Search Results (1,528)

The manuscript deals with the bending behavior of beams with relatively less investigated cellular topologies based on triply periodic minimal surfaces (TPMSs). Three types of sandwich-type specimens (namely Schoen IWP, Fischer–Koch S, and Schoen F-RD) with five different volume fractions of 10, 15, 20, 25, and 35% (±1%) made of aluminum alloy AlSi10Mg by selective laser melting (SLM) technology were investigated. Three-point bending tests were performed at room temperature on a Zwick/Roell 1456 universal testing machine. The force–deflection dependences were plotted, while in addition to nominal stresses, the effective flexural stiffness and energy absorption to failure were evaluated to compare the properties of the investigated cellular beams. In the preparatory phase, critical aspects of possible premature failure of the samples with the smallest and highest selected volume fractions were addressed, while the manufacturability and fracture surfaces of the samples were assessed in order to improve the input conditions of the setup. By comparing the results obtained in the experimental testing in the second phase, it was found that the highest nominal bending stresses were achieved by the Schoen F-RD structure (although not significantly higher than Fischer–Koch S), but in terms of stiffness and amount of absorbed energy, the Fischer–Koch S structure showed the highest values. The improvement of input parameters led to an increase in the achieved nominal bending stresses by at least 100 MPa for all types of investigated structures compared to the first phase. The combined use of preliminary SLM process optimization, bending tests, and fracture surface/EDX analysis made it possible to relate the flexural response of the investigated TPMS topologies to manufacturing-related defects and premature-failure mechanisms in thin-walled AlSi10Mg cellular structures. The presented specimen configuration is intended as a comparative experimental benchmark for flexural performance of sandwich-type TPMS beams under quasi-static loading. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Aiming at the technical bottleneck that traditional porous structures can hardly achieve mechanical load-bearing and acoustic regulation simultaneously, this study designs and fabricates three implicit surface porous structures (Gyroid, Diamond, Lidinoid) based on the bionic principle of trabecular bone. Experimental characterization and numerical analysis of their mechano-acoustic coupling performance are systematically carried out. Selective Laser Melting (SLM) technology is employed to realize the integrated forming of 316L bionic structures. Quasi-static compression experiments and finite element simulations are conducted to reveal the progressive deformation mechanism and energy absorption characteristics of different topological configurations. The results indicate that the Diamond structure exhibits the optimal comprehensive performance in terms of load-bearing capacity, specific energy absorption and isotropy. On this basis, the sound absorption and sound insulation performances of the structures are evaluated via an acoustic impedance tube test. The results show that the Diamond structure possesses a remarkably higher sound absorption coefficient and sound insulation value in the high-frequency range than other configurations, demonstrating excellent acoustic energy dissipation and sound wave isolation capability. The research indicates that the synergistic optimization of mechanical and acoustic performances can be achieved by regulating the Triply Periodic Minimal Surface (TPMS) topological configuration. Benefiting from its efficient stress transfer paths and intricate sound wave propagation channels, the Diamond structure realizes the coupling of high load-bearing capacity, superior energy absorption and favorable acoustic performance. This work provides a theoretical basis and technical support for the design of bionic porous structures in multifunctional scenarios such as bone implants and protective noise reduction. Full article

The emergence of selective laser melting (SLM) has enabled the fabrication of complex structures with exceptional mechanical performance. However, process-induced defects, including porosity and geometric deviations, pose significant challenges to structural reliability, and their dynamic evolution under loading remains poorly understood. In this study, helical layered square honeycomb structures were fabricated via SLM. The effects of process conditions on defect characteristics, as well as the influence of porosity and wall thickness defects on mechanical properties, were investigated using X-ray computed tomography (CT), in situ loading tests, and finite element simulation. The results indicate that the investigated high-quality process conditions minimize porosity, optimize pore morphology, and improve wall thickness uniformity, thereby substantially reducing the adverse effects of pores on tensile properties. Under compressive loading, defect evolution, including pore expansion and wall thickness thinning, is primarily concentrated at structural corners, with more pronounced variations observed under coarse process conditions. Increased porosity, wall thickness reduction, and uneven thickness distribution all degrade the quasi-static compressive performance and medium to high-velocity impact resistance of the structure. Furthermore, thickness distribution exerts an independent influence on mechanical properties beyond the effect of overall average thickness. 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

