Search Results (668)

Low-temperature oxidation of titanomagnetite in oceanic basalts distorts the primary thermoremanent magnetization (TRM) signal essential for reconstructing Earth’s magnetic field history, though the specific impact of magma intrusion-induced oxidation on paleointensity preservation remains poorly constrained. This investigation simulates such oxidation processes using a novel experimental design involving isothermal annealing (260 $°$C; 50 µT field; durations 12.5–1300 h) of Red Sea rift basalts (P72/4), employing the Thellier-Coe method to quantify how chemical remanent magnetization (CRM) overprinting affects TRM fidelity under controlled field orientations aligned either parallel or perpendicular to the initial TRM. Results demonstrate two-sloped Arai-Nagata diagrams with reliable TRM preservation below 360 $°$C but significant alteration artifacts above this threshold. Crucially, field orientation during oxidation critically influences accuracy: parallel configurations maintain fidelity (±3% deviation at $Z=0.48$), while perpendicular fields introduce systematic biases (38% overestimation at $Z=0.15$; 20% underestimation at $Z>0.48$), which is attributable to magnetostatic interactions in core-shell grain structures. These findings establish that paleointensity reliability in basalt prone to low-temperature oxidation depends fundamentally on the alignment between oxidation-era magnetic fields and primary TRM direction, necessitating stringent sample selection and directional constraints in marine paleomagnetic research to mitigate CRM-TRM interference. Full article

This study investigates the thermal properties and sterilization efficacy of polylactic acid (PLA) components fabricated via fused deposition modeling (FDM), focusing on PLA’s compatibility with autoclave sterilization protocols. While PLA is extensively recognized for its biobased and biodegradable characteristics, its limited thermal stability has traditionally restricted its application in high-temperature sterilization settings, such as in medical contexts. In our research, we examined three distinct specimen geometries—cylindrical, rectangular, and curved—subjecting them to thermal post-processing through constrained annealing, employing salt or silicone as the embedding medium. Following this process, we exposed the specimens to elevated temperatures, simulating typical sterilization conditions. The outcomes indicated that the annealed PLA specimens exhibited dimensional stability at temperatures exceeding 170 °C, thereby demonstrating their viability for steam sterilization procedures. To translate these findings into practical applications, we selected a small, complex geometrically relevant component, the Easy Bone Collector (EBC) shell, for autoclave testing at 134 °C. Post-sterilization, the part successfully retained its shape and functionality, indicating that, with appropriate thermal conditioning, PLA can be effectively utilized to manufacture cost-efficient, autoclavable components suitable for medical use. These results reveal a promising and sustainable approach to producing reusable, sterilization-compatible PLA devices, particularly in low-volume or single-use applications where biodegradability is advantageous. Full article

The reduced ductility caused by the brittle needle-like α′ martensite limits the application of TC4 alloys produced by selective laser melting (SLM). Appropriate heat treatment can improve the microstructures and properties of SLM-fabricated TC4 alloys. In this work, SLM-fabricated TC4 alloys underwent stress relief annealing at 600 °C and high-temperature annealing at 800 °C. The effects of heat treatment temperature on phase composition, microstructural morphology, grain orientation, and mechanical properties were investigated. Meanwhile, the microstructural evolution and fracture mechanisms during the heat treatment process were analyzed. The results indicate that after annealing at 600 °C, the needle-like α′ phase transforms into elongated α, and nano-β phase increases. When annealed at 800 °C, the α′ phase completely transforms into a more stable lath-shaped α phase and a short rod-shaped β phase, with the nano-β phase disappearing. The texture orientation gradually shifts from <0001> towards <01-10>, where slip systems are more active. Additionally, heat treatment promotes the transition of grain boundaries to high-angle grain boundaries, thereby alleviating stress concentration and enhancing solid-solution strengthening. After heat treatment, the ultimate tensile strength of the material slightly decreases, but the elongation significantly increases. As the annealing temperature increased, the elongation (EL) improved from 5.22% to 11.43%. Following high-temperature annealing at 800 °C, necking and larger dimples appear on the fracture surface, and the fracture mechanism shifts from a mixed brittle–ductile fracture to a ductile fracture. This work provides a theoretical basis for improving the microstructures and properties of SLM-fabricated TC4 alloys through heat treatment. Full article

