Search Results (522)

Additive manufacturing enables the production of geometrically complex nickel-based superalloy components, but the high-temperature tensile response of selective laser-melted Inconel 718 remains strongly dependent on manufacturing route and build orientation. In this study, the tensile behavior of Inconel 718 specimens machined from wrought bar stock and fabricated by selective laser melting in horizontal and vertical build orientations was investigated at 23 °C, 450 °C, 550 °C and 750 °C. The experimental values were used in a finite element simulation to create a model that can accurately predict the mechanical behavior of IN718. The results show that the X-oriented SLM specimens exhibit tensile properties comparable to those of the wrought material, whereas the Z-oriented specimens display reduced strength and increased scatter, highlighting the effect of build orientation on mechanical performance. The numerical simulations reproduced the experimental stress–strain response with good agreement within the elastic and plastic deformation regimes, demonstrating the suitability of the proposed modeling approach for high-temperature structural assessment of Inconel 718 components manufactured by conventional and additive technologies. Full article

Laser Powder Bed Fusion (LPBF) is widely employed for manufacturing complex metallic components; however, process-induced distortion, residual stress, and thermal accumulation remain major challenges affecting dimensional accuracy and structural integrity. In the present study, a three-dimensional thermo-structural finite element model was developed to investigate the combined influence of component orientation and base plate material on the thermo-mechanical behavior of LPBF-fabricated Inconel 718 components. In addition, an integrated optimization methodology combining response normalization, a weighted sum performance index, and radar chart analysis was adopted to realize the optimal combination of the process variables by a simultaneous consideration of distortion, residual stress, and temperature. Accordingly, five part orientations and five different base plate materials were examined under identical processing conditions through 25 simulation cases. The obtained results revealed that component orientation represents the dominant factor controlling distortion and residual stress development, whereas both orientation and base plate material significantly affect component temperature. The inclined orientations generated the highest distortion levels, while the vertical configuration exhibited the highest residual stresses. In contrast, the horizontal orientation along the X-direction demonstrated the most balanced thermo-mechanical performance. Furthermore, AlSi10Mg base plate material provided the lowest thermal accumulation due to its high thermal conductivity. The integrated optimization analysis based on radar chart assessment and performance index evaluation identified the Horizontal-X/Ti-6Al-4V and Horizontal-X/AlSi10Mg configurations as the most favorable LPBF conditions. These findings provide practical guidelines for selecting build orientation and base plate material to reduce thermo-mechanical defects, thereby improving the dimensional accuracy and manufacturing reliability of LPBF-fabricated Inconel 718 components. Full article

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

Inconel 718 exhibits poor machinability due to its high strength and low thermal conductivity, which induce severe thermo-mechanical loads. Conventional cooling strategies struggle to concurrently regulate heat dissipation and interface lubrication. This paper proposes a machining method based on Active Thermal Conductive Media (ATCM), which simultaneously exerts the Rehbinder mechanochemical effect and solid-phase enhanced heat transfer effect by pre-coating a liquid graphene film on the workpiece surface. Orthogonal turning tests were conducted using a K313 carbide tool at a cutting speed of 30 m/min, cutting width of 2 mm, and undeformed chip thickness of 0.1 mm. The cutting force, cutting temperature, cutting power, and tool wear characteristics under six machining conditions—dry cutting, flood cutting, Minimum Quantity Lubrication (MQL), Cryogenic MQL (CMQL), Nanofluid MQL (NMQL), and ATCM-assisted cutting—are systematically compared. The results show that ATCM achieves a 21.6% reduction in cutting force, a 20% reduction in cutting temperature, and a 34.9% reduction in cutting power through the synergistic coupling effect of reduced heat generation and enhanced heat dissipation, with adhesive wear and diffusion wear of the cutting tool significantly suppressed. Full article

