unified model. The framework comprises three key components: a nodal mass normalization scheme ensuring conservation during transformations, a ghost particle algorithm for solid-fluid heat transfer and interaction, and a bidirectional finite-element-to-particle conversion mechanism. This work represents the first implementation of bidirectional coupling between mesh-free Lagrangian SPH and Lagrangian FEM. The validation against benchmark cases confirms the framework’s accuracy in capturing transient thermal, hydrodynamic, and mechanical behavior. It successfully reproduces key LPBF phenomena, including melt pool morphology, Marangoni flows, and residual stress evolution, demonstrating its suitability for high-fidelity LPBF process simulation. It should be noted that the current ES-FEM-SPH framework has not taken into account the recoil pressure, evaporation, and the interaction between the powder and the molten pool. The powder is regarded as a rigid body. Future work will focus on incorporating these neglected physical factors to further improve the predictive capability of the proposed framework. Full article

This study proposes a novel dynamic composite heat source combining discrete tracking and vapor heating, which can precisely capture the transient energy deposition at the keyhole wall, and further discusses the formation mechanism of weld defects by investigating keyhole evolution and melt pool flow behavior. The weld morphology and dimensions predicted by the simulation are in good agreement with the experimental data, revealing the coupled mechanism between keyhole instability and porosity formation, as well as the generation mechanism of process-type porosity mainly influenced by the keyhole dynamic characteristics and the melt pool flow field together; specifically, keyhole instability forms a vapor cavity that will generate bubbles to participate in melt pool flow if it cannot be re-fused with the keyhole, and the bubble trajectory is related to buoyancy, gravity and liquid flow in the melt pool, with larger bubbles less likely to escape due to greater liquid viscous force. In addition, this study finds that increasing weld power and weld speed helps improve keyhole stability, weaken melt pool circulation intensity and shorten bubble escape path, thereby fundamentally revealing the formation mechanism of porosity defects during electron beam welding (EBW) of aluminum alloy, providing an effective numerical tool for optimizing EBW process parameters, and proposing corresponding inhibition measures to improve weld quality. Full article

This study proposes a stress field data-driven prediction method that combines a finite element thermo-mechanical coupling model with a multi-machine learning framework. This method takes the inversion of stress based on the shrinkage behavior of deposition layers as the core logic, extracts the node displacement shrinkage during the cooling to solidification process of the melt pool in the thermal coupling simulation as the key feature input, and constructs extreme gradient boosting (XGBoost), Gaussian process regression (GPR), and deep convolutional neural network (DCNN) models, respectively, to achieve accurate prediction of nodal effect stress and triaxial stress in the laser directed energy deposition (L-DED) node process. The experimental results show that the XGBoost algorithm performs the best in various stress prediction indicators, and its generated stress distribution cloud map is highly consistent with the thermal coupling simulation results, suggesting a strong correlation between deposition layer shrinkage behavior and the stress field under the investigated conditions. In addition, compared to traditional finite element simulations, this method significantly improves computational efficiency while ensuring prediction accuracy, providing a new approach for rapid assessment of residual stresses. Full article

The dynamic behavior within the melt pool governs the final quality of components fabricated by laser powder bed fusion (LPBF). To address key technical challenges—rapid keyhole evolution, low absorption contrast from metal vapor, and difficulties in quantifying internal flow fields—this study introduces move contrast X-ray imaging (MCXI), a technique leveraging time-series frequency characteristics. Combined with a multi-scale Horn–Schunck global optical flow method, MCXI enables full-field quantitative extraction of the melt-pool velocity field. Experimental validation across feature points shows a relative deviation of less than 2% compared to independent manual feature-point tracking, confirming consistency with the best available experimental ground truth. Analysis reveals the keyhole tail evolution cycle comprises three distinct dynamic stages: expansion, stratification, and contraction, with its area increasing from 1329 μm² to 6508 μm² before stabilizing. For the first time, pore pinch-off events were quantitatively measured, revealing front and rear wall collision velocities of 7.98 m/s and 8.04 m/s, respectively, consistent with available high-fidelity simulations. Furthermore, analysis of the overall melt-pool momentum field demonstrates a near-equal distribution of positive and negative momentum, providing an internal self-consistency check confirming the absence of systematic directional bias in the extracted velocity field. This study enables quantitative analysis of LPBF melt-pool dynamics, providing a novel tool for process optimization and defect control. Full article

Selective laser melting (SLM) offers a promising route for fabricating high-performance Cu–Sn alloys; however, the extremely transient thermal behavior of the molten pool and its influence on microstructural evolution and mechanical properties remain insufficiently understood. In this study, a finite element model based on ABAQUS was developed to simulate the transient temperature field and molten pool dynamics of Cu–10Sn alloy during the SLM process. By systematically varying the volumetric energy density (VED), the interplay among molten pool geometry, thermal characteristics, microstructure, and mechanical performance was investigated through a combination of numerical simulation and experimental investigation. The results reveal that increasing VED significantly enlarges the molten pool dimensions, elevates the peak temperature, and intensifies the maximum heating and cooling rates, thereby altering solidification conditions. At a VED of 208.33 J/mm³, the molten pool reached its maximum dimensions, with a length of 230 μm, a width of 161 μm, and a depth of 85 μm, resulting in the highest relative density within the investigated range (98.33%). Under this processing condition, the Cu–10 wt.% Sn (Cu–10Sn) alloy exhibited microhardness values of 190 HV near the solidified areas of melt pool interior and 208.4 HV near the solidified areas of melt pool boundary, accompanied by an ultimate tensile strength of 494 MPa. These findings elucidate the critical role of molten pool thermal behavior in governing microstructural refinement and mechanical properties of SLM-fabricated Cu–10Sn alloys and provide a mechanistic basis for understanding the effect of process parameters. Full article

