Search Results (147)

This study investigates the wear performance of 20CrMnTi steel, a commonly used material for spiral bevel gears, after heat treatment, with a focus on the microstructural evolution and wear behavior in both the surface and gradient direction of the carburized layer. The results show that the microstructure composition in the gradient direction of the carburized layer gradually transitions from martensite and residual austenite to a martensite–bainite mixed structure, and eventually transforms to fully bainitic in the matrix. With the extension of carburizing time, both the effective carburized layer depth and the hardened layer depth significantly increase. Wear track morphology analysis reveals that the wear track depth gradually becomes shallower and narrower, and the wear rate increases significantly with increasing load. However, the friction coefficient shows little sensitivity to changes in carburizing time and load. Further investigations show that as the carburized layer depth increases, the carbon concentration and hardness of the samples gradually decrease, resulting in an increase in the average wear rate and a progressive worsening of wear severity. After the wear tests, different depths of plowing grooves, spalling, and fish-scale-like features were observed in the wear regions. Additionally, with the increase in load and carburized layer depth, both the width and depth of the wear tracks significantly increased. The research results provide a theoretical basis for optimizing the surface carburizing process of 20CrMnTi steel and improving its wear resistance. Full article

Al-Mg-Si (6XXX) series aluminum alloys are widely applied in aerospace and transportation industries. However, exploring how varying compositions affect alloy properties and deformation mechanisms is often time-consuming and labor-intensive due to the complexity of the multicomponent composition space and the diversity of processing and heat treatments. This study, inspired by the Materials Genome Initiative, employs high-throughput experimentation—specifically the kinetic diffusion multiple (KDM) method—to systematically investigate how the pop-in effect, indentation size effect (ISE), and creep behavior vary with the composition of Al-Mg-Si alloys at room temperature. To this end, a 6016/Al-3Si/Al-1.2Mg/Al KDM material was designed and fabricated. After diffusion annealing at 530 °C for 72 h, two junction areas were formed with compositional and microstructural gradients extending over more than one thousand micrometers. Subsequent solution treatment (530 °C for 30 min) and artificial aging (185 °C for 20 min) were applied to simulate industrial processing conditions. Comprehensive characterization using electron probe microanalysis (EPMA), nanoindentation with continuous stiffness measurement (CSM), and nanoindentation creep tests across these gradient regions revealed key insights. The results show that increasing Mg and Si content progressively suppresses the pop-in effect. When the alloy composition exceeds 1.0 wt.%, the pop-in events are nearly eliminated due to strong interactions between solute atoms and mobile dislocations. In addition, adjustments in the ISE enabled rapid evaluation of the strengthening contributions from Mg and Si in the microscale compositional array, demonstrating that the optimum strengthening occurs when the Mg-to-Si atomic ratio is approximately 1 under a fixed total alloy content. Furthermore, analysis of the creep stress exponent and activation volume indicated that dislocation motion is the dominant creep mechanism. Overall, this enhanced KDM method proves to be an effective conceptual tool for accelerating the study of composition–deformation relationships in Al-Mg-Si alloys. Full article

This study investigates the mechanical, tribological, and electrochemical behaviour of a novel precipitation-hardenable martensitic alloy produced by selective laser melting (SLM). The alloy was specifically engineered with an optimised composition, free from cobalt and molybdenum, and featuring reduced nickel content (7 wt.%) and 8 wt.% chromium. It has been developed as a cost-effective and sustainable alternative to conventional maraging steels, while maintaining high mechanical strength and a refined microstructure tailored to the steep thermal gradients inherent to the SLM process. Several ageing heat treatments were assessed to evaluate their influence on microstructure, hardness, tensile strength, retained austenite content, dislocation density, as well as wear behaviour (pin-on-disc test) and corrosion resistance (polarisation curves in 3.5%NaCl). The results indicate that ageing at 540 °C for 2 h offers an optimal combination of hardness (550–560 HV), tensile strength (~1700 MPa), microstructural stability, and wear resistance, with a 90% improvement compared to the as-built condition. In contrast, ageing at 600 °C for 1 h enhances ductility and corrosion resistance (Rp = 462.2 kΩ; Ecorr = –111.8 mV), at the expense of a higher fraction of reverted austenite (~34%) and reduced hardness (450 HV). This study demonstrates that the mechanical, surface, and electrochemical performance of this novel SLM-produced alloy can be effectively tailored through controlled thermal treatments, offering promising opportunities for demanding applications requiring a customised balance of strength, durability, and corrosion behaviour. Full article

