Search Results (1,089)

Welding, as a critical process for achieving permanent material joining through localized heating or pressure, is extensively applied in mechanical manufacturing and transportation industries, significantly enhancing the assembly efficiency of complex structures. However, the associated localized high temperatures and rapid cooling often induce uneven thermal expansion and contraction, leading to complex stress evolution and residual stress distributions that compromise dimensional accuracy and structural integrity. In this study, we propose a combined heat source model based on the geometric characteristics of the weld pool to simulate the arc welding process of large flange shafts made of Fe-C-Mn-Cr low-alloy medium carbon steel. Simulations were performed under different welding durations and shaft diameters, and the model was validated through experimental welding tests. The results demonstrate that the proposed model accurately predicts weld pool geometry (depth error of only 2.2%) and temperature field evolution. Meanwhile, experimental and simulated deformations are presented with 95% confidence intervals (95% CI), showing good agreement. Residual stresses were primarily concentrated in the weld and heat-affected zones, exhibiting a typical “increase–steady peak–decrease” distribution along the welding direction. A welding duration of 90 s effectively reduced residual stress differentials perpendicular to the welding direction by 19%, making it more suitable for medium carbon steel components of this scale. The close agreement between simulation and experimental data verifies the model’s reliability and indicates its potential applicability to the welding simulation of other large-scale critical components, thereby providing theoretical support for process optimization. Full article

Passenger cars have unibody constructions, which means that their collision damage often involves key structural components. Successful repair requires the selection of appropriate technology and adherence to quality standards, which directly affects the safety of the vehicle’s continued operation. A commonly used method is a system of replacing damaged components with new ones, while repair by molding and forming is also possible—provided the original structural features are preserved. Automotive body repairs require advanced welding techniques and high precision. Methods such as MIG, TIG, as well as brazing and soldering have replaced older techniques, providing more efficient joining of HSS and HSLA components. Maintaining quality workmanship is crucial, as repair errors can weaken a vehicle’s structure and compromise passenger safety. This article presents the results of a study on the evaluation of the quality, microstructure, and mechanical properties of welded joints of a passenger car frame rail section made of high-strength, low-alloy steel—HSLA 320. The joints were made by three welding methods: MMA, MAG, and TIG, using different technological parameters. Microstructural analysis, non-destructive testing, and microhardness measurements made it possible to assess the impact of the chosen technology on the quality and strength of the joints. The best results were obtained for the TIG method, characterized by the highest repeatability and precision. Full article

Stainless steel welding plays a critical role in industrial manufacturing due to its superior corrosion resistance and structural reliability. The keyhole tungsten inert gas (K-TIG) welding, renowned for its high efficiency, high precision, and cost-effectiveness, demonstrates particular advantages in medium-to-thick plate joining. In order to synergistically leverage the properties of 2205 duplex stainless steel (DSS) and 316L austenitic stainless steel (ASS), we have implemented K-TIG welding with a single variable under control: a constant current and voltage travelling speeds spanning 280–360 mm/min. Defect-free dissimilar joints were consistently achieved within the 280–320 mm/min speed window. The effects of welding speed on microstructural characteristics, mechanical properties, and corrosion behavior of the weld seams were systematically investigated. The percentage of austenite in the weld zone decreases from 84.7% to 59.9% as the welding speed increases. At a welding speed of 280 mm/min, the microstructural features in the regions near the weld seam and fusion zone were investigated. All obtained joints exhibited excellent tensile properties, with their tensile strengths surpassing those of the 316L base metal. The optimal impact toughness of 142 J was achieved at a welding speed of 320 mm/min. The obtained joints exceeded the hardness of TIG joints by 19%. Notably, the grain refinement in the weld zone not only enhanced the hardness of the welded joint but also improved its corrosion resistance. This study provides valuable process references in dissimilar stainless steel K-TIG welding applications. Full article

