Search Results (104)

The applicability of two different joining processes for producing lap joints from high-strength steel sheets is investigated, reflecting their increasing use in advanced lightweight structures with demanding performance requirements. The work is primarily focused on the joining-by-forming process known as double-flush riveting, evaluated in two variants: one utilizing forged holes and the other employing machined holes. The performance of these two variants is compared with conventional fusion-based resistance spot welding using lap joints fabricated from 2 mm high-strength low-alloy S500MC steel sheets under varying geometric and process conditions, with support from finite element modelling. Results indicate that both double-flush riveting variants produce similar joint cross-sectional geometries, but the machined hole variant simplifies sheet preparation and eliminates the need for a progressive tooling system. Tensile lap-shear and peel test results reveal that double-flush riveted joints with forged holes exhibit superior strength, attributed to strain hardening in the forged regions. Furthermore, for nuggets and rivets of equivalent size, both double-flush riveting variants surpass resistance spot welding in terms of the mechanical strength of the final joints. These results suggest that double-flush riveting represents a promising alternative for assembling high-strength steel sheets in lightweight structural applications. Full article

Diamond coatings with three distinct surface textures, namely spherical, pyramidal, and prismatic morphologies, were fabricated using the hot-filament chemical-vapor deposition (HFCVD) method. Scanning electron microscopy (SEM) was employed to analyze the surface morphological characteristics and differences among the coatings. Raman spectroscopic analysis further confirmed that all three diamond films exhibited excellent deposition uniformity and high crystalline quality. A three-dimensional optical microscopy system was used to measure the surface roughness values, which were determined to be Ra 0.423 μm, Ra 0.515 μm, and Ra 0.809 μm, respectively. An HFCVD diamond-coated tool was innovatively employed for the lapping of sapphire wafers, enabling a systematic investigation of the tribological behavior during the lapping process. Based on the experimental results, three morphological material removal models were established. The study demonstrates that the spherical diamond coating achieves a superior surface finish (Ra 0.22 μm) due to its continuous multi-point contact geometry, governed by the agglomerated nanocrystalline structure. Sample 3 had the highest removal rate of 24.3 μm/min. This is related to its surface morphology characteristics and is also due to the two-body contact between the diamond-coated tool and sapphire, offering a high-efficiency alternative for precision machining. Full article

Aiming at the key problems of the material removal rate and surface integrity of existing tools in the lapping of sapphire hard and brittle crystals, an efficient lapping tool has been developed to explore a new process for HFVCD (hot filament chemical vapor deposition) diamond tools to efficiently lap sapphire wafers. With the premise of ensuring the surface roughness of the wafer is Ra ≤ 0.5 μm, the material removal rate is increased to more than 1 μm/h. To explore a high-efficiency lapping process for sapphire wafers using HFCVD diamond tools. The influence of key preparation parameters on the surface characteristics of CVD (chemical vapor deposition) diamond films was systematically investigated. Three types of CVD diamond coating tools with distinct surface morphologies were fabricated. These tools were subsequently employed to conduct lapping experiments on sapphire wafers in order to evaluate their processing performance. The experimental results demonstrate that the gas pressure, methane concentration, and substrate temperature collectively influenced the surface morphology of the diamond coatings. The fabricated coatings exhibited well-defined grain boundaries and displayed pyramidal, prismatic and spherical features, corresponding to high-quality microcrystalline and nanocrystalline diamond layers. In the lapping experiments, the prismatic CVD diamond coating tool exhibited the highest material removal rate, reaching approximately 1.7 μm/min once stabilized. The spherical diamond coating tool produced the lowest surface roughness on the lapped sapphire wafers, with a value of about 0.35 μm. Surface morphology-controllable diamond tools were used for the lapping processing of the sapphire wafers. This achieved a good surface quality and high removal rate and provided new ideas for the precision machining of brittle hard materials in the plane or even in the curved surface. Full article

