作者机构：
[Liu, Peng; Wu, Gang; Feng, Guoda; Liu, Zhiqiang] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha 410114, Peoples R China.;[Zhao, Xiaohuan] Jinan Univ, Int Energy Coll, Energy & Elect Res Ctr, Zhuhai Campus, Zhuhai 519070, Peoples R China.;[Zhao, Xiaohuan] Jinan Univ, Sch Mech & Construct Engn, MOE Key Lab Disaster & Control Engn, Guangzhou 510632, Peoples R China.;[Zhao, Xiaohuan] Jinan Univ, Minist Educ, Sch Mech & Construct Engn, Key Lab Disaster & Control Engn, Guangzhou 510632, Peoples R China.
通讯机构：
[Zhao, XH ] J;Jinan Univ, Int Energy Coll, Energy & Elect Res Ctr, Zhuhai Campus, Zhuhai 519070, Peoples R China.
关键词：
CO oxidation;Oxygen vacancy;DFT plus U;Catalysts;Doped CeO2
摘要：
Catalyst addition can improve system after treatment efficiency of pollutants purification. In this study, the performance of (Mn, Hf) co-doped Ce-based catalysts has been investigated in oxidation to mitigate CO emission of engines. A series of Ce-Mn-Hf-Ox with different metal ratios (Ce: Mn: Hf = 8:1:1,7:2:1,7:1:2,6:1:3,6:2:2 and 6:3:1) catalysts were prepared. DFT (density functional theory) calculations were carried out to investigate the mechanism of applied surface catalytic reaction. XRD (X-ray Diffraction) analysis and SEM (Scanning Electron Microscope) revealed Mn and Hf doping increased the catalysts surface area contact with the CO gas with the catalytic performance improvement. XPS (X-ray Photoelectron Spectra), H2-TPR (Temperature-Programmed Reduction) and DFT calculations manifested the Mn and Hf doping would promote the formation of oxygen vacancy to enhance the catalytic performance for CO oxidation. The thermal stability of the catalysts increased with Hf content increasement. Ce6Mn3Hf1 had the highest catalytic activity with the favorable thermal stability according to the temperature-programmed oxidation of CO experiment. T10, T50 and T90 of CO oxidation were 156, 188 and 198 °C, which lost 4.82 % mass at 500°C and 6.18 % mass at 900 °C. Ce6Mn1Hf3Ox displayed the best thermal stability according to TG with the lowest catalytic activity.
Catalyst addition can improve system after treatment efficiency of pollutants purification. In this study, the performance of (Mn, Hf) co-doped Ce-based catalysts has been investigated in oxidation to mitigate CO emission of engines. A series of Ce-Mn-Hf-Ox with different metal ratios (Ce: Mn: Hf = 8:1:1,7:2:1,7:1:2,6:1:3,6:2:2 and 6:3:1) catalysts were prepared. DFT (density functional theory) calculations were carried out to investigate the mechanism of applied surface catalytic reaction. XRD (X-ray Diffraction) analysis and SEM (Scanning Electron Microscope) revealed Mn and Hf doping increased the catalysts surface area contact with the CO gas with the catalytic performance improvement. XPS (X-ray Photoelectron Spectra), H2-TPR (Temperature-Programmed Reduction) and DFT calculations manifested the Mn and Hf doping would promote the formation of oxygen vacancy to enhance the catalytic performance for CO oxidation. The thermal stability of the catalysts increased with Hf content increasement. Ce6Mn3Hf1 had the highest catalytic activity with the favorable thermal stability according to the temperature-programmed oxidation of CO experiment. T10, T50 and T90 of CO oxidation were 156, 188 and 198 °C, which lost 4.82 % mass at 500°C and 6.18 % mass at 900 °C. Ce6Mn1Hf3Ox displayed the best thermal stability according to TG with the lowest catalytic activity.
摘要：
Many materials in practical engineering exhibit completely different mechanical properties under tension and compression, such as reinforced concrete materials and fiber-reinforced polymers, etc. However, the existing structural design methods usually assume that the mechanical responses of material structures under tensile and compressive loads are the same (i.e. Symmetrical tension and compression characteristics). Considering that the loads are deterministic, the obtained design results may not meet the service requirements and could potentially cause catastrophic damage. This paper proposes a topology optimization method for the strut-and-tie composite structure model under uncertain load conditions. First, a composite structural model is constructed using three-phase materials with different tensile and compressive properties. Then, the design domain is discretized using the hybrid stress element, and a criterion for determining the state of the element in tension and compression is developed. Furthermore, the bivariate dimension reduction method and Gaussian integration method are employed to quantify and propagate load uncertainty. Moreover, an additional method for determining the state of the element in tension and compression under multiple load conditions is developed. Finally, the sensitivity of the objective function concerning the design variables is derived for both single and multiple load cases. Several examples are used to verify the effectiveness of this method, and the influence of optimization parameters such as different load uncertainty levels and the ratio of the elastic moduli of the tensile material and the compressive material on the design results is studied in detail.