The growing use of composites in automotive and aerospace fields highlights the need for effective joining of dissimilar materials. Adhesive bonding offers significant advantages over traditional methods. Therefore, comprehensively exploring the relationship between multiple design variables and joint strength, and subsequently achieving accurate prediction of joint strength based on this understanding, is essential for enhancing the effectiveness and efficiency of adhesive joint structural optimization. However, the joint—the critical yet weakest part—has strength governed by complex structural variables that are not fully understood, limiting optimization potential. Based on the effectiveness of finite element simulation in tensile fracture mechanics, this study developed a deep neural network (DNN). Combining the DNN model with a genetic algorithm (GA), both single-objective and multi-objective optimization were conducted. The single-objective optimization focused solely on maximizing joint strength, while the multi-objective GA further quantified the Pareto optimal trade-offs between joint strength and bond area, identifying compromise solutions. The effectiveness of the optimized parameters was validated, demonstrating higher efficiency and accuracy compared to traditional optimization methods such as response surface methodology (RSM). This integrated approach provides a robust framework for predicting joint strength and achieving effective optimization of bonded structures. Full article

Time Division Multiplexing Passive Optical Networks (TDM-PONs) serve as a key enabler for the evolution of broadband access network infrastructure. As TDM-PONs adapt to support 6G networks, reducing energy consumption becomes increasingly critical. Sleep modes have been widely adopted as an effective energy-saving solution. However, their use can introduce delays that compromise performance. This issue becomes especially problematic in 6G PONs, where ultra-low latency and stringent service requirements leave minimal tolerance for delay-related inefficiencies. In this paper, we propose a novel sleep synchronization mechanism for both single and multiple TDM-PONs, allowing Optical Network Units (ONUs) to join one or more sleep/wake-up groups based on the service type and delay tolerance. Our practical design framework incorporates delay-based grouping and existing sleep modes to address the operational complexities of multi-PON systems while remaining fully compatible with current PON standards. The simulation results show that our approach satisfies the requirements of delay-sensitive traffic and achieves up to 37% energy savings. Compared to baseline methods such as adaptive scheduling and fixed-interval cyclic sleep, it offers a 15–20% improvement in the energy–delay trade-off. These results demonstrate the potential for near-term deployment of 6G PONs and lay the foundation for more advanced, delay-aware energy management strategies in next-generation optical access networks. Full article

Aluminium alloys are known for their high strength-to-weight-ratio offering a wide range of applications in the aerospace and automotive industries. However, challenges exist like porosity, oxidation, solidification shrinkage, hot cracking, etc., in joining aluminium alloys. To address these challenges, there is a necessity to understand the process parameters for the welding/joining of aluminium alloys. The present study aims to investigate the effect of cold metal transfer (CMT) welding process parameters (i.e., welding speed and wire feed rate) on mechanical properties for dissimilar AA6061-AA6082 alloys weld joints. Two different welding conditions viz. CMT1 (speed: 0.5 m/min with feed: 5 m/min) and CMT2 (speed: 0.3 m/min with feed: 3 m/min), were considered. The weldments were deployed for testing different mechanical properties such as tensile, impact, hardness, corrosion tests and bead profile geometries. The results reveal that CMT1 has better mechanical properties (tensile_233 MPa; impact_8 J; corrosion rate_0.01368 mm/year) than CMT2, showing the welding speed and wire feed rate play a significant role in the joint performance. The heat affected zone and fusion zone are narrow for CMT1 when compared with CMT2. The present study provides insights into the CMT process and dissimilar joining of aluminium alloys that might be helpful for additive manufacturing of dissimilar aluminium alloys as future research directions. Full article

Individual identification of Holstein cattle is crucial for the intelligent management of farms. The existing closed-set identification models are inadequate for breeding scenarios where new individuals continually join, and they are highly sensitive to obstructions and alterations in the cattle’s appearance, such as back defacement. The current open-set identification methods exhibit low discriminatory stability for new individuals. These limitations significantly hinder the application and promotion of the model. To address these challenges, this paper proposes a prototype network-based incremental identification framework for Holstein cattle to achieve stable identification of new individuals under small sample conditions. Firstly, we design a feature extraction network, ResWTA, which integrates wavelet convolution with a spatial attention mechanism. This design enhances the model’s response to low-level features by adjusting the convolutional receptive field, thereby improving its feature extraction capabilities. Secondly, we construct a few-shot augmented prototype network to bolster the framework’s robustness for incremental identification. Lastly, we systematically evaluate the effects of various loss functions, prototype computation methods, and distance metrics on identification performance. The experimental results indicate that utilizing ResWTA as the feature extraction network achieves a top-1 accuracy of 97.43% and a top-5 accuracy of 99.54%. Furthermore, introducing the few-shot augmented prototype network enhances the top-1 accuracy by 4.77%. When combined with the Triplet loss function and the Manhattan distance metric, the identification accuracy of the framework can reach up to 94.33%. Notably, this combination reduces the incremental learning forgetfulness by 4.89% compared to the baseline model, while improving the average incremental accuracy by 2.4%. The proposed method not only facilitates incremental identification of Holstein cattle but also significantly bolsters the robustness of the identification process, thereby providing effective technical support for intelligent farm management. Full article