Additive manufacturing technologies are no longer limited to rapid prototyping but are increasingly used for low-volume production of functional end-use components. Among advanced AM techniques, HP Multi-Jet Fusion (MJF) stands out for its high precision and efficiency. Polyamides, thanks to their balanced mechanical and thermal properties, are commonly used as building materials in this technology. However, these materials are notoriously difficult to bond with conventional adhesives. This study investigates the shear strength of bonded joints made from two frequently used MJF materials—PA12 and glass-bead-filled PA12—using four different industrial adhesives. Experimental procedures were conducted according to ASTM standards. Specimens for single-lap-shear tests were fabricated on an HP MJF 4200 series printer, bonded using a custom jig, and tested on a Zwick-Roell Z250 electro-mechanical testing machine. Surface roughness of the adherends was measured with a 3D optical microscope to assess its influence on bonding performance. The polyurethane-based adhesive (3M Scotch-Weld DP620NS) demonstrated superior performance with maximum shear strengths of 5.0 ± 0.35 MPa for PA12 and 4.4 ± 0.03 MPa for PA12GB, representing 30% and 17% higher strength, respectively, compared to epoxy-based alternatives. The hybrid cyanoacrylate–epoxy adhesive (Loctite HY4090) was the only system showing improved performance with glass-bead-reinforced substrate (16.5% increase from PA12 to PA12GB). Statistical analysis confirmed significant differences between adhesive types (F_3,24 = 31.37, p < 0.001), with adhesive selection accounting for 65.7% of total performance variance. In addition to the experimental work, a finite element-based numerical simulation was performed to analyze the distribution of shear and peel stresses across the adhesive layer using Siemens Simcenter 3D 2406 software with the NX Nastran solver. The numerical results were compared with analytical predictions from the Volkersen and Goland–Reissner models. Full article

Fiber-reinforced polymer (FRP)–concrete bonding is widely adopted for structural strengthening, yet its durability is highly vulnerable to freeze–thaw (FT) degradation. This study combines experimental testing with interpretable machine learning (ML) to reveal the degradation mechanism and predict the interfacial behavior of FRP–concrete systems under FT exposure. Single-lap shear tests showed that all specimens failed through interfacial debonding accompanied by partial concrete peeling. The ultimate bond strength decreased by 6.0–18.5%, and the peak shear stress dropped by 53–80%, indicating a pronounced loss of ductility and adhesion. To extend the analysis, experimental data were integrated with literature datasets, and three ensemble ML algorithms—AdaBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to predict key bond–slip parameters including ultimate bond strength, local maximum bond stress, slip values, and interfacial fracture energy. Among them, XGBoost achieved the highest predictive accuracy, with R² values exceeding 0.94 for most output parameters and consistently low RMSE values. Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) further identified adhesive tensile strength, fiber modulus, FRP thickness, and concrete strength as dominant factors and defined their optimal ranges. The findings offer a scientific foundation for evaluating and predicting the long-term bond durability of FRP–concrete systems and support the development of reliable reinforcement strategies for infrastructure in cold and severe environments. Full article

Reinforced concrete (RC) columns play a vital role in structural integrity, and accurately predicting their failure modes is essential for enhancing seismic safety and performance. This study explores the use of a supervised machine learning approach—specifically, an artificial neural network (ANN) model—to classify failure modes of RC columns. The model is trained using data from the well-established Pacific Earthquake Engineering Research Center (PEER) structural performance database, which contains results from over 400 cyclic lateral-load tests on RC columns. These tests encompass a wide range of column types, including those with spiral or circular hoop confinement, rectangular ties, and varying configurations of longitudinal reinforcement with or without lap splices at critical sections. The ANNs were evaluated using a randomly selected subset from the PEER database, achieving classification accuracies of 94% for rectangular columns and 95% for circular columns. Notably, in certain cases, the model’s predictions aligned with or exceeded the accuracy of traditional building code-based methods. These findings underscore the strong potential of machine learning—particularly ANNs—for reliably postdicting failure modes (even the brittle ones) in RC columns, signaling a promising advancement in the field of earthquake engineering. Full article

As a key component of aero transmission systems, internal splines suffer from problems of low efficiency and poor precision in traditional lapping processes due to geometric deformation and high hardness after heat treatment. To address this, this study proposes an ultrasonic vibration lapping technology, which combines the synergistic mechanism of high-frequency vibration and free abrasive particles to achieve efficient and precise machining of small-sized hardened internal splines. By establishing an abrasive grain impact trajectory model and a rolling abrasive grain material removal model, the mechanisms of micro-cutting and impact removal of abrasive particles under ultrasonic vibration are revealed. Based on the local resonance theory, a longitudinal ultrasonic vibration system is designed, and its resonant frequency is optimized through finite element modal analysis. An ultrasonic lapping experimental platform is built, and heat-treated 9310 internal spline samples are used for experimental verification. The results show that, compared with traditional manual lapping, ultrasonic vibration lapping significantly improves the tooth profile and tooth lead deviations. After measurement, following ultrasonic vibration lapping, both the total tooth profile deviation and tooth lead deviation of the internal spline meet the Grade 6 accuracy requirements specified in GB/T 3478.1-2008 Cylindrical straight-tooth involute splines (Metric Module, Tooth Side Fit)—Part 1: General. This study confirms that ultrasonic vibration lapping can effectively correct the geometric accuracy of tooth surfaces and suppress thermal damage, and provides an innovative solution for the high-quality repair of aero transmission components. Full article