Refractory high-entropy alloys (RHEAs) have attracted increasing attention for structural applications under extreme conditions; however, the uniformity and reliability of their mechanical properties remain critical challenges, particularly when processed by additive manufacturing. In this work, the microstructural heterogeneity and mechanical uniformity of a selective laser melting (SLM)-fabricated MoNbZrTaW RHEA were systematically investigated. Microstructural characterization revealed a dual-phase BCC structure with dendritic and interdendritic regions distributed along the build direction. Statistical analyses were employed to quantify variations in microstructure and mechanical properties, including hardness, fracture strength, yield strength, and fracture strain. The effects of strain rate and specimen aspect ratio on mechanical behavior were further examined through compression testing. Weibull statistical analysis demonstrated that strength-related properties exhibit high uniformity despite pronounced microstructural heterogeneity, whereas fracture strain shows comparatively greater scatter. The results indicate that solid-solution strengthening governs the mechanical response and helps mitigate the influence of microstructural non-uniformity. These findings provide important insights into the mechanical reliability of SLM-fabricated RHEAs under room-temperature quasi-static loading, and support their potential for further investigation in advanced structural applications. Full article

This study addresses the challenge of modeling the complex non-linear relationship between process parameters and relative density in selective laser melting (SLM) of IN718 nickel-based superalloy under small-sample conditions. A data-driven prediction framework integrating data augmentation, physics-informed feature engineering, machine learning, and model interpretability analysis was developed and systematically validated. Fourteen sets of experimental data covering both vertical and horizontal building directions were collected by varying laser power (P), scan speed (v), and hatch spacing (h). To overcome the small-sample limitation, three augmentation strategies—radial basis function (RBF) interpolation, generative adversarial network (GAN), and K-nearest neighbors (KNN)—were systematically compared under unified physical constraints combining local perturbation and volumetric energy density (E_vol) filtering, with Pearson correlation coefficient consistency used to select the optimal strategy. Eight physically meaningful input features were constructed, including E_vol and line energy density (E_line), explicitly embedding SLM process physics into the learning framework. Support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were trained and their hyperparameters were systematically optimized via exhaustive grid search combined with leave-one-out cross-validation (LOO-CV), ensuring robust model selection under small-sample constraints. A physics-based baseline model (E_vol quadratic fitting, LOO-CV average R² = 0.2534) was established to quantify the gain of machine learning over empirical formulas. LOO-CV results show that ANN achieves the highest average R² of 0.9269, followed by SVR (0.9148) and RF (0.8393), all of which substantially outperform the physical baseline. Feature importance analysis reveals that E_vol accounts for 51.58% of the predictive power, and ablation experiments confirm that introducing physics-derived features improves the average R² by 0.0246 compared with raw process parameters alone. To further elucidate the predictive mechanism of the optimal ANN model, Partial Dependence Plot (PDP) analysis was conducted for all eight input features, visualizing their marginal effects on predicted density and confirming physical consistency with SLM mechanisms. This framework provides a reliable, interpretable, data-driven solution for intelligent SLM process optimization with limited experimental data. Full article