In this study, the milling performance of Inconel 718 alloys produced by forging (WP1), Inconel 718 produced by Selective Laser Melting (SLM) (WP2), and Inconel 718 (WP3) subjected to heat treatment after SLM, under different cooling/lubrication conditions, was evaluated using experimental and artificial intelligence-based approaches. Microstructural analysis showed a homogeneous fine-grained structure in WP1, while WP2 exhibited dendritic features and porosity. Heat treatment improved the microstructural homogeneity of WP3. The hardness values of WP1, WP2, and WP3 were 457 Hv, 303.33 Hv, and 391 Hv, respectively. Milling experiments yielded cutting forces of 336.5–1185.9 N, surface roughness values of 0.22–1.39 µm, and cutting temperatures of 168–658 °C. Compared with dry machining, MQL reduced average cutting force and cutting temperature by 15.5% and 18.65%, respectively, while improving tool wear and surface integrity. Machine learning models including LR, DTR, SVR, and GPR were developed to predict machining responses. GPR provided the highest prediction accuracy, achieving 98.72% for cutting force and 98.99% for cutting temperature. The results demonstrate that manufacturing route and cooling strategy significantly affect the machinability of Inconel 718 and that machine learning techniques can effectively support machining process optimization. Full article

Activated Tungsten Inert Gas (ATIG) welding employs an activating flux to increase penetration and improve productivity compared with the conventional Tungsten Inert Gas (TIG) process. Conventional fluxes typically consist of metallic oxides dispersed in alcohol- or acetone-based solvents. In this study, a novel flux composed of SiO₂ and TiO₂ nanoparticles suspended in a sodium-silicate solvent was used for welding Inconel 718. The proposed flux achieved full penetration of a 7 mm thick plate at 160 A DCEN using 60° and 90° electrode tip angles, without visible distortion or defects in the examined cross-sections. Microstructural characterization revealed notable changes in the content, morphology, and size of Nb-rich interdendritic constituents consistent with Laves phase formation compared with welds produced without flux. ATIG specimens contained a lower amount of these brittle intermetallic constituents, which exhibited a less branched and more coagulated morphology despite the lower cooling rate. As a result, a greater fraction of alloying elements remained available for dendrite reinforcement rather than being segregated into Nb-rich interdendritic regions, leading to higher weld-metal microhardness in the ATIG60 specimen than in TIG welds. These observations were attributed to enhanced weld-pool stirring caused by molten metal flow toward the weld center and downward through the weld pool, consistent with the reversal of Marangoni convection. Full article

Laser powder bed fusion (LPBF) is a widely utilized metal additive manufacturing (AM) process for fabricating intricate geometries with high mechanical strength. However, achieving defect-free parts remains challenging due to complex thermodynamics and process variability. Component quality is primarily determined by mel-pool morphology, which depends on key process parameters such as laser power, scan speed, and layer thickness. Improper parameter selection causes defects like porosity (keyhole and lack of fusion), balling, and residual stresses, compromising structural integrity. Optimizing these parameters is crucial but difficult due to the multi-scale, multi-physics nature of the process, which traditionally relies on costly, time-intensive experimental trials. We present results from a data-driven approach using machine learning (ML) models to predict and optimize LPBF melt-pool characteristics, reducing reliance on trial-and-error experimentation. We find that laser power and scan speed predominantly influence the melt-pool formation. Higher scan speeds produce more favorable melt pools, whereas excessive laser power at low scan speeds leads to deep keyhole defects. To predict and classify melt pools efficiently, several ML models are deployed, including logistic regression, decision trees, ensemble learning, and fully connected neural networks. The standard neural network achieved the highest cross-validated macro-F1 score of 0.978 ± 0.014, while the weighted neural network achieved the highest recall for the rare optimal melt-pool class, 0.967 ± 0.050. These findings show that class-weighted learning provides a recall-oriented strategy for identifying suitable LPBF process windows, while avoiding overreliance on single train-test split performance. The findings underscore the effectiveness of ML in accurately classifying LPBF melt pools to rapidly identify optimal process parameters. Full article

Non-destructive evaluation of surface cracks in Inconel 718 nickel-based alloys operating at high temperatures is crucial for monitoring aero-engine hot-section components. Conventional eddy current testing is often constrained by thermal core degradation and low signal-to-noise ratios, struggling to meet detection requirements in such extreme environments. To address this, this study proposes an optimized absolute probe integrated with an efficient water-cooling system. A multi-physics finite element model was developed to optimize the probe design, focusing on key parameters such as excitation frequency and the geometric dimensions of the coil and ferrite core. Experimental results demonstrate that the optimized probe significantly enhances detection sensitivity over conventional models. Specifically, the peak amplitude increased by 76.2% and the signal-to-noise improved by nearly 10 dB for a 0.3 mm-deep crack. In practical applications, the probe achieves high-sensitivity detection of a 0.3 mm-deep crack at 500 °C. At 600 °C, it reliably detects a 0.5 mm-deep crack with a coefficient of variation not exceeding 3.5% and it retains detection capabilities even at 650 °C. Therefore, this sensor design strategy proves to be a highly viable method for non-destructive evaluation in extreme industrial thermal environments. Full article