Many materials in practical engineering exhibit completely different mechanical properties under tension and compression, such as reinforced concrete materials and fiber-reinforced polymers, etc. However, the existing structural design methods usually assume that the mechanical responses of material structures under tensile and compressive loads are the same (i.e. Symmetrical tension and compression characteristics). Considering that the loads are deterministic, the obtained design results may not meet the service requirements and could potentially cause catastrophic damage. This paper proposes a topology optimization method for the strut-and-tie composite structure model under uncertain load conditions. First, a composite structural model is constructed using three-phase materials with different tensile and compressive properties. Then, the design domain is discretized using the hybrid stress element, and a criterion for determining the state of the element in tension and compression is developed. Furthermore, the bivariate dimension reduction method and Gaussian integration method are employed to quantify and propagate load uncertainty. Moreover, an additional method for determining the state of the element in tension and compression under multiple load conditions is developed. Finally, the sensitivity of the objective function concerning the design variables is derived for both single and multiple load cases. Several examples are used to verify the effectiveness of this method, and the influence of optimization parameters such as different load uncertainty levels and the ratio of the elastic moduli of the tensile material and the compressive material on the design results is studied in detail.
作者机构：
[Long, Chengyun; Long, CY] South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou 511442, Peoples R China.;[Cai, Zizheng; Zhao, Bing; Zhou, Weichao; He, Daji] Changsha Univ Sci & Technol, Sch Civil Engn, Dept Mech, Changsha 410114, Peoples R China.
通讯机构：
[Long, CY ] S;[Zhao, B ] C;South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou 511442, Peoples R China.;Changsha Univ Sci & Technol, Sch Civil Engn, Dept Mech, Changsha 410114, Peoples R China.
摘要：
Accurate assessment of the thermal buckling behavior of microdevices is critical for the safe and secure operation of MEMS. However, the coupling of shearing and size effects on the thermal buckling of microbeams remains elusive and needs to be clarified. Here, we propose a theoretical model for the thermal buckling of microbeams that considers both the shearing and size effects, based on the modified gradient elasticity theory combined with the Timoshenko beam model. The explicit analytical forms of the deflection, the bending angle, the shearing angle and the critical buckling temperature rise are presented. The results show that the bending angle increases with the length/thickness ratio while the shearing angle decreases. It is confirmed that the shearing effect cannot be ignored in the thermal buckling of the doubly clamped microbeams with low aspect ratio. The size effect is captured in the thermal buckling of microbeams, which shows an increase in thermoelastic strength with the size reduction. The coupling effect on thermal buckling shows a weakening of the shearing effect by the size effect as the microbeam size decreases. This work could provide a theoretical reference for the design and monitoring of microdevices operating in high temperature environments.
摘要：
Stress-constrained topology optimization under geometrical nonlinear conditions is still an open topic as it often encounter difficulties such as mesh distortion, inaccurate stress evaluation and low computational efficiency. For this purpose, this paper develops a novel parallel-computing based topology optimization methodology for geometrically nonlinear continuum structures with stress constraints. To alleviate the mesh distortions in the low-density regions, a smooth material interpolation scheme from with different penalization for the elastic and nonlinear stiffness is proposed. Moreover, a new hybrid stress finite element formulation is included into the geometrically nonlinear topology optimization to capture a more accurate stress distribution that is less sensitive to mesh distortions. Then, to improve the computational efficiency of geometrically nonlinear and sensitivity analysis, a parallel computing framework based on the assembly free iterative solution is established. Meanwhile, an efficient sparse matrix-vector multiplication strategy, which is applicable to solve the geometrically nonlinear problems, is proposed to exploit the computing power of GPU effectively. Finally, several numerical examples are given to illustrate the efficiency and feasibility of the proposed method.
Stress-constrained topology optimization under geometrical nonlinear conditions is still an open topic as it often encounter difficulties such as mesh distortion, inaccurate stress evaluation and low computational efficiency. For this purpose, this paper develops a novel parallel-computing based topology optimization methodology for geometrically nonlinear continuum structures with stress constraints. To alleviate the mesh distortions in the low-density regions, a smooth material interpolation scheme from with different penalization for the elastic and nonlinear stiffness is proposed. Moreover, a new hybrid stress finite element formulation is included into the geometrically nonlinear topology optimization to capture a more accurate stress distribution that is less sensitive to mesh distortions. Then, to improve the computational efficiency of geometrically nonlinear and sensitivity analysis, a parallel computing framework based on the assembly free iterative solution is established. Meanwhile, an efficient sparse matrix-vector multiplication strategy, which is applicable to solve the geometrically nonlinear problems, is proposed to exploit the computing power of GPU effectively. Finally, several numerical examples are given to illustrate the efficiency and feasibility of the proposed method.
期刊：
JOURNAL OF SURVEYING ENGINEERING,2025年151(3):04025005 ISSN：0733-9453
通讯作者：
Fengling Li
作者机构：
[Hongwei Hu] Professor, College of Automobile and Mechanical Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China;[Zezhou Long] Postgraduate Student, College of Automobile and Mechanical Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China;Associate Professor, College of Mechanical and Vehicle Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China;[Kai Gao] Associate Professor, College of Automobile and Mechanical Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China;[Fengling Li] Associate Professor, College of Mechanical and Vehicle Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China
通讯机构：
[Fengling Li] A;Associate Professor, College of Mechanical and Vehicle Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China
摘要：
Proper spacing of rebar mesh is essential for structural integrity and safety, but measuring the spacing performance in three-dimensional (3D) space can be complicated. Existing spacing verification either relies on well-annotated data or encounters challenges when dealing with obscured point clouds. To address these challenges, we propose a novel approach using a projection clustering algorithm. Initially, we project the 3D point cloud onto a frontal view and perform density peak clustering in two directions, enabling accurate recognition of vertical and horizontal boundaries. Then, we iteratively select the centerline of each rebar based on clustering center points, maximizing internal points. Evaluation using the Dimension Quality Assessment System showed that our method achieves 94.5% accuracy in estimating rebar spacing. Compared with classical methods, our approach significantly improves spacing measurement.