This study systematically investigates the homogenization-induced Laves phase dissolution kinetics and recrystallization mechanisms in laser powder bed fusion (L-PBF) processed IN718 superalloy. The as-built material exhibits a characteristic fine dendritic microstructure with interdendritic Laves phase segregation and high dislocation density, featuring directional sub-grain boundaries aligned with the build direction. Laves phase dissolution demonstrates dual-stage kinetics: initial rapid dissolution (0–15 min) governed by bulk atomic diffusion, followed by interface reaction-controlled deceleration (15–60 min) after 1 h at 1150 °C. Complete dissolution of the Laves phase is achieved after 3.7 h at 1150 °C. Recrystallization initiates preferentially at serrated grain boundaries through boundary bulging mechanisms, driven by localized orientation gradients and stored energy differentials. Grain growth kinetics obey a fourth-power time dependence, confirming Ostwald ripening-controlled boundary migration via grain boundary diffusion. Such a study is expected to be helpful in understanding the microstructural development of L-PBF-built IN718 under heat treatments. Full article

The growing complexity of modern manufacturing has intensified the need for precise control of residual stresses to ensure structural reliability, dimensional stability, and material performance. This study conducts a bibliometric review using data from Scopus and Web of Science, covering publications from 2019 to 2024. Residual stress research in metal manufacturing has gained prominence, particularly in relation to welding, additive manufacturing, and machining—processes that induce significant stress gradients affecting mechanical behavior and service life. Emerging trends focus on simulation-based prediction methods, such as the finite element method, heat treatment optimization, and stress-induced defect prevention. Key thematic clusters include process-induced microstructural changes, mechanical property enhancement, and the integration of modeling with experimental validation. By analyzing the evolution of research output, global collaboration networks, and process-specific contributions, this review provides a comprehensive overview of current challenges and identifies strategic directions for future research in residual stress management in advanced metal manufacturing. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

This study investigates the influence of process parameters on the fabrication and mechanical performance of Scalmalloy^® lattice structures produced via laser powder bed fusion (PBF-LB) and their mechanical responses at different cell size. A full-factorial design of experiments was employed to evaluate the effect of scan speed, hatch distance, and downskin power on internal porosity and dimensional accuracy. Regression models revealed significant relationships, with optimised parameters identified at a scan speed of 700 mm/s, hatch distance of 0.13 mm, and downskin power of 80 W. Mechanical characterisation through tensile tests of bulk samples and compression tests of lattice structures highlighted the strengthening effects of the heat treatment. Experimental data on quasi-elastic gradient and yield strength were compared to predictions from the Ashby–Gibson model, revealing a partial agreement but noticeable deviations attributed to cell geometry and manufacturing defects. The scaling laws observed differed from the classical model, particularly in the yield strength exponent, indicating the need for empirical models tailored to metallic lattices. This work provides key insights into the optimisation of PBF-LB parameters for Scalmalloy^® and underlines the complex interplay between process parameters, structural design, and mechanical behaviour. Full article

The prediction of microstructure evolution during thermal processing plays a crucial role in tailoring the mechanical properties of metallic components. Therefore, this work presents a comprehensive, multiscale modelling approach to simulating phase transformations in large-diameter steel rings during cooling. A finite-element-based thermal model was first used to simulate transient temperature distributions in a large-diameter ring under different cooling conditions, including air and water quenching. These thermal histories were subsequently employed in two complementary phase transformation models of different levels of complexity. The Avrami model provides a first-order approximation of the evolution of phase volume fractions, while a complex full-field cellular automata approach explicitly simulates the nucleation and growth of ferrite grains at the microstructural level, incorporating local kinetics and microstructural heterogeneities. The results highlight the sensitivity of final grain morphology to local cooling rates within the ring and initial austenite grain sizes. Simulations demonstrated the formation of heterogeneous microstructures, particularly pronounced in the ring’s surface region, due to sharp thermal gradients. This approach offers valuable insights for optimising heat treatment conditions to obtain high-quality large-diameter ring products. Full article