Natural dynamical systems can often display various long-term behaviours, ranging from entirely predictable decaying states to unpredictable, chaotic regimes or, more interestingly, highly correlated and intricate states featuring emergent phenomena. That, of course, imposes a level of generality on the models we use to study them. Among those models, coupled oscillators and cellular automata (CA) present a unique opportunity to advance the understanding of complex temporal behaviours because of their conceptual simplicity but very rich dynamics. In this contribution, we review the work completed by our research team over the last few years in the development and application of an alternative information-based characterization scheme to study the emergent behaviour and information handling of nonlinear systems, specifically Adler-type oscillators under different types of coupling: local phase-dependent (LAP) coupling and Kuramoto-like local (LAK) coupling. We thoroughly studied the long-term dynamics of these systems, identifying several distinct dynamic regimes, ranging from periodic to chaotic and complex. The systems were analysed qualitatively and quantitatively, drawing on entropic measures and information theory. Measures such as entropy density (Shannon entropy rate), effective complexity measure, and Lempel–Ziv complexity/information distance were employed. Our analysis revealed similar patterns and behaviours between these systems and CA, which are computationally capable systems, for some specific rules and regimes. These findings further reinforce the argument around computational capabilities in dynamical systems, as understood by information transmission, storage, and generation measures. Furthermore, the edge of chaos hypothesis (EOC) was verified in coupled oscillators systems for specific regions of parameter space, where a sudden increase in effective complexity measure was observed, indicating enhanced information processing capabilities. Given the potential for exploiting this non-anthropocentric computational power, we propose this alternative information-based characterization scheme as a general framework to identify a dynamical system’s proximity to computationally enhanced states. Furthermore, this study advances the understanding of emergent behaviour in nonlinear systems. It explores the potential for leveraging the features of dynamical systems operating at the edge of chaos by coupling them with computationally capable settings within machine learning frameworks, specifically by using them as reservoirs in Echo State Networks (ESNs) for time series forecasting and predictive modeling. This approach aims to enhance the predictive capacity, particularly that of chaotic systems, by utilising EOC systems’ complex, sensitive dynamics as the ESN reservoir. Full article

Optical materials are widely used in large optical systems such as lithography machines and astronomical telescopes. However, optical materials inevitably produce subsurface damage (SSD) during lapping and polishing processes, degrading the laser damage threshold and impacting the service life of the optical system. The large surface roughness of the lapped optical materials further increases the difficulty of the nondestructive detection of SSD. Quantum dots (QDs) show great development potential in the nondestructive detection of SSD in lapped materials. However, existing QD-based SSD detection methods ignore the polarization sensitivity of QDs to excitation light, which affects the detection accuracy of SSD. To address this problem, this paper explores the fluorescence polarization properties of QDs in the SSD of optical materials. First, the detection principle of SSD based on the fluorescence polarization of QDs is investigated. Subsequently, a fluorescence polarization detection system is developed to analyze the fluorescence polarization properties of QDs in SSD. Finally, the SSD is detected based on the studied polarization properties. The results show that the proposed method effectively improves the detection rate of SSD by 10.8% and thus provides guidance for evaluating the quality of optical material and optimizing optical material processing technologies. The research paradigm is equally applicable to biomedicine, energy, optoelectronics, and the environment, where QDs have a wide range of applications. Full article

The limited build space of additive manufacturing (AM) machines constrains the maximum size of AM components, while manufacturing costs rise with geometric complexity. To enhance value and overcome size limitations, it can be more efficient to join non-AM and AM components to meet the requirements by means of a hybrid structure. Adhesive bonding is particularly suitable for such joints, as it imposes no constraints on the joining surface’s geometry or the adherend’s material. To ensure structural integrity, it is conceivable to exploit the design freedom underlying AM processes by optimizing the topology of the AM component to stress the adhesive layer homogeneously. This study explores the feasibility of this concept using the example of an axially loaded single-lap tubular joint between a carbon fiber-reinforced composite tube and an additively manufactured laser-based powder-bed-fusion aluminum alloy sleeve. The sleeve topology was optimized using the finite element method, achieving a $75 \%\mathrm{P}$ reduction in adhesive stress increase compared to a non-optimized sleeve. Due to the pronounced ductility of the two-component epoxy-based adhesive, the static bond strength remained unaffected, whereas fatigue life significantly improved. The findings demonstrate the feasibility of leveraging AM design freedom to enhance adhesive joint performance, providing a promising approach for hybrid structures in lightweight applications. Full article