摘要：
In order to explore the failure behaviors of lithium-ion batteries (LIBs) during thermal runaway (TR) under various oven temperatures, this study conducts comprehensive oven box abuse tests to investigate the impact of oven temperature on the characteristics of multidimensional signals during battery failure processes. The temporal relationships among expansion force, voltage, and temperature are revealed across four distinct stages of the thermal abuse process. The results indicate that within the oven temperature range of 150 °C to 250 °C, the LIBs invariably undergo venting, with the venting force ranging from 4000 N to 6422 N. Furthermore, the voltage of the batteries remains relatively stable at approximately 3.33 V before venting. An Internal short circuit and TR events occur once the oven temperature exceeds 180 °C, and the corresponding TR trigger temperatures are approximately 232 °C. Moreover, the abnormal force can be utilized as the earliest warning indicator prior to venting, and the lead time for expansion force warning signals decreases with increasing oven temperature in batteries undergoing TR. This study investigates multidimensional signals to effectively identify battery failures, deepening the understanding of the TR behaviors of batteries under different oven temperatures, and providing valuable insights for early warning and safety design of battery systems.
In order to explore the failure behaviors of lithium-ion batteries (LIBs) during thermal runaway (TR) under various oven temperatures, this study conducts comprehensive oven box abuse tests to investigate the impact of oven temperature on the characteristics of multidimensional signals during battery failure processes. The temporal relationships among expansion force, voltage, and temperature are revealed across four distinct stages of the thermal abuse process. The results indicate that within the oven temperature range of 150 °C to 250 °C, the LIBs invariably undergo venting, with the venting force ranging from 4000 N to 6422 N. Furthermore, the voltage of the batteries remains relatively stable at approximately 3.33 V before venting. An Internal short circuit and TR events occur once the oven temperature exceeds 180 °C, and the corresponding TR trigger temperatures are approximately 232 °C. Moreover, the abnormal force can be utilized as the earliest warning indicator prior to venting, and the lead time for expansion force warning signals decreases with increasing oven temperature in batteries undergoing TR. This study investigates multidimensional signals to effectively identify battery failures, deepening the understanding of the TR behaviors of batteries under different oven temperatures, and providing valuable insights for early warning and safety design of battery systems.
作者机构：
[Dongying Dong; Kun Chen; Linjun Zeng] College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha, 410114, China;[Jianing Zhang; Zhoucheng Wu] International College of Engineering, Changsha University of Science & Technology, Changsha, 410114, China;[Xu Zhang] College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha, 410114, China;[Junjia Cui] State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China
通讯机构：
[Linjun Zeng] C;College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha, 410114, China
摘要：
TC4 based composites were facing an urgent need to enhance physical performance. In this work, electromagnetic powder compaction (EMPC) technology was used to prepare carbon nanotubes reinforced TC4 based (CNTs/TC4) composites. The micro morphology and properties of the sintered bodies were studied through scanning/transmission electron microscopy (SEM/TEM) and a universal material testing machine at different sintering temperatures. Results showed that there was a significant turning point at 1100 °C, with a relative density of 98.01%. The relationship between the relative length and density of sintered bodies was established through Gaussian fitting. The CNTs partially reacted with the TC4 matrix after 700 °C, generating equiaxed TiC particles. The compressive strength and strain reached the maximum values at 1300 °C and 600 °C, respectively, with values of 1884.64 MPa and 0.2336. The enhancement factors for compressive strength were only alloying and TiC particles at 1100 °C. There was not much difference in the fusion effect of particles at 1100 °C and 1300 °C. From the above analysis, it can be concluded that the CNTs/TC4 composites prepared by EMPC had relatively good comprehensive mechanical properties when sintered at 1100 °C.
TC4 based composites were facing an urgent need to enhance physical performance. In this work, electromagnetic powder compaction (EMPC) technology was used to prepare carbon nanotubes reinforced TC4 based (CNTs/TC4) composites. The micro morphology and properties of the sintered bodies were studied through scanning/transmission electron microscopy (SEM/TEM) and a universal material testing machine at different sintering temperatures. Results showed that there was a significant turning point at 1100 °C, with a relative density of 98.01%. The relationship between the relative length and density of sintered bodies was established through Gaussian fitting. The CNTs partially reacted with the TC4 matrix after 700 °C, generating equiaxed TiC particles. The compressive strength and strain reached the maximum values at 1300 °C and 600 °C, respectively, with values of 1884.64 MPa and 0.2336. The enhancement factors for compressive strength were only alloying and TiC particles at 1100 °C. There was not much difference in the fusion effect of particles at 1100 °C and 1300 °C. From the above analysis, it can be concluded that the CNTs/TC4 composites prepared by EMPC had relatively good comprehensive mechanical properties when sintered at 1100 °C.