Metastable titanium alloys have been developed for biomedical use due to their lower elastic modulus, combined with high strength, good ductility, and excellent corrosion resistance. In this study, the electrochemical corrosion resistance of the alternative Ti-23.6Nb-5.1Mo-6.7Zr alloy was investigated. The alloy was initially homogenized at 1000 °C for 24 h and then tested under different processing conditions: 90% cold rolling; 90% cold rolling followed by annealing at 950 °C for 1 h and water quenching; and 90% cold rolling followed by aging at 300 °C, 400 °C, and 500 °C for 4 h each. Electrochemical behavior was assessed using anodic polarization, open circuit potential (OCP), and electrochemical impedance spectroscopy (EIS) tests in a synthetic solution (Ringer’s solution) to simulate body fluid. The obtained results demonstrate the stability of the passive film formed of the conventional and modified alloys, considering long-term use in the human body, regardless of the volumetric fraction and phase distribution across the various processing routes studied as β, α, α″ and ω. The electrochemical parameters, combined with Young’s modulus and hardness of the alternative alloys, enable the definition of a multicriteria selection method of the most suitable mechanical process routes to be used. The application focused on components of functional femoral stems. Full article

This study investigates the subsequent stages of recrystallisation in Interstitial-Free (IF) steel subjected to an unconventional continuous annealing process with a controlled thermal gradient. A cold-rolled steel strip was exposed to varying annealing temperatures along its length, enabling the analysis of microstructural evolution during the course of recrystallisation. The microstructure and stored energy were assessed at various positions along the strip using Electron Backscatter Diffraction (EBSD). The results underscore the significant influence of local misorientation and structural inhomogeneity on orientation selection during recrystallisation. The remaining non-recrystallised volume fraction (NRF) strongly correlates with the average misorientation gradient, obeying a phenomenological power-law correspondence with an exponent of ~3.7. This indicates that the recrystallisation process is highly sensitive to small changes in local orientation gradients. These findings highlight the crucial role of stored energy distribution for texture evolution, particularly during the early stages of recrystallisation in continuous annealing. It is observed that g-fiber grains, in comparison to a-fiber grains, are much more susceptible to grain fragmentation and therefore develop more robust intra-granular misorientation gradients, allowing for successful nucleation events to occur. In the present study, these phenomena are documented in a statistically representative manner. These insights are valuable for optimising thermal processing in interstitial-free (IF) steels. Full article

Urban logistics face complexity due to traffic congestion, fleet heterogeneity, warehouse constraints, and driver workload balancing, especially in the Heterogeneous Multi-Trip Vehicle Routing Problem with Time Windows and Time-Varying Networks (HMTVRPTW-TVN). We develop a mixed-integer linear programming (MILP) model with dual-peak time discretization and exact linearization for heterogeneous fleet coordination. Given the NP-hard nature, we propose a Hyper-Heuristic based on Cumulative Reward Q-Learning (HHCRQL), integrating reinforcement learning with heuristic operators in a Markov Decision Process (MDP). The algorithm dynamically selects operators using a four-dimensional state space and a cumulative reward function combining timestep and fitness. Experiments show that, for small instances, HHCRQL achieves solutions within 3% of Gurobi’s optimum when customer nodes exceed 15, outperforming Large Neighborhood Search (LNS) and LNS with Simulated Annealing (LNSSA) with stable, shorter runtime. For large-scale instances, HHCRQL reduces gaps by up to 9.17% versus Iterated Local Search (ILS), 6.74% versus LNS, and 5.95% versus LNSSA, while maintaining relatively stable runtime. Real-world validation using Shanghai logistics data reduces waiting times by 35.36% and total transportation times by 24.68%, confirming HHCRQL’s effectiveness, robustness, and scalability. Full article

