Search Results (62)

This study addresses the urgent demand for high-precision manufacturing of curved components by developing a fully servo-driven multi-axis controlled four-roll bending machine. By integrating a modular symmetric roller system design with a distributed hierarchical motion control architecture, we achieved substantial enhancements in scalability, forming stability, and machining accuracy. The mechanical system underwent static simulation optimization using SolidWorks Simulation, ensuring maximum stress in the guiding mechanism was controlled below $7.118\times {10}^3$ N/m². ABAQUS-based roll-bending dynamic simulations validated the geometric adaptability and process feasibility of the proposed mechanical configuration. A master-slave dual-core control architecture was implemented in the control system, enabling synchronized error ≤ 0.05 mm, dynamic response time ≤ 10 ms, and positioning accuracy of ±0.01 mm through collaborative control of the master controller and servo drives. Experimental validation demonstrated that the machine achieves bending errors within 1%, with an average forming error of 0.798% across various radii profiles. The arc integrity significantly outperforms conventional equipment, while residual straight edge length was reduced by 86.67%. By adopting fully servo-electric cylinder actuation and integrating a C#-developed human–machine interface with real-time feedback control, this research effectively enhances roll-bending precision, minimizes residual straight edges, and exhibits broad industrial applicability. Full article

Understanding the complex phenomena of interactions between the elements of a combined piled raft foundation (CPRF) is essential for the proper design of such foundations. To evaluate the effects of mutual influence among the CPRF’s elements, a series of long-term measurements of selected physical quantities related to the performance of the foundation were conducted on a building with a frame structure, stiffening walls, and monolithic technology, consisting of seven aboveground stories and one underground story. The analysis distinguishes the real deformations resulting from temperature changes and from stress strains resulting from load changes. The two types of deformations were subjected to further interpretation of only changes in the stress and strain over time. Changes in stress values in the subsoil, as well as strain measurements in the vertical direction of concrete columns, were recorded to assess the load distribution between the CPRF’s components. The numerical analysis results obtained for a fragment of the monitored foundation were compared with actual measurement results to verify the numerical model of interaction between the structure and the soil. Field monitoring and FEA methods were used to compare the long-term deformation analysis, and they helped to minimize the monitoring time. This comparison also served to supplement and simultaneously expand the dataset of test results on a real-world scale. Full article

Expected lives of mechanical parts and structures depend upon the environmental conditions, their dynamic behaviours and the task-oriented spectra of different loadings. This paper exploits contactless full-field mobilities, estimated by Scanner Laser Doppler Vibrometry (SLDV), in the real manufacturing, assembling and loading conditions of the thin plate tested, whose structural dynamics can be described in broad frequency bands, with no distorting inertia of sensors and no numerical models. The paper derives the mobilities into full-field strain Frequency Response Functions (FRFs), which map, by selecting the proper complex-valued broad frequency band excitation spectrum, the surface strains. From the latter, by means of the constitutive model, dynamic stress distributions are computed, to be exploited in fatigue spectral methods to map the expected life of the component, according to the selected tasks’ spectra and the excitation locations. The results of this experiment-based approach are thoroughly commented in sight of non-destructive-testing, damage and failure prognosis, Structural Health Monitoring, manufacturing and maintenance actions. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

In laser-directed energy deposition (LDED) additive manufacturing, stress-induced deformation and cracking often occur unexpectedly, and, once initiated, they are difficult to remedy. To address this issue, we previously proposed the Dynamic Counter Method (DCM), which monitors internal stress based on deposition layer shrinkage, enabling real-time stress monitoring without damaging the component. To validate this method, we used AlSi10Mg material, which has a low melting point and high reflectivity, and developed a high-precision segmentation network based on DeeplabV3+ to test its ability to measure shrinkage in high-exposure images. Using a real-time reconstruction model, stress calculations were performed with DCM and thermal–mechanical coupling simulations, and the results were validated through XRD residual stress testing to confirm DCM’s accuracy in calculating internal stress in aluminum alloys. The results show that the DeeplabV3+ segmentation network accurately extracted deposition-layer contours and shrinkage information. Furthermore, DCM and thermal–mechanical coupling simulations showed good consistency in residual stress distribution, with all results falling within the experimental error range. In terms of stress evolution trends, DCM was also effective in predicting stress variations. Based on these findings, two loading strategies were proposed, and, for the first time, DCM’s application in online stress monitoring of large LDED components was validated, offering potential solutions for stress monitoring in large-scale assemblies. Full article