The CRISPR/Cas9 genome editing system has emerged as an effective platform to generate loss-of-function gene edits through non-homologous end joining (NHEJ) without a repair template. To verify whether small molecules can enhance the efficiency of CRISPR/ Cas9-mediated NHEJ gene editing in porcine cells, this experiment investigated the effects of six small-molecule compounds, namely Repsox, Zidovudine, IOX1, GSK-J4, YU238259, and GW843682X, on the efficiency of CRISPR/Cas9-mediated NHEJ gene editing. The results showed the optimal concentrations of the small molecules, including Repsox, Zidovudine, IOX1, GSK-J4, YU238259, and GW843682X, for in vitro-cultured PK15 viability. Compared with the control group, the single small molecules Repsox, Zidovudine, GSK-J4, and IOX1 increased the efficiency of NHEJ-mediated gene editing 3.16-fold, 1.17-fold, 1.16-fold, and 1.120-fold, respectively, in the Cas9-sgRNA RNP delivery system. There were no benefits when using YU238259 and GW843682X compared with the control group. In the CRISPR/Cas9 plasmid delivery system, the Repsox, Zidovudine, IOX1, and GSK-J4 treatments increased the efficiency of NHEJ-mediated gene editing 1.47-fold, 1.15-fold, 1.21-fold, and 1.23-fold, respectively, compared with the control group. Repsox can also improve the efficiency of NHEJ-mediated multi-gene editing based on a CRISPR sgRNA-tRNA array. We also explored the mechanism of Repsox’s effect on the efficiency of NHEJ-mediated gene editing. The results showed that Repsox reduces the expression levels of SMAD2, SMAD3, and SMAD4 in the TGF-β pathway, indicating that Repsox can increase the efficiency of CRISPR NHEJ-mediated gene editing in porcine cells through the TGF-β pathway. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

This study investigates the friction stir joining of AA6082-T6 aluminum alloy and Noryl GFN2 polymer in a buttstrap configuration, targeting the development of lightweight cylindrical-shaped structures where the polymer provides thermal, chemical, and electrical insulation, while the aluminum ensures mechanical integrity. A parametric analysis was carried out to assess the ability to produce friction stir buttstrap composite panels in a single processing step and assess the resulting tensile and flexural behavior. To that end, travel and rotating speeds ranging from 2150 to 2250 rpm, and 100 to 140 mm/min, respectively, were employed while keeping plunge depth and the tilt angle constant. A total of nine composite joints were successfully produced and subsequently subjected to both tensile and four-point bending tests. The tensile and flexural strength results ranged from 80 to 139 MPa, and 39 to 47 MPa, respectively. Moreover, the microstructural examination revealed that all joints exhibited a defect within the joining region and its size and shape had a significant effect on tensile strength, whereas the flexural strength was less affected with more uniform results. The joining region was also characterized by a decrease in hardness, particularly in the pin-affected region on the aluminum end of the joint, exhibiting a W-shaped pattern. Contrarily, on the polymeric end of the joining region, no significant change in hardness was observed. Full article