Additive manufacturing (AM) introduces surface roughness that is much larger than that in chemically etched printed circuit heat exchanger (PCHE) channels, limiting the applicability of established design correlation. In this study, four selective laser melting (SLM) 3D-printed stainless steel test sections were tested, namely two semicircular and two rounded-edge semicircular channels, at hydraulic diameters of 2 mm and 4 mm. Water was used as the test fluid in the experiment, with a Reynolds number ranging from 500 to 7000 and wall heat flux ranging from 20 to 90 kW/m². Scanning electron microscopy image characterization shows significant material accumulation concentrated at the rounded edges of the as-built channels. The experimental results show that for the entire flow regime, the printed rounded edge increases the friction factor by approximately 9% for 2 mm and 4 mm channels. The filleting design would increase the effective hydraulic roughness in small-diameter AM channels. The SLM 3D-printed rougher channel has a lower transition Reynolds number and higher turbulent friction factors compared to the etching channel. The data were compared with existing smooth PCHE channel data and rough AM mini-channel correlation, and two empirical correlations were developed for SLM 3D-printed mini-channels for transition and turbulent regimes. Full article

Surface defects in additively manufactured internal channels limit their practical applications. Conventional post-processing methods suffer from limited accessibility and a tendency toward over-polishing, whereas magnetic abrasive finishing (MAF) offers high adaptability and precise process controllability. This study systematically investigates the material removal mechanisms in magnetic abrasive polishing and clarifies the distinctions and transitions between two-body and three-body wear modes. Based on these findings, a rolling removal model grounded in rough surface contact theory and a sliding removal model incorporating correction factors are established. Experiments were conducted on AlSi10Mg internal channels fabricated via selective laser melting (SLM) using a composite magnetic field polishing apparatus. The results verify the accuracy of the proposed models and demonstrate that the process effectively reduces surface defects and surface roughness. Although some deviations arise from model idealization and non-uniform magnetic field distribution, this study establishes a systematic theoretical framework for material removal in additively manufactured complex internal channels. Full article

Al₂O₃ single beads were deposited on a Ti-6Al-4V (Ti64) substrate by laser-directed energy deposition (DED-LB) to establish baseline process conditions for ceramic protective layers and future Ti64/Al₂O₃ functionally graded materials (FGMs). These ceramic-containing surface layers are applicable to titanium components requiring improved oxidation, wear, and thermal resistance in aerospace, automotive, and high-temperature structural applications. Laser power (300–700 W) and scan speed (300–700 mm/min) were varied, and bead geometry was quantified from cross-sectional observations; energy density and dilution ratio were calculated. Melt pool depth increased with higher power and lower speed, indicating increased heat input and substrate melting. Crack formation in the melt zone was more sensitive to laser power than to scan speed. In contrast, bead height showed a non-monotonic response to energy density, which may be associated with possible coupled effects such as recoil pressure-driven melt pool disturbance, powder scattering, and insufficient powder melting at high scan speeds. Dilution-based optimization identified 300 W laser power and 400 mm/min scan speed, with a powder feed rate of 3 g/min, as the most suitable condition within the investigated process window, giving the lowest practical dilution ratio of approximately 40.27%. SEM–EDS and XRD analyses were conducted to examine the interfacial microstructure and phase characteristics under the selected condition. Overall, this study provides fundamental process guidelines and mechanistic insight into bead formation, dilution behavior, and interface formation, supporting the future application of DED-LB-based ceramic protective or graded layers on Ti64 surfaces. Full article

The increasing demand for porous stainless-steel materials produced by selective laser melting (L-PBF) for biomedical implants, filtration systems, heat exchangers, and energy devices has created an urgent need to improve their mechanical performance. Optimizing process parameters and microstructural properties is therefore critical for enhancing the overall functionality and reliability of L-PBF porous stainless-steel structures. This paper studies the effect of an aging heat treatment on the mechanical properties of L-PBF specimens, manufactured with stainless steel Uddeholm Corrax powders. The porosity was selected to be about 3%, based on manufacturer’s experience on the production injection mold inserts, with the ability to drain air. To reach this porosity, a set of manufacturing variables were selected, quantified in terms of VED (Volumetric Energy Density) of 59.01 J/mm³. The analysis of the mechanical behavior was focused on the compressive and flexural strength, dynamic Young’s modulus and the energy dissipation during earlier fatigue loading cycles. This study concluded that the heat treatment produces a negligible effect on dynamic Young’s modulus and increases the bending strength by about 25% and the compressive plateau strength by about 17%. Both specimens’ batches exhibit similar fatigue strain accumulation for cyclic compressive tests. Full article