The majority of engineering structures are subjected to intricate loading scenarios or possess intricate geometries, resulting in a mixed-mode stress within the component. This study aims to investigate the fracture behavior of these components under mixed-mode loading conditions by examining the relationship among the fatigue stress ratio (R), loading angle, and geometry thicknesses in compact tension shear (CTS) specimens. Using advanced ANSYS simulation techniques, this research explores how these factors affect the fatigue life cycles of engineering materials. To simulate real-world loading scenarios and study various mixed-mode configurations, compact tension shear (CTS) specimens were subjected to three specific loading angles: 30°, 45°, and 60°. These angles were applied in combination with various stress ratios (0.1–0.5) to capture a wide range of loading conditions. This study employed ANSYS Workbench 19.2, featuring cutting-edge technologies such as separating, morphing, and adaptive remeshing (SMART), to precisely model crack growth, calculate fatigue life, and analyze stress distribution. A comparative analysis with experimental data revealed that the loading angle has a profound effect on both the trajectory of fatigue crack growth (FCG) and the number of fatigue life cycles. The results demonstrate that the loading angle significantly influences the trajectory of FCG and the number of fatigue life cycles. Specifically, a loading angle of 45 degrees resulted in the maximum principal and shear stresses, indicating a state of pure shear loading. The findings reveal critical insights into the interaction between stress ratios, geometry thicknesses, fatigue life cycles, and loading angles, enhancing the understanding of engineering components’ behavior under mixed-mode stress situations. Full article

Background: Ophioglossum vulgatum Linn. is a medical herb widely distributed in Southwest China. It has been used for the treatment of various diseases, including wounds or dermatitis, since ancient times, but little is known about its pharmacological and pharmaceutical chemistry. Methods: The ethyl acetate fraction of O. vulgatum (OpvE) was prepared with the reflex extraction and fractional extraction method. Its components were detected and identified with the UPLC-Q/TOF-MS system and the SCIEX OS database. The related biological activities and the underlying mechanisms were predicted by computational analysis. HaCaT cells were treated with gradient concentrations of OpvE, and a CCK-8 assay was performed to determine the cell viability. The OpvE-pretreated HaCaT cells were exposed to H₂O₂ or LPS for antioxidative and anti-inflammatory assessment. DPPH, GSH, SOD, and MDA kits were used to evaluate oxidative stress. A serially diluted microbiota assay and a disc diffusion assay were used to evaluate anti-Staphylococcus aureus activities. The transcription of genes was semi-quantitatively studied by reversed real-time PCR. Protein levels were determined with western blotting. Results: The extract ratio of OpvE was 2.00 ± 0.12% (g/g). A total of 21 ingredients were identified. The computational analysis found that the PI3K/Akt signaling pathway might be a crucial target of OpvE. OpvE (7.5~125 μg/mL) stimulated HaCaT cell proliferation and migration by stimulating the over-expressed collagen type I alpha 1 Chain (COL1A1) and fibronectin 1 (FN1) and upregulating PI3K/AKT/GSK3-β signaling pathway. In the antioxidative assay test, 250 μg/mL OpvE scavenged obvious 97.28% DPPH-released free radicals. Pretreatment of OpvE inhibited H₂O₂-induced oxidative stress and protected against LPS-induced inflammatory injury by respectively regulating the Nrf2/HO-1/COX2 and TLR4/MYD88 signaling pathways. OpvE also showed anti-S. aureus properties with a MIC of 1.2 mg/mL, and with this concentration, OpvE produced an 8.3 ± 0.16 mm inhibition zone on a bacterial plate. Conclusions: This work highlighted the phytochemical character and some bioactivities, as well as the underline mechanism, which would support the further studies and application of O. vulgatum Linn. Full article

