We investigated the surface structure of the YCoCrAl alloy, which exhibits half-metallic (HM) behaviour, using first-principles density functional theory (DFT) calculations. We evaluated the stability, electronic, and magnetic properties of both the bulk and its (0 0 1) surface. For the bulk, the spin magnetic moment (SMM) was 3.00 μβ according to the Slater-Pauling method, with complete spin polarisation (SP) observed. We calculated the energy gap (Eg) using the generalised gradient approximation (GGA) to be 0.35 eV, supporting HM behaviour and indicating mechanical stability and ductility, as confirmed by further calculations. We investigated both (0 0 1) surface terminations. We found that the Y-Al termination retained HM properties, exhibiting full SP and an integer magnetic moment. The Co-Cr termination displayed a non-integer magnetic moment, unlike the bulk. We also calculated the work function and surface energy, as these parameters are essential for predicting the behaviour of various magnetic devices. Additionally, the optical properties of both the bulk and Y-Al-terminated surfaces suggest use in absorbing and reflective coatings and spin-optoelectronic devices.
Aluminum matrix composites reinforced with solid lubricants have attracted considerable attention for tribological applications owing to their potential to combine high specific strength with improved friction and wear behavior. In the present work, Al-5 wt.% MoS 2 composite sheets and monolithic aluminum sheets were successfully fabricated through the accumulative roll bonding (ARB) process up to eight passes. The influence of ARB passes on the microstructure, crystallite size, hardness, tensile properties, friction coefficient, and wear behavior of both materials was systematically investigated. Microstructural characterization revealed a significant grain refinement and homogeneous distribution of MoS 2 within the aluminum matrix after repeated ARB processing. The crystallite size decreased progressively with increasing ARB passes, leading to an increase in hardness and tensile strength for both materials. The Al-5 wt.% MoS 2 composite exhibited higher hardness and strength than monolithic aluminum at all processing conditions due to the combined effects of grain refinement and reinforcement strengthening. Tribological investigations showed that the incorporation of MoS. reduced the coefficient of friction compared with monolithic aluminum. However, the wear rate of both the composite and monolithic materials increased with increasing ARB passes. Examination of the worn surfaces indicated that delamination and subsurface crack propagation were the dominant wear mechanisms, particularly after higher ARB passes, where the heavily refined laminated structure promoted layer separation during sliding. The results demonstrate that ARB processing is an effective route for enhancing the mechanical performance of Al/MoS 2 composites, although the associated microstructural evolution significantly influences their wear behavior.
This work comprehensively assesses the machinability of basalt/flax fiber laminates that were compressed-molded with titanium and then cut via abrasive aqua-jet cutting (AAJC). The focus is on four key AAJC parameters, including abrasive pressure, cutting speed, distance from nozzle and rate of abrasion, to assess how each parameter affects the cut quality and also seeks to develop improved machine learning prediction capabilities for increased precision in machining. An ensemble machine learning (EML) model, integrating Ridge Regression (RR), Random Forest (RF), XGBoost (XGB), and Neural Networks (NN), was constructed and evaluated against Response Surface Methodology (RSM). ANOVA demonstrated that [Formula: see text] was the dominant parameter, contributing 70.0% and 71.4% of the observed variation in SR and material removal rate (MRR), respectively ([Formula: see text]). The developed quadratic RSM models achieved [Formula: see text] values of 0.9568 for MRR and 0.9763 for SR, with predicted [Formula: see text] values of 0.9164 and 0.9433, respectively. The EML models achieved [Formula: see text] values of 0.9769 for SR and 0.9810 for MRR. RSM optimization reduced SR from 2.65[Formula: see text][Formula: see text]m to 2.15[Formula: see text][Formula: see text]m and increased MRR from 144.18[Formula: see text]mm 3 /s to 149.92[Formula: see text]mm 3 /s, with prediction errors of only 0.93% and 0.94%, respectively. The experimentally validated EML Pareto solution achieved 3.69[Formula: see text][Formula: see text]m SR and 151.25[Formula: see text]mm 3 /s MRR, closely matching predictions of 3.65[Formula: see text][Formula: see text]m and 150.13[Formula: see text]mm 3 /s, with errors of 1.08% and 0.74%. Scanning Electron Microscopy, Atomic Force Microscope Images and 3D Surface Topography were used to evaluate the microstructure and surface of the Hybrid Titanium alloy fiber laminates (TiAFL) in order to show that different morphologies of the hybrid TiAFL are formed as a result of erosion processes. The results of this investigation provide an advanced machinability understanding of Hybrid TiAFL and deliver an ensemble-based predictive model for optimizing machining parameters on an industrial scale with improved performance.
Hydrogen storage and transport systems use pipeline steel, where hydrogen embrittlement accelerates fatigue crack propagation and reduces service life. Accurate life evaluation is necessary for safe operation. Conventional approaches, such as experimental fatigue testing, finite-element simulations, and empirical models like the Paris law, are limited by high experimental costs, dependence on often-unavailable hydrogen constitutive laws, and deterministic predictions that do not account for uncertainty.This paper offers a physics-informed neural network (PINN) and ensemble learning framework. The method integrates physical and data-driven modeling using Bayesian model averaging (BMA) with a probabilistic neural network (PNN). Validation on API-X70 and X100 steels, BMA-PINN achieves a log mean squared error (MSE) of 0.0538 and [Formula: see text] of 0.9934 for X100, outperforming other models. PINN captures fatigue life stochasticity with 95th-percentile intervals.
A set of glass-ceramics was produced using a ternary mixture of B 2 O 3 , granite-sludge waste, and different amounts of Bi 2 O 3 . The samples were prepared through controlled melting and heat treatment guided by DTA. XRD confirmed that all parent glasses were completely amorphous. Heating at 550 °C triggered the first crystallization stage, forming orthorhombic and tetragonal SiO 2 . At higher Bi 2 O 3 levels, a monoclinic bismite phase also appeared. Increasing the temperature to 600 °C produced the same phases but with sharper and stronger peaks, indicating higher crystallinity. HRSEM images showed hexagonal plates in the undoped sample, while 5 wt% Bi 2 O 3 produced petal-like crystals. EDX verified the elemental composition of the detected phases. Photoluminescence revealed a blue emission at 435 nm, linked to Bi3 3+ centers or defect-related sites. Microhardness decreased with Bi 2 O 3 addition, but the glass-ceramics remained harder than the parent glasses due to crystal formation. These findings demonstrate an effective method for converting granite waste into functional glass-ceramics suitable for structural and optical applications.