作者机构：
[Li, Zelong; Zhang, Zhiwu] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410114, China;Hunan Province University Key Laboratory of Intelligent Testing and Control Technology for Engineering Equipment, Changsha 410114, China;[Liu, Zhiping; Lyu, Duo; Hu, Hongwei] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410114, China<&wdkj&>Hunan Province University Key Laboratory of Intelligent Testing and Control Technology for Engineering Equipment, Changsha 410114, China
通讯机构：
[Hongwei Hu] C;College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410114, China<&wdkj&>Hunan Province University Key Laboratory of Intelligent Testing and Control Technology for Engineering Equipment, Changsha 410114, China
摘要：
Leaky Rayleigh waves are sensitive to surface defects and beneficial for automated detection due to their non-contact characteristics. However, the amplitude of the leaky Rayleigh waves is low due to the attenuation of waveform conversion and acoustic waves propagation, which limits the imaging quality for long-distance detection. This study introduces a novel method combining leaky Rayleigh waves detection with virtual source total focusing method. The virtual source (VS) technology enhances the emission energy of leaky Rayleigh waves. Then, the total focusing method (TFM) is applied to acquire high-accuracy images of defects. To reduce noise and artifacts, a weighting function based on the coherence factor (CF) is developed to weight the TFM superimposed signals, thereby achieving high-quality image reconstruction. Experimental results show that compared with the conventional TFM method, the proposed method can effectively improve the amplitude of imaging signals while reducing system noise and imaging artifacts. The lateral accuracy of defects is improved, with the average lateral error of defect size being 0.178 mm. The signal-to-noise ratio (SNR) of ultrasound images is increased by 27.59 dB, and the array performance index (API) of ultrasound images is decreased by 33.78 %. The proposed method provides a new and effective approach for quantitatively assessing the surface defects of metal components using leaky Rayleigh waves.
Leaky Rayleigh waves are sensitive to surface defects and beneficial for automated detection due to their non-contact characteristics. However, the amplitude of the leaky Rayleigh waves is low due to the attenuation of waveform conversion and acoustic waves propagation, which limits the imaging quality for long-distance detection. This study introduces a novel method combining leaky Rayleigh waves detection with virtual source total focusing method. The virtual source (VS) technology enhances the emission energy of leaky Rayleigh waves. Then, the total focusing method (TFM) is applied to acquire high-accuracy images of defects. To reduce noise and artifacts, a weighting function based on the coherence factor (CF) is developed to weight the TFM superimposed signals, thereby achieving high-quality image reconstruction. Experimental results show that compared with the conventional TFM method, the proposed method can effectively improve the amplitude of imaging signals while reducing system noise and imaging artifacts. The lateral accuracy of defects is improved, with the average lateral error of defect size being 0.178 mm. The signal-to-noise ratio (SNR) of ultrasound images is increased by 27.59 dB, and the array performance index (API) of ultrasound images is decreased by 33.78 %. The proposed method provides a new and effective approach for quantitatively assessing the surface defects of metal components using leaky Rayleigh waves.
作者机构：
[Weiqiang Jia] Zhejiang Lab, Hangzhou, China;[Qiuze Yao; Xiang Peng; Jiquan Li; Shaofei Jiang] College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China;[Xin Liu] Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha, China
通讯机构：
[Xiang Peng] C;[Weiqiang Jia] Z;Zhejiang Lab, Hangzhou, China<&wdkj&>College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
关键词：
composite;deep learning;finite element method;local stress;representative volume element
摘要：
Predicting the full-field stress distribution has important significance in analyzing mechanical characteristics of composite materials. Numerical calculations of stress field distribution using finite element analysis (FEA) can call for significant computational effort for microscale geometries. To address this challenge, this paper demonstrates a deep learning (DL) framework for predicting local stress distributions in fiber-reinforced composites with diverse fiber volume fractions. An adaptive generation algorithm of representative volume element (RVE) microstructures is developed for constructing stochastic RVE with diverse fiber volume fractions, and the corresponding von Mises stress distribution is calculated using the FEA method. The U-Net framework is developed, whose weights are trained based on the samples with fiber volume fractions of 30%, 40%, and 50%. Then the weights of the DL model are updated based on additional little samples with random fiber volume fractions between 30% and 50%. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are used for qualifying the accuracy of predicted stress field distributions. The predicted results for a series of microscale geometries show that the mean SSIM values for stress field prediction for diverse fiber volume fractions are up to 98.04%, which can illustrate that the proposed DL model can predict the stress field distribution with diverse fiber volume fractions successfully.
作者机构：
[Hao Zhong; Haijun Wang] School of Intelligent Engineering and Intelligent Manufacturing, Hunan University of Technology and Business, Changsha 410205, PR China;[Fei Lei; Jie Liu; Fei Ding] State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, PR China;[Wenhao Zhu] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China
通讯机构：
[Fei Lei] S;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, PR China
摘要：
Advanced heating is essential for mitigating temporary losses in battery capacity and peak power at low temperatures, thereby enhancing utilization efficiency and reducing safety risks. Expanding upon our prior research, this paper proposes a safety-reinforced mutual pulse heating (MPH) strategy based on microscopic-state estimation, in which multiplexing converter-based drivers are reused as on-board heating topologies. An adaptive super-twisting sliding mode observer is utilized to capture battery solid-liquid phase Li + concentration, electrode and electrolyte potentials, and side-reaction overpotentials. Then, a microscopic-state constraints-based MPH current prediction method is formulated to delineate the safe operational area of battery micro-states. The MPH current amplitude is updated by an online negative feedback-based optimization controller, while the heating duty ratio is still adjusted using fuzzy feedback mechanisms. Experimental validation of the proposed method is conducted across five critical metrics: heating duration, temperature-rise rate, safety risk, temperature uniformity, and degradation. The method demonstrated its efficacy by elevating the battery temperature from -20 °C to 0 °C within 178.3 s, while keeping the microscopic states within safe thresholds, with a peak temperature gradient of <2 °C. During 800 heating cycles, battery integrity remains intact as evidenced by stable capacity, coulomb efficiency, internal resistance, and differential voltage curves.