Cannabis sativa L. is a versatile plant with applications in various sectors such as agriculture, medicine, food, and cosmetics. The therapeutic properties of cannabis are often linked to its secondary compounds. The worldwide cannabis market is undergoing swift changes due to varying legal frameworks. Medicinal cannabis (as a heterozygous and dioecious species) is distinct from most annual crops grown in controlled environments, typically propagated through stem cutting rather than seeds to ensure genetic uniformity. Consequently, as with any commercially cultivated crop, biomass yield plays a crucial role in overall productivity. The key factors involved in cultivation conditions, such as successful root establishment, stress tolerance, and the production cycle duration, are critical for safeguarding, improving, and optimizing plant yield. Grafting is a long-established horticultural practice that mechanically joins the scion and rootstock of distinct genetic origins by merging their vascular systems. This approach can mitigate undesirable traits by leveraging the strengths of particular plants, proving beneficial to various applications. Grafting is not used commercially in Cannabis. Only three very recent investigations suggest that grafting holds significant promise for enhancing both the agronomic and medicinal potential of Cannabis. This review critically examines the latest advancements in cannabis grafting and explores prospects for improving biomass (stem, root, flower, etc.) yield and secondary metabolite production. Full article

Initially, the effects of sheet combinations for joining two sheets, including 780 MPa steel and A5052 aluminum alloy sheets, on the joined cross-sectional shapes of the sheets in a clinch-bonding process and the tension-shear load of joined sheets were investigated. The effect of an adhesive on the amounts of the interlock and the minimum thickness in the upper sheet was not large, whereas the effect of the sheet combination was observed. Subsequently, for joining the upper 980 MPa ultra-high-strength steel and lower aluminum alloy sheets in the clinch-bonding process, the effects of the die shape, punch velocity, and sheet holding force on the joinability were investigated. As a result, defect-free conditions were narrowly constrained. Finally, a method that involved controlling material flow using an adhesive with fine particles to increase friction between the sheets was introduced. The upper 980 MPa steel and lower aluminum alloy sheets were successfully joined using this approach. Full article

The anode assembly, as a key component in the electrolytic aluminum process, is composed of steel claws and aluminum guide rods. The connection quality between the steel claws and guide rods directly affects the current conduction efficiency, energy consumption, and operational stability of equipment. Achieving high-quality joining between the aluminum alloy and steel has become a key process in the preparation of the anode assembly. To join the guide rods and steel claws, this work uses Cold Metal Transfer (CMT) technology to clad aluminum on the steel surface and employs machine vision to detect surface forming defects in the cladding layer. The influence of different currents on the interfacial microstructure and mechanical properties of aluminum alloy cladding on the steel surface was investigated. The results show that increasing the cladding current leads to an increase in the width of the fusion line and grain size and the formation of layered Fe₂Al₅ intermetallic compounds (IMCs) at the interface. As the current increases from 90 A to 110 A, the thickness of the Al-Fe IMC layer increases from 1.46 μm to 2.06 μm. When the current reaches 110 A, the thickness of the interfacial brittle phase is the largest, at 2 ± 0.5 μm. The interfacial region where aluminum and steel are fused has the highest hardness, and the tensile strength first increases and then decreases with the current. The highest tensile strength is 120.45 MPa at 100 A. All the fracture surfaces exhibit a brittle fracture. Full article

Breast cancer remains one of the most prevalent malignancies worldwide, and radiation therapy is a central component of its management. However, intrinsic or acquired resistance to radiation significantly compromises therapeutic efficacy. This systematic review aimed to identify and evaluate molecular mechanisms and interventions that influence radiation sensitivity in breast cancer models. A comprehensive PubMed search was conducted using the terms “breast cancer” and “radiation resistance” for studies published between 2002 and 2024. Seventy-nine eligible studies were included. The most frequently investigated mechanisms included the dysregulation of the PI3K/AKT/mTOR and MAPK signaling pathways, enhanced DNA damage repair via non-homologous end joining (NHEJ), and the overexpression of cancer stem cell markers such as CD44⁺/CD24⁻/low and ALDH1. Several studies highlighted the role of non-coding RNAs, particularly the lncRNA DUXAP8 and microRNAs such as miR-21, miR-144, miR-33a, and miR-634, in modulating radiation response. Components of the tumor microenvironment, including cancer-associated fibroblasts and immune regulators, also contributed to radiation resistance. By synthesizing current evidence, this review provides a consolidated resource to guide future mechanistic studies and therapeutic development. This review highlights promising molecular targets and emerging strategies to enhance radiosensitivity and offers a foundation for translational research aimed at improving outcomes in radiation-refractory breast cancer. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article