The real-time stress assessment of metal pressure components is one of the key factors in ensuring the safe operation of thermal power plants. To address the challenge of real-time prediction of stress in the key areas of complex special-shaped metal pressure-bearing components in a certain domestic 300 MW thermal power plant, three typical complex metal pressure-bearing components, the main steam pipe tee (MSPT), the steam drum downcomer joint (DDJ) and the header ligament (HL), were taken as research objects. The stress distribution of the three complex metal pressure-bearing components under different conditions was analyzed through the finite element method, and the stress results at the dangerous points were used as samples to establish training sample data. Subsequently, different machine learning methods were employed to train the sample data. The training results indicate that neural networks (NNs) and the Auto-Sklearn Regression (ASR) models can accurately predict the stress of the key parts of complex metal pressure-bearing components in real time. The ASR method demonstrates better performance in stress prediction of the main steam pipe tee, with a prediction accuracy of ≥96%. The NN model shows better prediction for the header ligament, with a prediction accuracy of ≥94%. These research findings provide effective support for the high-temperature lifespan assessment and safe operation of thermal power plants. Full article

This paper focuses on the estimation of multicomponent stress–strength models, an important concept in reliability analyses used to determine the probability that a system will function successfully under varying stress conditions. Understanding and accurately estimating these probabilities is essential in fields such as engineering and risk management, where the reliability of components under extreme conditions can have significant consequences. This is the case in applications where one seeks to model extreme hydrological events. Specifically, this study examines cases where the random variables X (representing strength) and Y (representing stress) follow extreme value distributions. New analytical expressions are derived for multicomponent stress–strength reliability (MSSR) when different classes of extreme distributions are considered, using the extreme value $\mathbb{H}$-function. These results are applied to three l-max stable laws and six p-max stable laws, providing a robust theoretical framework for multicomponent stress–strength analyses under extreme conditions. To demonstrate the practical relevance of the proposed models, a real dataset is analyzed, focusing on the monthly water capacity of the Shasta Reservoir in California (USA) during August and December from 1980 to 2015. This application showcases the effectiveness of the derived expressions in modeling real-world data. Full article

High-oxygen-permeability ionomers (HOPIs) are being actively developed to enhance the performance and durability of high-power polymer electrolyte membrane fuel cells (PEMFCs). While methods for evaluating binder performance are well-established, techniques for assessing binder durability and measuring its degradation in situ during the AST process remain limited. This study examines the distribution of relaxation times (DRT) and Warburg-like response (WLR) methods as in situ analysis techniques during the catalyst-accelerated stress test (AST) process. We conducted catalyst-ASTs (0.6–0.95 V cycling) for 20,000 cycles, monitoring changes using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV). Contrary to expectations, during the catalyst-AST, the ion transport resistance of the binder decreased, indicating no binder degradation. Scanning electron microscopy/energy dispersive spectrometer (SEM/EDS) analysis revealed that the degradation rate of the catalyst and the support was relatively higher than that of the binder, leading to a reduction in catalyst layer thickness and improved binder network formation. By applying the DRT method during the catalyst-AST process, we were able to measure the increase in oxygen reduction reaction (ORR) resistance and the decrease in proton transport resistance in situ. This allowed for the real-time detection of the reduction in catalyst layer thickness and improvements in ionomer networks due to catalyst and support degradation. These findings provide new insights into the complex interplay between catalyst degradation and binder performance, contributing to the development of more durable PEMFC components. Full article

The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model framework was proposed by integrating the finite-element method (FEM) and machine learning (ML), in which the basic methodology involved interrupting the computational workflow of the FEM and reassembling it with ML. Specifically, the displacement field, as the primary unknown quantity to be solved using the FEM, was mapped onto the displacement boundary conditions of the tube component with ML. To this end, the titanium tube material as well as the hydroforming process was investigated, and a fairly accurate FEM model was developed based on the CPB06 yield criterion coupled with a simplified Kim–Tuan hardening model. Numerous FEM simulations were performed by varying the loading conditions to generate the training database for ML. Then, a random forest algorithm was applied and trained to develop the surrogate model, in which the grid search method was employed to obtain the optimal combination of the hyperparameters. Sequentially, the principal strain, the effective strain/stress, as well as the wall thickness was derived according to continuum mechanics theories. Although further improvements were required in certain aspects, the developed FEM-ML surrogate model delivered extraordinary accuracy and instantaneity in reproducing multi-physical fields, especially the displacement field and wall-thickness distribution, manifesting its feasibility in the real-time, full-field simulation and monitoring of deformation states. Full article