Advanced heating is essential for mitigating temporary losses in battery capacity and peak power at low temperatures, thereby enhancing utilization efficiency and reducing safety risks. Expanding upon our prior research, this paper proposes a safety-reinforced mutual pulse heating (MPH) strategy based on microscopic-state estimation, in which multiplexing converter-based drivers are reused as on-board heating topologies. An adaptive super-twisting sliding mode observer is utilized to capture battery solid-liquid phase Li + concentration, electrode and electrolyte potentials, and side-reaction overpotentials. Then, a microscopic-state constraints-based MPH current prediction method is formulated to delineate the safe operational area of battery micro-states. The MPH current amplitude is updated by an online negative feedback-based optimization controller, while the heating duty ratio is still adjusted using fuzzy feedback mechanisms. Experimental validation of the proposed method is conducted across five critical metrics: heating duration, temperature-rise rate, safety risk, temperature uniformity, and degradation. The method demonstrated its efficacy by elevating the battery temperature from -20 °C to 0 °C within 178.3 s, while keeping the microscopic states within safe thresholds, with a peak temperature gradient of <2 °C. During 800 heating cycles, battery integrity remains intact as evidenced by stable capacity, coulomb efficiency, internal resistance, and differential voltage curves.
摘要：
In this article, the hyperelastic material of resilient wheels of subway vehicles is taken as the research object, a method system of identifying and calibrating the constitutive parameters of hyperelastic model based on joint experiment-simulation-deep learning is proposed. The theoretical analytical expression of the true stress-strain on stretch of the hyperelastic model Yeoh model under uniaxial loading condition is deduced, the accurate stress-strain curve data are captured by performing the compression experiment of cylindrical specimen of rubber component of urban rail transit system resilient wheel, and the initial values of parameters of the Yeoh hyperelastic model are fitted by the experimental data. The true stress-strain response samples of compression specimens under different Yeoh model parameter conditions were obtained by finite element numerical simulation. The Yeoh model's optimal parameters are obtained by training the data using a deep learning technique under the specified compression test stress-strain circumstances. Finite element numerical simulation is utilized to confirm the parameters' accuracy. The radial stiffness test of the resilient wheel rubber component was carried out, and the examination of the resilient wheel rubber component's stiffness analysis using the model parameters that were optimized. The study's findings indicate that the methodological system of identifying and calibrating the hyperelastic model constitutive parameters of the hyperelastic model proposed in this paper by combined experiment-simulation-deep learning has high prediction accuracy for the hyperelastic constitutive model parameters.
In this article, the hyperelastic material of resilient wheels of subway vehicles is taken as the research object, a method system of identifying and calibrating the constitutive parameters of hyperelastic model based on joint experiment-simulation-deep learning is proposed. The theoretical analytical expression of the true stress-strain on stretch of the hyperelastic model Yeoh model under uniaxial loading condition is deduced, the accurate stress-strain curve data are captured by performing the compression experiment of cylindrical specimen of rubber component of urban rail transit system resilient wheel, and the initial values of parameters of the Yeoh hyperelastic model are fitted by the experimental data. The true stress-strain response samples of compression specimens under different Yeoh model parameter conditions were obtained by finite element numerical simulation. The Yeoh model's optimal parameters are obtained by training the data using a deep learning technique under the specified compression test stress-strain circumstances. Finite element numerical simulation is utilized to confirm the parameters' accuracy. The radial stiffness test of the resilient wheel rubber component was carried out, and the examination of the resilient wheel rubber component's stiffness analysis using the model parameters that were optimized. The study's findings indicate that the methodological system of identifying and calibrating the hyperelastic model constitutive parameters of the hyperelastic model proposed in this paper by combined experiment-simulation-deep learning has high prediction accuracy for the hyperelastic constitutive model parameters.
期刊：
Engineering Applications of Artificial Intelligence,2025年151:110760 ISSN：0952-1976
通讯作者：
Ong, ZC
作者机构：
[Ong, Zhi Chao; Khoo, Shin Yee; Ong, ZC; Siow, Pei Yi; Wang, Teng; Wang, Tao] Univ Malaya, Fac Engn, Dept Mech Engn, Kuala Lumpur, Malaysia.;[Ong, Zhi Chao] Univ Malaya, Fac Engn, Ctr Res Ind CRI 40 40, Kuala Lumpur, Malaysia.;[Zhang, Jinlai] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, 960 Sect 2,Wanjiali RD S, Changsha, Peoples R China.
通讯机构：
[Ong, ZC ] U;Univ Malaya, Fac Engn, Dept Mech Engn, Kuala Lumpur, Malaysia.