To address the high experimental costs and data scarcity inherent in Directed Energy Deposition (DED), this study proposes a data-efficient hybrid optimization framework for the precision manufacturing of Inconel 718 aerospace components. The framework leverages a two-stage strategy to bridge traditional experimental design with advanced machine learning, ensuring robust process optimization even with limited datasets. In the first stage, the Taguchi method (L16 orthogonal array) was employed for coarse-grained screening to identify influential control factors. In the second stage, a Fully Connected Neural Network (FNN) coupled with Bayesian Optimization (BO) was deployed. Crucially, this machine learning component functions as an optimization-oriented trend surrogate rather than a global regressor, successfully guiding the optimization under extreme data scarcity. The optimized process window yielded exceptional structural integrity, achieving a porosity as low as 0.03%. To thoroughly validate its practical efficacy, tensile testing (ASTM E8/E8M) and Rockwell hardness measurements (ASTM E18) were systematically conducted on the optimized specimens. The mechanical characterization demonstrated an average tensile strength of approximately 1358 MPa and a hardness of ~40 HRC. Finally, the framework was successfully validated through the robotic DED fabrication of a complex-geometry aerospace engine combustion chamber casing, bridging laboratory-scale optimization with authentic industrial applications. Full article

Additive manufacturing using laser powder bed fusion (LPBF) has been widely used to produce nickel-based superalloy components with complex shapes for high-temperature applications requiring creep resistance. In this research, the creep behavior of LPBF Inconel 718 under solution and double-aging heat treatments, performed at 590–650 °C under stresses of 450–550 MPa, is studied. The characterization included optical microscopy, scanning electron microscopy (SEM), porosity analysis, Vickers microhardness tests, and fracture surface examination. The findings revealed that even after heat treatment, the material maintained a mainly directional, columnar microstructure, with an average porosity below 1%, which was unevenly distributed and contained critical defects related to lack-of-fusion (LOF) and trapped powder. Fracture after creep presents regions of ductile failure alongside facets indicative of quasi-cleavage. Kinetic analysis revealed a high stress exponent (n = 18.26) and an activation energy (Qc = 410–538 kJ/mol), indicating that the deformation operates within the power-law breakdown (PLB) regime, where dislocation–precipitate interactions govern the creep rate in this precipitation-strengthened superalloy. Overall, the results highlight that the directional microstructure and residual defects typical of LPBF can reduce the creep resistance of Inconel 718, underscoring the importance of post-processing methods and internal defect control specifically tailored for additively manufactured materials. Full article

Additive friction stir deposition (AFSD) is a solid-state additive manufacturing process that enables the fabrication of fully dense metallic components without common fusion-related defects. Inconel 718, widely used in aerospace and energy sectors, requires high structural reliability; therefore, evaluating its response to AFSD is essential for advanced applications. This study investigates the effects of AFSD on IN718 by comparing the mechanical properties and microstructure of the as-deposited material with the feedstock condition. Tensile testing showed that the ultimate tensile strength (UTS) increased by 5% along the traverse direction, whereas elongation was reduced compared to the feedstock. In contrast, build-direction tensile specimens exhibited lower UTS and substantially reduced elongation, revealing mechanical anisotropy. Microhardness increased by 20%, consistent with substantial grain refinement from 11 µm to 3 µm due to dynamic recrystallization during deposition. X-ray diffraction (XRD) revealed no clearly detectable secondary phase formation after AFSD within the resolution limits of conventional XRD, suggesting that the increased hardness and traverse-direction strength can be partly explained by grain refinement. Elemental mapping detected oxygen-enriched Al/Ti regions at interlayer boundaries, which may contribute to the reduced build-direction ductility. Overall, AFSD refined the microstructure, enhanced hardness, and improved traverse-direction strength, while build-direction tensile testing revealed anisotropic mechanical behavior. Full article