This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fusion (LPBF) is widely adopted for fabricating Inconel 718 (IN718) components in aerospace and energy applications; however, navigating its high-dimensional, nonlinear process parameter space remains a central challenge. High-fidelity finite element simulations are computationally prohibitive for extensive parameter sweeps, whereas purely data-driven machine learning (ML) models are limited by data scarcity and unphysical extrapolation behavior. This study presents a physics-informed neural network (PINN) framework that embeds the transient heat conduction equation and Goldak double-ellipsoidal heat source model directly into the neural network training loss, enforcing thermophysical consistency simultaneously with data fidelity. The model was trained on a curated, multi-source dataset of LPBF IN718 parameter combinations drawn from peer-reviewed experimental studies and validated finite element simulation outputs, spanning the laser power (70–400 W), scan speed (200–2000 mm/s), hatch spacing (50–140 µm), and layer thickness (20–50 µm). The PINN predicted the melt pool width, depth, peak temperature, and relative density with mean absolute percentage errors (MAPE) of 3.8%, 4.7%, 3.1%, and 1.9%, respectively, outperforming a baseline artificial neural network (ANN) with an identical architecture. The framework correctly identified the optimal volumetric energy density (VED) window of 55–105 J/mm³, yielding relative densities ≥99.5%, consistent with the published experimental thresholds for IN718. A data efficiency analysis demonstrated that the PINN with 25% training data achieves a performance equivalent to that of the fully trained ANN with 100% data, confirming an approximately four-fold data efficiency improvement attributable to physics-informed regularization, consistent with theoretical predictions. Sensitivity analysis via automatic differentiation confirmed that laser power and scan speed were the dominant parameters (~85% combined variance), which is in agreement with previous studies. This study provides a computationally efficient, interpretable, and physically consistent ML pathway for the accelerated process qualification of IN718 components for aerospace and energy applications. Full article

Selective laser melting (SLM) is a complex additive manufacturing process involving rapid laser–material interaction, steep thermal gradients, and phase change phenomena. In this work, a two-dimensional thermal model based on the lattice Boltzmann method (LBM) is developed to simulate the SLM process of AlSi10Mg and 316L stainless steel (316L SS) alloys. The model captures the laser–material interaction, layer-by-layer deposition, phase change behavior, and heat transfer mechanisms, including conduction and convection. Experimental observations of melt pool width and depth were also performed on the microstructures of the two SLM alloys in order to compare the results with the numerical predictions. For the AlSi10Mg alloy, good agreement is obtained, with relative errors of 19.13% in melt pool width and 7.58% in depth, accurately capturing melt pool penetration and remelting behavior. In contrast, moderate deviations are observed for 316L SS, indicating a higher sensitivity to thermophysical properties and suggesting that further model refinement is required. Overall, the results demonstrate the capability of the LBM framework as an efficient and robust tool for analyzing thermal behavior in SLM and for supporting process parameter optimization. Full article

Optimizing stirring methods is crucial for enhancing the efficiency of the Electric Arc Furnace (EAF) production process. This study explores the mixing characteristics of a 150-ton Consteel EAF. The similarity ratio between the water model and the prototype is 1:8. The average mixing time (AMT) was employed as the criterion to evaluate various stirring methods, including the horizontal deflection angle of side-blowing, non-uniform bottom-blowing layouts, and their combinations. A new ice whose composition was a 35 wt% sugar solution was used to simulate the movement and bonding of scrap steel. The melting and temperature difference were compared in this way. The conclusions are as follows: (1) The side blowing lances with a certain angle of horizontal deflection are more conducive to the mixing of the molten pool. The preferred side-blowing lances’ horizontal deflection angle is 10°. (2) The preferred bottom blowing layout is EKO. The bottom blowing layout needs to pay attention to the offset between the bottom blowing nozzles. Bottom blowing nozzles cannot be too far or too close. Rational non-uniform layout of bottom blowing is better than uniform. (3) The preferred combined stirring layout is the EKN, combined with side blowing, with counterclockwise deflection of 10° in the horizontal direction. Gas injection of side blowing and bottom blowing exhibits complementary action zones, thereby achieving enhanced stirring uniformity in the molten bath. But it is necessary to consider the bottom-blowing and side-blowing positions to avoid the local kinetic energy loss caused by airflow offset. At the same time, the deflection angle of the side-blowing lances should be consistent with the direction of the circulation formed by the non-uniform bottom blowing. (4) Under the rational combined stirring method, the scrap steel moved faster, and the bonding phenomenon was significantly reduced. And the temperature difference decreased the fastest. In summary, the rational combined stirring method is the most preferred method for mixing. Full article