关键词：
Data augmentation;Generative adversarial network;Dual cross-frequency attention;Variable operating conditions;Fault diagnosis
摘要：
As a typical data augmentation method, a generative adversarial network is widely applied to solve data scarcity problems for imbalanced bearing fault diagnosis. However, these methods still face challenges in generating longer data due to the risk of mode collapse and instability during training. To address this issue, an enhanced generative adversarial network is proposed for generating longer vibration time data to improve imbalanced bearing fault diagnosis under variable operating conditions. Firstly, a dual cross-frequency attention block is integrated into the discriminator to adaptively extract intra-component and inter-component features across low and high frequency components decomposed using Wavelet, thereby facilitating generator to generate longer synthetic time data with higher frequency resolution. Furthermore, the sequence information block is introduced to generate synthetic time data under variable operating conditions by incorporating specific operating condition information into the generator. To expedite the synthetic data generation process under variable operating conditions, healthy data corresponding to these conditions are used as input for the generator, replacing random noise. Finally, the superiority of the proposed method is validated through experiments on two bearing datasets for imbalanced bearing fault diagnosis. Experimental results on these two datasets demonstrate that the proposed method with 2048-length synthetic data achieves the highest accuracy of 98.75 % and 96.88 %, respectively, outperforming state-of-the-art methods. Therefore, the proposed method can effectively address the challenge of generating longer vibration time data, improving diagnostic accuracy in imbalanced bearing fault diagnosis under variable operating conditions.
As a typical data augmentation method, a generative adversarial network is widely applied to solve data scarcity problems for imbalanced bearing fault diagnosis. However, these methods still face challenges in generating longer data due to the risk of mode collapse and instability during training. To address this issue, an enhanced generative adversarial network is proposed for generating longer vibration time data to improve imbalanced bearing fault diagnosis under variable operating conditions. Firstly, a dual cross-frequency attention block is integrated into the discriminator to adaptively extract intra-component and inter-component features across low and high frequency components decomposed using Wavelet, thereby facilitating generator to generate longer synthetic time data with higher frequency resolution. Furthermore, the sequence information block is introduced to generate synthetic time data under variable operating conditions by incorporating specific operating condition information into the generator. To expedite the synthetic data generation process under variable operating conditions, healthy data corresponding to these conditions are used as input for the generator, replacing random noise. Finally, the superiority of the proposed method is validated through experiments on two bearing datasets for imbalanced bearing fault diagnosis. Experimental results on these two datasets demonstrate that the proposed method with 2048-length synthetic data achieves the highest accuracy of 98.75 % and 96.88 %, respectively, outperforming state-of-the-art methods. Therefore, the proposed method can effectively address the challenge of generating longer vibration time data, improving diagnostic accuracy in imbalanced bearing fault diagnosis under variable operating conditions.
摘要：
Accurate trajectory prediction of surrounding vehicles is crucial for ensuring the safety of autonomous vehicles. However, current methods based on neural networks still have room for improvement by further reducing their long-term error. This challenge stems from extracting temporal feature dependencies and mining spatial interactions in lane change scenarios. Previous research has not adequately established the connection between the past and the future features. To this end, we propose a spatial-temporal multi-feature fusion and intention-enlightened decoding (STMF-IE) model that jointly considers all these aspects. Initially, STMF-IE uses multi-head temporal self-attention to establish temporal dependencies that make explicit the extent of information usage at different time steps. The interplay between the neighboring vehicles and the target vehicle is quantitatively depicted through a devised multi-head spatial cross-attention mechanism. Furthermore, a multi-feature fusion module is proposed such that the extracted temporal dependence and spatial interaction features are integrated to reduce the superfluous features. Additionally, a novel mask matrix and an intention-enlightened decoding module are developed to refine the prediction performance in different lane change scenarios. Experimental results show that STMF-IE outperforms state-of-the-art methods for long-term prediction on NGSIM and highD datasets. We improve the RMSE metric by 13%-36% at a prediction horizon of 3$\sim$5 s. We also analyze the spatial-temporal feature correlation through visual results, promoting more interpretation.
摘要：
To support regulations related to cyclists dismounting and pushing across motor vehicle lanes, this study first extracted posture parameters for riding/pushing through physical experiments, then simplified the posture parameters and extracted typical postures with distinguishability. Finally, simulated experiments were designed to compare the differences in human injury between riding and pushing in collision accidents. The physical experiment found that the bicycle controller's (individuals who ride or push bicycles) forward leaning angle of the back (referred to as the 'back angle') when riding can be estimated by the ratio of saddle height to bicycle handlebar height, but the back angle when pushing is not affected by other parameters. Eight typical postures containing only lower limb posture parameters were then identified, and statistical analysis showed significant differences in over 80% of parameters among the postures. Through simulated experiments, the rider's tolerable saddle height was identified. Even at this saddle height, the injury in riding state was still significantly higher than that in the pushing state, indicating that pushing the bicycle across the motor vehicle lane can reduce the injury to the bicycle controller. The research results not only support relevant regulations, but also provide typical postures for the bicycle controller in vehicle-bicycle collision experiments in the future, and have certain theoretical and practical value.
摘要：
Ultrasonic leaky Rayleigh wave enables easy automated detection of surface or sub-surface defects due to its non-contact detection advantages. The existing leaky Rayleigh wave detection methods using single transducer suffer from low focused energy and short detection range. To solve these problems, this paper adopts the detection method of leaky Rayleigh wave generated by phased array, and proposes an imaging algorithm using extended phase shift migration (EPSM) and image fusion. Firstly, the virtual source-based extended phase shift migration (VSEPSM) algorithm is proposed to enhance the effective detection distance by increasing the energy of the transmitting elements. Then, the fast Fourier transform (FFT) interpolation algorithm is employed to enhance the lateral detail representation in EPSM and VSEPSM imaging, improving the imaging quality. Finally, a custom image fusion method is used to perform arithmetic processing on the imaging amplitudes at corresponding positions in EPSM and VSEPSM imaging, merging the detection advantages of EPSM and VSEPSM at different distances. Compared to traditional time-domain full focusing (TFM) imaging, the imaging algorithm proposed in this paper achieves better imaging performance for defects at relatively distant locations, with a 5.94 dB increase in average signal-to-noise ratio (SNR) and a 61.4% improvement in imaging efficiency. This provides an effective method for detecting surface and sub-surface defects in the industrial field.