This article is devoted to the non-destructive testing of the soldering integrity of soldered lap joints of the current-carrying busbars in electrical machines’ stator windings. The design of a two-element eddy current probe with tangentially positioned coils and active shielding for measuring the soldering integrity of soldered lap joints is developed. This paper considers the method of suppressing the influence of stray parameters on the testing results and the method of correcting the measurements in the case of a deviation in the electrical conductivity of the conductive busbar material. The test results demonstrate the performance of the developed eddy current probe for determining the actual value of the soldering integrity of soldered lap joints in the range of 0–100% with a permissible relative error of no more than 5%. Full article

The chemical–mechanical polishing (CMP) of silicon wafers involves high-precision surface machining after double-sided lapping. Silicon wafers are subjected to chemical corrosion and mechanical removal under pressurized conditions. The multichip CMP process for 4~6-inch silicon wafers, such as those in MOSFETs (Metal Oxide Semiconductor Field Effect Transistors), IGBTs (Insulated-Gate Bipolar Transistors), and MEMS (Micro-Electromechanical System) field materials, is conducted to maintain multiple chips to improve efficiency and improve polish removal uniformity; that is, the detected TTV (total thickness variation) gradually increases from 10 μm to less than 3 μm. In this work, first, a mathematical model for calculating the small deflection of silicon wafers under pressure is established, and the limit values under two boundary conditions of fixed support and simple support are calculated. Moreover, the removal uniformity of the silicon wafers is improved by improving the uniformity of the wax-coated adhesion state and adjusting the boundary conditions to reflect a fixed support state. Then, the stress distribution of the silicon wafers under pressure is simulated, and the calculation methods for measuring the TTV of the silicon wafers and the uniformity measurement index are described. Stress distribution is changed by changing the size of the pressure ring to achieve the purpose of removing uniformity. This study provides a reference for improving the removal uniformity of multichip silicon wafer chemical–mechanical polishing. Full article

Volatile organic compounds (VOCs) are associated with obesity health risks, while the association of mixed VOCs with visceral adiposity indicators remains unclear. In this study, a total of 2015 adults from the National Health and Nutrition Examination Survey (NHANES) were included. Weighted generalized linear models, restricted cubic spline (RCS), weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR) were adopted to assess the association of VOC metabolites (mVOCs) with the visceral adiposity index (VAI) and lipid accumulation product (LAP). Multiple mVOCs were positively associated with the VAI and LAP in the single-exposure model, especially N-acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA) and N-acetyl-S-(N-methylcarbamoyl)-L-cysteine (AMCC). The associations of mVOCs with VAI and LAP were more significant in <60-year-old and non-obese individuals, with interactions of CEMA with age and AMCC with obesity status. Nonlinear relationships between certain mVOCs and the VAI or the LAP were also observed. In the WQS model, co-exposure to mVOCs was positively correlated with the VAI [β (95%CI): 0.084 (0.022, 0.147)]; CEMA (25.24%) was the major contributor. The result of the BKMR revealed a positive trend of the association between mixed mVOCs and the VAI. Our findings suggest that VOC exposure is strongly associated with visceral obesity indicators. Further large prospective investigations are necessary to support our findings. Full article

Globally, fire incidents cause significant social, economic, and environmental destruction, making early detection and rapid response essential for minimizing such devastation. While various traditional machine learning and deep learning techniques have been proposed, their detection performances remain poor, particularly due to low-resolution data and ineffective feature selection methods. Therefore, this study develops a novel framework for accurate fire detection, especially in challenging environments, focusing on two distinct phases: preprocessing and model initializing. In the preprocessing phase, super-resolution is applied to input data using LapSRN to effectively enhance the data quality, aiming to achieve optimal performance. In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. Additionally, we contribute a medium-scale custom fire dataset, comprising high-resolution, imbalanced, and visually similar fire/non-fire images. Moreover, this study conducts an extensive experiment by exploring various pretrained DNN networks with attention modules and compares the proposed network with several state-of-the-art techniques using both a custom dataset and a standard benchmark. The experimental results demonstrate that our network achieved optimal performance in terms of precision, recall, F1-score, and accuracy among different competitive techniques, proving its suitability for real-time deployment compared to edge devices. Full article