Heat treatment is a critical intermediate process in copper strip manufacturing, where strips go through an air-cushion annealing furnace. The production scheduling for the air-cushion annealing furnace can contribute to cost reduction and efficiency enhancement throughout the overall copper strip production process. The production scheduling problem must account for time window constraints and gas atmosphere transition requirements among jobs, resulting in a complex combinatorial optimization problem that necessitates dual-objective optimization of the total atmosphere transition cost of annealing and the total penalties for time window violations. Most multi-objective optimization algorithms rely on the evolution of a single population, which makes them prone to premature convergence, leading to local optimal solutions and insufficient exploration of the solution space. To address the challenges above effectively, we propose a Bi-population Co-evolutionary Multi-objective Optimization Algorithm (BCMOA). Specifically, the BCMOA initially constructs two independent populations that evolve separately. When the iterative process meets predefined conditions, elite solution sets are extracted from each population for interaction, thereby generating new offspring individuals. Subsequently, these new offspring participate in elite solution selection alongside the parent populations via a non-dominated selection mechanism. The performance of the BCMOA has undergone extensive validation on benchmark datasets. The results show that the BCMOA outperforms its competitive peers in solving the relevant problem, thereby demonstrating significant application potential in industrial scenarios. Full article

The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing in a G2P robotic mobile fulfillment system with multiple picking stations. To model this complex problem, we develop a mathematical formulation and propose a two-phase heuristic algorithm that combines simulated annealing, genetic algorithms, and beam search for efficient solution. In addition, we explore and compare two order allocation strategies—order similarity and order association—across a range of operational scenarios. Extensive computational experiments and sensitivity analyses demonstrate the effectiveness of the proposed approach and provide insights into how strategic order allocation can significantly improve picking efficiency. Computational experiments on small-scale instances show that our algorithm achieves near-optimal solutions with up to 93.3% reduction in computation time compared to exact optimization for small cases. In large-scale scenarios, the order similarity strategy reduces rack movements by up to 44.8% and the order association strategy by up to 33.5% relative to a first-come, first-served baseline. Sensitivity analysis reveals that the association strategy performs best with fewer picking stations and lower rack capacity, whereas the similarity strategy is superior in systems with more stations or higher rack capacity. The findings offer practical guidance for the design and operation of intelligent warehousing systems. Full article

Amorphous indium gallium zinc oxide (a-IGZO) is widely used as an oxide semiconductor in the electronics industry due to its low leakage current and high field-effect mobility. However, a-IGZO suffers from notable limitations, including crystallization at temperatures above 600 °C and the high cost of indium. To address these issues, nitrogen-doped zinc oxynitride (ZnON), which can be processed at room temperature, has been proposed. Nitrogen in ZnON effectively reduces oxygen vacancies (V_O), resulting in enhanced field-effect mobility and improved stability under positive bias stress (PBS) compared to IGZO. In this study, selective deep ultraviolet femtosecond (DUV fs) laser annealing was applied to the channel region of ZnON thin-film transistors (TFTs), enabling rapid threshold voltage (Vth) modulation within microseconds, without the need for vacuum processing. Based on the electrical characteristics of both Vth-modulated and pristine ZnON TFTs, an NMOS inverter was fabricated, demonstrating reliable performance. These results suggest that laser annealing is a promising technique, applicable to various logic circuits and electronic devices. Full article