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article

Welding of maraging steels leads to a microstructural gradient from base material (BM) to weld metal (WM). During post-weld heat treatment (PWHT) the precipitation and reverted austenite (γ_r) reactions will occur defining the mechanical properties. These reactions are affected by the microstructure and local chemical composition of each zone in the “as welded” (AW) condition. This effect has not been clearly described yet nor the evolution of the microstructure. The objective of this work was to analyse the phase transformations at the different zones of the welded joint during the PWHT to explain the microstructure obtained at each zone. Samples of C250 maraging steel were butt-welded by GTAW-P (Gas Tungsten Arc Welding—Pulsed) process without filler material. The AW condition showed an inhomogeneous microhardness profile, associated with a partial precipitation hardening in the subcritical heat affected zone (SC-HAZ) followed by a softening in the intercritical (IC-HAZ) and recrystallized heat affected zone (R-HAZ). A loop-shaped phase was observed between low temperature IC-HAZ and SC-HAZ, associated with γ_r, as well as microsegregation at the weld metal (WM). The microstructural evolution during PWHT (480 °C) was evaluated on samples treated to different times (1–360 min). Microhardness profile along the welded joint was mostly homogeneous after 5 min of PWHT due to precipitation reaction. The microhardness in the WM was lower than in the rest of the joint due to the depletion of Ni, Ti and Mo in the martensite matrix related with the γ_r formation. The isothermal kinetics of precipitation reaction at 480 °C was studied using Differential Scanning Calorimetry (DSC), obtaining a JMAK expression. The average microhardness for each weld zone was proposed for monitoring the precipitation during PWHT, showing a different behaviour for the WM. γ_r in the WM was also quantified and modelled, while in the IC-HAZ tends to increase with PWHT time, affecting the microhardness. Full article

Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study addresses fully automatic orthodontics tracing based on the usage of artificial intelligence (AI) applied to 2D and 3D images, by designing a cephalometric system that analyzes the significant landmarks and regions of interest (ROI) needed in orthodontics tracing, especially for the mandible and maxilla teeth. In this research, a computerized system is developed to automate the tasks of orthodontics evaluation during 2D and Cone-Beam Computed Tomography (CBCT or 3D) systems measurements. This work was tested on a dataset that contains images of males and females obtained from dental hospitals with patient-informed consent. The dataset consists of 2D lateral cephalometric, panorama and CBCT radiographs. Many scenarios were applied to test the proposed system in landmark prediction and detection. Moreover, this study integrates the Grad-CAM (Gradient-Weighted Class Activation Mapping) technique to generate heat maps, providing transparent visualization of the regions the model focuses on during its decision-making process. By enhancing the interpretability of deep learning predictions, Grad-CAM strengthens clinical confidence in the system’s outputs, ensuring that ROI detection aligns with orthodontic diagnostic standards. This explainability is crucial in medical AI applications, where understanding model behavior is as important as achieving high accuracy. The experimental results achieved an accuracy exceeding 98.9%. This research evaluates and differentiates between the two-dimensional and the three-dimensional tracing analyses applied to measurements based on the practices of the European Board of Orthodontics. The results demonstrate the proposed methodology’s robustness when applied to cephalometric images. Furthermore, the evaluation of 3D analysis usage provides a clear understanding of the significance of integrated deep-learning techniques in orthodontics. Full article

Stress shielding remains a major concern in cementless total hip arthroplasty (THA) due to the stiffness mismatch between femoral stems and surrounding bone. This study investigated the biomechanical and clinical performance of a novel Ti-33.6Nb-4Sn (Ti-Nb-Sn) alloy stem with a graded Young’s modulus achieved through localized heat treatment. A finite element model (FEM) of the Ti-Nb-Sn stem, incorporating experimentally validated Young’s modulus gradients, was constructed and implanted into a patient-specific femoral model. Stress distribution and micromotion were assessed under physiological loading conditions. Clinical validation was performed by evaluating radiographic outcomes at 1 and 3 years postoperatively in 40 patients who underwent THA using the Ti-Nb-Sn stem. FEM analysis showed low micromotion at the proximal press-fit region (4.89 μm rotational and 11.74 μm longitudinal), well below the threshold for osseointegration and loosening. Stress distribution was concentrated in the proximal region, effectively reducing stress shielding distally. Clinical results demonstrated minimal stress shielding, with no cases exceeding Grade 3 according to Engh’s classification. The Ti-Nb-Sn stem with a gradient Young’s modulus provided biomechanical behavior closely resembling in vivo conditions and showed promising clinical results in minimizing stress shielding. These findings support the clinical potential of modulus-graded Ti-Nb-Sn stems for improving implant stability in THA. Full article