Ultrasonic leaky Rayleigh wave enables easy automated detection of surface or sub-surface defects due to its non-contact detection advantages. The existing leaky Rayleigh wave detection methods using single transducer suffer from low focused energy and short detection range. To solve these problems, this paper adopts the detection method of leaky Rayleigh wave generated by phased array, and proposes an imaging algorithm using extended phase shift migration (EPSM) and image fusion. Firstly, the virtual source-based extended phase shift migration (VSEPSM) algorithm is proposed to enhance the effective detection distance by increasing the energy of the transmitting elements. Then, the fast Fourier transform (FFT) interpolation algorithm is employed to enhance the lateral detail representation in EPSM and VSEPSM imaging, improving the imaging quality. Finally, a custom image fusion method is used to perform arithmetic processing on the imaging amplitudes at corresponding positions in EPSM and VSEPSM imaging, merging the detection advantages of EPSM and VSEPSM at different distances. Compared to traditional time-domain full focusing (TFM) imaging, the imaging algorithm proposed in this paper achieves better imaging performance for defects at relatively distant locations, with a 5.94 dB increase in average signal-to-noise ratio (SNR) and a 61.4% improvement in imaging efficiency. This provides an effective method for detecting surface and sub-surface defects in the industrial field.
摘要：
The safety assessment of intelligent vehicles as an important part of the intelligent vehicle industry is used to assess the safety of vehicles on the road. Drivers' risk perception affects their driving behavior, so taking it into account in safety assessments can more accurately assess the safety of intelligent vehicles. Decision-makers often have difficulty choosing the best option from multiple indicators in a safety assessment. The primary objective of this paper is to assess the safety of intelligent vehicles, considering drivers' risk perception in a fuzzy environment. A multi-level evaluation system for the safety assessment of intelligent vehicles is developed, covering functional safety, active and passive vehicle safety, netlink information reliability, and driver risk perception. A hybrid decision-making methodology under Interval 2-tuple q-rung Orthopair Fuzzy Sets is proposed for safety assessments of intelligent vehicles. Subsequently, an empirical application of four safe driving schemes demonstrates the validity and practicality of this integrated methodology. Comparative analysis, sensitivity analysis, and discussions are performed. The results prove that this approach provides an accurate and effective tool for the safety assessment of intelligent vehicles.
The safety assessment of intelligent vehicles as an important part of the intelligent vehicle industry is used to assess the safety of vehicles on the road. Drivers' risk perception affects their driving behavior, so taking it into account in safety assessments can more accurately assess the safety of intelligent vehicles. Decision-makers often have difficulty choosing the best option from multiple indicators in a safety assessment. The primary objective of this paper is to assess the safety of intelligent vehicles, considering drivers' risk perception in a fuzzy environment. A multi-level evaluation system for the safety assessment of intelligent vehicles is developed, covering functional safety, active and passive vehicle safety, netlink information reliability, and driver risk perception. A hybrid decision-making methodology under Interval 2-tuple q-rung Orthopair Fuzzy Sets is proposed for safety assessments of intelligent vehicles. Subsequently, an empirical application of four safe driving schemes demonstrates the validity and practicality of this integrated methodology. Comparative analysis, sensitivity analysis, and discussions are performed. The results prove that this approach provides an accurate and effective tool for the safety assessment of intelligent vehicles.
摘要：
In this work, interstitial carbon has been employed to further enhance the mechanical and anti-corrosion properties of a metastable quinary Fe 40 Mn 10 Co 20 Cr 20 Ni 10 (at. %) high-entropy alloy (HEA). Both the C-free and C-doped (0.5 at. %) HEAs exhibit a face-centered cubic (FCC) single-phase structure after annealing. Upon tensile deformation, martensitic transformation prevails in the C-free alloy with the formation of hexagonal closed packed (HCP) phase, whereas dislocation slip and twinning are the dominant deformation modes in the C-doped HEA. Such shift of deformation mechanisms can be attributed to the carbon induced increase of stacking fault energy (SFE) (from ∼10 to ∼15 mJ/m 2 ). Simultaneous increases of strength and ductility are achieved in this HEA system by carbon alloying. Carbon-induced interstitial solid solution strengthening effect contributes to the increased stress level, whereas the promoted twinning behavior contributes to the enhanced strain hardening ability. Besides, electrochemical corrosion analysis demonstrates that interstitial carbon reduces the active current density and accelerates the passivation process of the HEA upon immersion in 0.1M H 2 SO 4 solution, contributing to the enhanced corrosion resistance. The findings offer insights into the design of strong, ductile and corrosion-resistant alloys.