Pulsed laser-induced phase change in silicon underpins applications from photonic device trimming to stealth dicing, yet predictive models that capture the non-equilibrium kinetics governing the competition between epitaxial recrystallization and amorphization remain limited. In this work, we developed a two-dimensional axisymmetric numerical model at the continuum level for picosecond laser-induced melting, resolidification, and amorphization of crystalline silicon at 532 nm laser wavelength, coupling transient heat conduction with Wilson–Frenkel interface kinetics and Lagrangian marker-based interface tracking. The model predicts a bounded amorphization window defined by lower and upper fluence thresholds, within which the central amorphous thickness exhibits a bell-shaped fluence dependence. Under a Gaussian beam, this window governs a morphological transition from a central amorphous spot to an amorphous ring. The predicted amorphization threshold of ≈0.22 J/cm² agrees with published experimental data for 20 ps, 532 nm irradiation. Parametric studies reveal that reducing the spot diameter or substrate temperature shifts or eliminates the upper threshold, transforming the bounded window into a monotonically increasing function, while increasing the pulse duration narrows the window symmetrically until collapse. These results provide quantitative guidelines for selecting irradiation parameters to control phase change in silicon photonic and laser processing applications. Full article

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

Selective laser melting (SLM) offers significant potential for fabricating lightweight 316L stainless steel lattice structures (LSs), while forming defects and microstructural heterogeneity remain challenging, especially in fine struts. In this study, response surface methodology (RSM) and analysis of variance (ANOVA) were employed to quantify the coupled effects of geometric parameters (forming angle, FA; rod diameter, RD) and processing parameters (laser power, LP; scanning speed, SS; hatch spacing, HS) on the mass deviation (MD) of fine struts. The results show that FA and RD are the dominant factors affecting MD within the investigated parameter range, whereas LP and SS exhibit comparatively weaker effects. Representative samples with different FA and RD were further characterized by SEM, XRD, and EBSD to examine the associated microstructural evolution. The observations indicate that changes in FA and RD are accompanied by variations in solidification morphology, defect distribution, crystallographic texture, and GND density. Higher FA is associated with lower MD and stronger texture alignment along the building direction, whereas larger RD tends to promote columnar growth and enhanced texture intensity. These results suggest that geometric parameters can serve as effective design variables for tailoring forming deviation and representative microstructural characteristics of fine struts in SLM-fabricated 316L lattice structures. Full article

This investigation elucidates the elevated-temperature (650 °C) monotonic mechanical response and very-high-cycle fatigue (VHCF) characteristics of Inconel 718 superalloys additively manufactured via selective laser melting (SLM), with a comparative assessment between the as-built and post-process heat-treated states. The results indicate that mechanical performance improves after heat treatment, primarily due to the formation of γ′ and γ″ precipitates, which interact with dislocations to strengthen the alloy. Relative to the as-built specimens, the fatigue strength of the specimen after heat treatment has increased by more than twice. For the as-built specimen, fatigue cracks nucleate at the specimen surface. However, in the high stress range, crack initiation in the heat-treated specimens consistently occurs at the free surface, whereas under low stress conditions, the crack initiation site transitions to the subsurface region encompassing internal defects. Post heat treatment, the fatigue crack trajectory adopts a markedly ductile and tortuous morphology, engendered by the concerted influence of grain-boundary (Laves/δ) precipitates that enforce repeated crack deflection, matrix-strengthening phases that homogenize plastic strain and the attendant reduction in local strain accumulation under the effect of cyclic load. Full article