Anisotropic microstructures and wear resistance are caused by large thermal gradients during selective laser melting (SLM). Investigating the wear resistance in different planes of SLM specimens is crucial. Hence, the effect of heat treatment on the anisotropy of the microstructure, density, microhardness, and wear resistance of SLM 316L stainless steel was studied. Specimens subjected to solution + aging treatment exhibited γ austenite and α ferrite phases with lower microstrain, as determined via X-ray diffraction (XRD) analysis. Microstructure observations demonstrated that SLM 316L appears as intersecting melt pools on the XOY plane and fish scale-like melt pools on the XOZ plane. After heat treatment, the melt boundaries disappeared, carbides (M₂₃C₆) precipitated at grain boundaries and within the grains, and the microstructures coarsened and became more uniform. The microhardness and wear resistance of the XOY plane were shown to be superior to those of the XOZ plane, and the microhardness decreased following heat treatment. Compared with SLM 316L, the microhardness of the XOY and XOZ planes of the specimen subjected to solution + aging treatment decreased by 5.96% and 4.98%. The friction and wear test results revealed that the specimen after solution + aging treatment had the lowest friction coefficient and the smallest wear rate. The wear rates of specimens from the XOY and XOZ planes after solution + aging treatment were 21.1% and 27.1% lower than that of SLM 316L, exhibiting the best wear resistance. Full article

Natural pigments like betacyanins are highly unstable under heat, light, acid, and alkaline conditions. Yeast protein (YP) is a promising substitute protein, while chitooligosaccharides (COS) are water-soluble alkaline polysaccharides. Water-in-oil-in-water (W₁/O/W₂) emulsions, with two-membrane, three-phase structure, can serve as effective carriers for stabilizing pigments. In this study, YP-COS complexes formed through electrostatic interactions were used as hydrophilic emulsifiers to create betacyanin-coated W₁/O/W₂ emulsions. The W₁/O colostrum was designed to make up 30%, 70%, and 90% of the emulsion (v/v)and the W₂ was designed by the complexes with three concentrations of YP (2%, 1.25% and 0.5%, w/v)-COS (6%, 3.75% and 1.5%, w/v). The optimal formulation was determined through comprehensive evaluation of micromorphological characteristics, particle size, zeta potential and creaming index, ultimately yielding a system comprising YP (2%)-COS (6%) and 90% W₁/O colostrum. Moreover, the W₁/O/W₂ emulsion system significantly improved the betacyanins retention under thermal treatment, photolytic exposure, pH gradients, and extended storage compared to the betacyanin aqueous solution (p < 0.05). In vitro digestion tests showed the emulsion retained 58.39% of betacyanins, while the betacyanin aqueous solution retained only 41.42%, demonstrating the emulsion’s ability to delay the betacyanins release, offering new insights for using YP-COS complexes in food production and other fields. Full article

In the present work, the effect of different casting processes on the microstructure and creep properties of the second-generation single-crystal superalloy DD419 was investigated. Under conventional production conditions and a contour-suited thermal insulation method, single-crystal rods of types A and B were fabricated, respectively. In comparison to rod type A, the solidification process of rod type B featured a 1.6-fold increase in the temperature gradient and a 32% reduction in primary dendrite spacing. The γ/γ′ eutectic in the as-cast microstructure, the residual eutectic phase, and porosity after heat treatment were also significantly reduced, resulting in the improved homogeneity of the single crystal castings. Under the testing conditions of 850 °C/650 MPa and 1050 °C/190 MPa, the stress rupture life of sample B was enhanced by 25% and 5.2%, respectively, compared to sample A. Therefore, due to dendrite structure refinement, the stress rupture life of the superalloy was evidently improved, especially at medium temperatures. Full article