Inconel superalloys are employed in demanding components of different equipment. However, they can be exposed to atmospheric corrosion systems, such as marine and industrial environments. This research is focused on studying the localized corrosion susceptibility of Inconel 600, 690 and 718 exposed to H₂SO₄, 1 wt.% and 3.5 wt. % NaCl solutions, simulating marine and industrial atmospheres at 25 ± 0.5 °C. Localized corrosion behavior was characterized by electrochemical noise (EN) and cyclic potentiodynamic polarization (CPP) curves according to ASTM 6-199 ASTM G61 standards. The EN technique was analyzed through time series and analysis for chaotic systems, such as Hurst, Lyapunov and Husdorff coefficients, to determine the corrosion type of each system to reduce the uncertainty in common statistical analysis. The EN results show how Inconel superalloys tend to present localized attacks, being more notorious in NaCl. The application of specialized methods such as Hurst and Lyapunov helped to determine the corrosion system when alloys were characterized by EN. The results indicated that all superalloys exhibit positive hysteresis under CPP, indicating susceptibility to localized pitting corrosion. Full article

Tool wear monitoring is essential for ensuring machining efficiency and product quality, particularly for difficult-to-machine materials such as Inconel 718 (IN718). Traditional deep learning models, such as Conventional Convolutional Neural Networks (CNNs), often struggle to capture complex wear patterns and lack accuracy across varying machining conditions while developing image-based tool wear identification systems. To address these limitations, this paper presents a Vision Transformer (ViT) model for identifying tool-wear categories during end-milling of IN718. The performance of the ViT-based model is systematically compared with a CNN-based EfficientNet-b0 model. The robustness and generalization of the ViT-based model are validated on two previously unseen image datasets: one with conditions similar to those of the training data and another acquired under varying lighting conditions. The results indicate that the ViT model outperforms the EfficientNet-b0 model in terms of classification accuracy and computational efficiency. The ViT model achieves higher accuracy with fewer training epochs and faster convergence. Furthermore, it exhibits strong generalization across different lighting conditions, demonstrating robustness to variations in the machining environment. The findings presented in this work clearly demonstrate ViT’s effectiveness in tool wear classification and its potential as a reliable, efficient algorithm for developing tool wear monitoring systems for practical machining applications. Full article

Wire electrical discharge machining (WEDM) is widely used for the precision cutting of difficult-to-machine materials, including nickel-based superalloys. Wire electrode wear, however, remains a practical limitation, because it affects process stability, wire consumption, and machining cost. This work examines the wear behaviour of a gamma-phase Cu₅Zn₈-coated copper-core wire electrode (Elecut X, ø 0.25 mm) during the WEDM of Inconel 718 using direct gravimetric measurement. A 3³ full factorial experiment was carried out with three electrical parameters: pulse-on time (A), pulse-off time (B), and servo reference voltage (Aj). The discharge process was monitored with an oscilloscope so that measurements only started after the programmed pulse-off time had been reached. Electrode wear was evaluated as the mass loss Δm of 4 m wire segments after 5 min cutting intervals on a Charmilles Robofil 310 machine, and factor significance was assessed by analysis of variance (ANOVA). Pulse-on time was the dominant factor, accounting for 88.45% of the total variation in Δm, followed by servo reference voltage and pulse-off time. SEM/EDS examination showed material transfer from the Inconel 718 workpiece to the worn electrode surface, with local nickel content reaching 16.84 wt.% on the frontal face of the most worn sample. The results provide a quantitative basis for reducing wire consumption during the WEDM of Inconel 718 while recognising the trade-off with cutting productivity. Full article

Hypersonic aircraft represent a cutting-edge technology in aerospace engineering. Coupled heat transfer is a critical physical phenomenon in such aircraft. However, existing studies face challenges in predicting aerothermal behavior. Based on a specific geometric configuration, an axisymmetric model and the ideal gas assumption, this study establishes a numerical simulation model for coupled heat transfer in hypersonic wide-speed-range cruise aircraft. Through numerical simulations, the heat transfer characteristics of the aircraft under Mach numbers of 6, 7, 8 and 9 are analyzed, revealing the evolution of the temperatures at characteristic points and surfaces as the Mach number increases. Additionally, this study analyzes the heat transfer characteristics of metallic materials such as Inconel 718, 17-4PH, 93WNiFe and TA19, revealing differences in thermal protection performance among aircraft made of different materials under hypersonic conditions. Correlation functions relating nose temperature to time and surface temperatures to Mach number are fitted. The results indicate that as the Mach number increases, the aerodynamic heating temperature of the aircraft rises, and the aerodynamic heating effect at the stagnation point becomes more pronounced. Among the materials studied, 17-4PH exhibits the best overall thermal protection performance. This study provides methodological support for thermal prediction of hypersonic aircraft. Full article