Compared to rigidly-backed absorbers, the selection of appropriate optimization techniques for the optimal design of broadband acoustic mufflers remains under-investigated. This study determines the most effective optimization strategy for maximizing the total dissipation of rainbow-trapping silencers (RTSs), composed of graded side-branch cavities that enable broadband dissipation of sound through visco-thermal effects. Model-based and data-driven optimization strategies are compared, particularly in high-dimensional design spaces with flat cost function landscapes where gradient-based approaches are inadequate. It is found that model-based particle swarm optimization (PSO) outperforms simulated annealing, genetic algorithm, and surrogate method in maximizing RTS total dissipation, especially in high-dimensional designs. PSO uniquely handles flat or valleyed cost landscapes through efficient exploration–exploitation trade-offs. Data-driven approaches using Bayesian regularization neural networks (BRNNs) drastically reduce computational cost in high-dimensional spaces, though they require large datasets to avoid over-smoothing. In low dimensions, direct optimization on BRNN outputs suffices, making global search unnecessary. Both model-based and BRNN methods show robustness to input errors, but data-driven approaches handle output noise better. These findings, validated using transfer matrix models, offer strategic guidance for selecting optimization methods, especially when using computationally expensive visco-thermal finite element simulations. Full article

Snow plays a crucial role in global climate regulation, hydrological processes, and environmental change, making the accurate acquisition of snow depth data highly significant. In this study, we used Sentinel-1 radar data and employed a simulated annealing algorithm to select the optimal influencing factors from radar backscatter characteristics and spatiotemporal geographical parameters within the study area. Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. The results indicate that the RF model achieves better agreement with the measured data than existing snow depth products. Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R² of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. In contrast, the SVM regression model shows weaker agreement with the observations, with an R² lower than that of the existing snow depth product (0.51) in Xinjiang, and it performs poorly in other regions as well. Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques. Full article

Selective laser melting (SLM) offers a novel approach for manufacturing intricate structures, broadening the application of titanium alloy parts in the aerospace industry. After the build period, heat treatments of annealing (AT) and hot isostatic pressing (HIP) are often implemented, but a comparison of their mechanical performances based on the specimen orientation is still lacking. In this study, horizontally and vertically built Ti6Al4V SLM specimens that underwent the aforementioned treatments, together with their microstructural and defect characteristics, were, respectively, investigated using metallography and X-ray imaging. The mechanical properties and failure mechanism, via fracture analysis, were obtained. The critical factors influencing the mechanical properties and the correlation of the fatigue lives and failure origins were also estimated. The results demonstrate that the mechanical performances were determined by the α-phase morphology and defects, which included micropores and fewer large lack-of-fusion defects. Following the coarsening of the α phase, the strength decreased while the plasticity remained stable. With the discrepancy in the defect occurrence, anisotropy and scatter of the mechanical performances were introduced, which was significantly alleviated with HIP treatment. The fatigue failure origins were governed by defects and the α colony, which was composed of parallel α phases. Approximately linear relationships correlating fatigue lives with the X-parameter and maximum stress amplitude were, respectively, established in the AT and HIP states. The results provide an understanding of the technological significance of the evaluation of mechanical properties. Full article

This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios. Full article

In this study, the influence of annealing on the phase evolution and mechanical properties of the Fe₆₂Ni₁₈P₁₃C₇ (at.%) alloy was investigated. Ribbons produced via melt-spinning were annealed at various temperatures, and their structural transformations and hardness were evaluated. The alloy exhibited a narrow supercooled liquid region (ΔT_x ≈ 22 °C), confirming its low glass-forming ability (GFA). Primary crystallization began at approximately 380 °C with the formation of α-(Fe,Ni) and Fe₂NiP, followed by the emergence of γ-(Fe,Ni) phase at higher temperatures. A significant increase in hardness was observed after annealing up to 415 °C, primarily due to nanocrystallization and phosphide precipitation. Further heating resulted in a hardness plateau, followed by a noticeable decline. Additionally, samples were produced via selective laser melting (SLM). The microstructure of the SLM-processed material revealed extensive cracking and the coexistence of phosphorus-rich regions corresponding to Fe₂NiP and iron-rich regions associated with γ-(Fe,Ni). Full article

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article