This paper presents the results of experimental testing of joints welded using conventional TIG and laser methods. The welded components were sheets of the low-carbon steels 13CrMo4-5 and 16Mo3. Welded joints were made using different levels of linear welding energy. In the case of laser welding, a bifocal beam with longitudinal positioning of the focal lengths in relation to the welding direction was used. Experimental tests on welded joints included a bending test and determination of hardness distribution, mechanical properties, and fracture toughness, as well as microstructural research in the material of the various joint zones. Based on the determined strength characteristics, the true stress–strain relationships were defined, and a numerical model of the laser joints was developed in Abaqus 6.12-3. The modelled joint was subjected to loading to determine the most stressed areas of the joints. The numerical results were compared with those obtained using GOM’s Aramis 3D 5M digital image correlation system. The system used made it possible to record displacements on the surface of the analysed joints in real time. Good agreement was obtained between the strain fields calculated numerically and those recorded using the Aramis 3D 5M video system. The numerical calculations provided information on the strains and stresses occurring inside the analysed joint during loading. It was found that the welded joints were characterised by increased hardness and high strength properties in relation to the base material. The bending test of the laser-welded joints gave a positive result—no cracks were observed on the face or root of the weld. The fracture toughness of the joint zones is slightly lower in relation to that of the base material, but no brittle fracture was observed. Full article

This paper introduces a three-layer system, proposing a comprehensive model of granular mixture transport over a mobile sloped bed in a steady flow. This system, consisting of the bottom, contact, and upper zones, provides complete, continuous sediment velocity and concentration vertical profiles. The aim of this study is to develop and experimentally verify this model for sediment transport over a bottom locally sloping in line with or opposite the direction of sediment flow. The model considers gravity’s effect on sediment transport in the bottom (dense) layer when the component of gravity parallel to the bottom acts together with shear stresses associated with water flow. This is a crucial factor often overlooked in previous studies. This effect causes an increase in velocity in the mobile sublayer of the dense layer and significantly affects the vertical distributions of velocity and concentration above this layer. The proposed shear variation due to the interaction between fractions and an intensive sediment mixing and sorting process over a mobile sloped bed adds to the novelty of our approach. The data sets used for the model’s validation cover various conditions, including slopes, grain diameters, densities, and grain mobility conditions, from incipient motion to a fully mobilized bed. This extensive validation process instils confidence in the theoretical description and its applicability to real-world scenarios in the design of hydraulic infrastructure, such as dams, barrages, bridges, and irrigation, and flood control systems. Full article

This article presents a one-dimensional dynamic model for a thin-walled U-shaped telescopic crane boom segment, considering cross-section deformation, to address complex and inefficient dynamic modeling issues. The symmetric U-shaped cross-section provides a uniform distribution of mass and stress, enhancing the beam’s stability and bending stiffness. This symmetry allows for a simplified analysis in dynamic modeling, reducing the number of variables that need to be considered. The cross-section deformation is captured by basis functions satisfying displacement continuity conditions, which lays the foundation for constructing the initial model formulation based on the Hamilton principle. The variation forms of the cross-section are obtained by the decoupling eigenvalue problem, and then the principal component analysis is carried out to identify major cross-section deformation. The identified cross-section deformation features are hierarchically structured and have real physical significance. Finally, the initial one-dimensional higher-order dynamics model is improved by using the identified deformation mode. Numerical examples are presented in order to validate the three-dimensional dynamic properties and transient dynamic behavior of the U-shaped boom segment. The proposed model demonstrated high accuracy compared to ANSYS models, with relative errors below 2%. In addition, the method can be widely applied to a thin-walled U-shaped boom segment with a slenderness ratio of more than four. Full article

Strain sensors that can rapidly and efficiently detect strain distribution and magnitude are crucial for structural health monitoring and human–computer interactions. However, traditional electrical and optical strain sensors make access to structural health information challenging because data conversion is required, and they have intricate, delicate designs. Drawing inspiration from the moisture-responsive coloration of beetle wing sheaths, we propose using Ecoflex as a flexible substrate. This substrate is coated with a Fabry–Perot (F–P) optical structure, comprising a “reflective layer/stretchable interference cavity/reflective layer”, creating a dynamic color-changing visual strain sensor. Upon the application of external stress, the flexible interference chamber of the sensor stretches and contracts, prompting a blue-shift in the structural reflection curve and displaying varying colors that correlate with the applied strain. The innovative flexible sensor can be attached to complex-shaped components, enabling the visual detection of structural integrity. This biomimetic visual strain sensor holds significant promise for real-time structural health monitoring applications. Full article