In this work, interstitial carbon has been employed to further enhance the mechanical and anti-corrosion properties of a metastable quinary Fe 40 Mn 10 Co 20 Cr 20 Ni 10 (at. %) high-entropy alloy (HEA). Both the C-free and C-doped (0.5 at. %) HEAs exhibit a face-centered cubic (FCC) single-phase structure after annealing. Upon tensile deformation, martensitic transformation prevails in the C-free alloy with the formation of hexagonal closed packed (HCP) phase, whereas dislocation slip and twinning are the dominant deformation modes in the C-doped HEA. Such shift of deformation mechanisms can be attributed to the carbon induced increase of stacking fault energy (SFE) (from ∼10 to ∼15 mJ/m 2 ). Simultaneous increases of strength and ductility are achieved in this HEA system by carbon alloying. Carbon-induced interstitial solid solution strengthening effect contributes to the increased stress level, whereas the promoted twinning behavior contributes to the enhanced strain hardening ability. Besides, electrochemical corrosion analysis demonstrates that interstitial carbon reduces the active current density and accelerates the passivation process of the HEA upon immersion in 0.1M H 2 SO 4 solution, contributing to the enhanced corrosion resistance. The findings offer insights into the design of strong, ductile and corrosion-resistant alloys.
关键词：
green supply chain management;type-2 fuzzy set;decision behavior;prospect-regret theory
摘要：
The concept of green supply chain management (GSCM) describes how to reduce the negative impact of the supply chain on the environment while balancing the economic and social benefits of a company being in the supply chain. Selecting the optimal multi-dimensional GSCM scheme, a typical multi-criteria decision-making (MCDM) problem, is a crucial step in implementing the GSCM concept. Therefore, this paper constructs a multi-dimensional GSCM index system for the comprehensive analysis of the important influencing factors of GSCM. Then, cross-entropy combining the interval type-2 trapezoidal fuzzy set (IT2TFS) is adopted to determine the weight distribution of GSCM indices, and a hybrid MCDM method integrating the IT2TFS prospect-regret method is proposed to analyze the psychological behaviors of decision makers who are selecting the best GSCM scheme. Moreover, the case study, comparative analysis, and sensitivity analysis are presented to verify the effectiveness and reasonableness of the proposed MCDM method. The results affirm the validity of the proposed MCDM method, with A4 identified as the optimal GSCM scheme, demonstrating its effectiveness and applicability in MCDM problems.
摘要：
Metal matrix composite (MMC) coatings with good metallurgical bonding were fabricated using laser cladding, focusing on the effect of SiC ceramic contents on the tribological properties and oxidation resistance of nickel matrix composite coatings. The results demonstrated that the microhardness of MMC coatings increased by a factor of 2.28–3.16 and 1.20–1.38, respectively, over the substrate and metal coating under the synergistic action of multiple strengthening effects. As SiC content increases, the wear resistance of coatings shows a trend of first enhancement and then decrease, but both are better than the SiC-free coating and substrate. Where the MMC coating with 15 wt% SiC addition showed the best tribological performance at room and high temperatures, the wear rate was reduced by 71.54 %/ 49.28 % (RT) and 58.8 %/38.17 % (HT) compared to the metal coating and the substrate, respectively, which was mainly attributed to its highest microhardness and densest microstructure. After prolonged oxidation at elevated-temperature, the uniformity and densification of the oxide film on the coating surface increased with increasing SiC content, and the oxidation resistance was linearly strengthened. The synergistic effect of uniformly distributed stable oxide Cr 2 O 3 and self-repairing SiO 2 led to a remarkable improvement in the oxidation resistance of MMC coatings.
Metal matrix composite (MMC) coatings with good metallurgical bonding were fabricated using laser cladding, focusing on the effect of SiC ceramic contents on the tribological properties and oxidation resistance of nickel matrix composite coatings. The results demonstrated that the microhardness of MMC coatings increased by a factor of 2.28–3.16 and 1.20–1.38, respectively, over the substrate and metal coating under the synergistic action of multiple strengthening effects. As SiC content increases, the wear resistance of coatings shows a trend of first enhancement and then decrease, but both are better than the SiC-free coating and substrate. Where the MMC coating with 15 wt% SiC addition showed the best tribological performance at room and high temperatures, the wear rate was reduced by 71.54 %/ 49.28 % (RT) and 58.8 %/38.17 % (HT) compared to the metal coating and the substrate, respectively, which was mainly attributed to its highest microhardness and densest microstructure. After prolonged oxidation at elevated-temperature, the uniformity and densification of the oxide film on the coating surface increased with increasing SiC content, and the oxidation resistance was linearly strengthened. The synergistic effect of uniformly distributed stable oxide Cr 2 O 3 and self-repairing SiO 2 led to a remarkable improvement in the oxidation resistance of MMC coatings.
摘要：
Achieving favorable strength and hydrogen embrittlement (HE) resistance synergy has long been one of the main challenges for developing structural materials. The current work reports a AlCoCrFeNi 2.1 eutectic high-entropy alloy with ultrahigh-strength and well HE resistance, achieved by well controlled selective laser melting process. The dislocation pile-up along the nano cellular B2 phase resulted in the high yield strength of 1065 MPa, ultimate tensile strength of 1263 MPa, and fracture strain of 25.3 %. Although the piled-up dislocations can also assist hydrogen concentration in B2/matrix interface during straining, resulted in the HE crack initiation and propagation along the interface. However, the refined lamellar B2 phase increased the length of the B2/matrix interface, thus decrease the level of hydrogen concentration near the interface and shows improved HE resistance.
Achieving favorable strength and hydrogen embrittlement (HE) resistance synergy has long been one of the main challenges for developing structural materials. The current work reports a AlCoCrFeNi 2.1 eutectic high-entropy alloy with ultrahigh-strength and well HE resistance, achieved by well controlled selective laser melting process. The dislocation pile-up along the nano cellular B2 phase resulted in the high yield strength of 1065 MPa, ultimate tensile strength of 1263 MPa, and fracture strain of 25.3 %. Although the piled-up dislocations can also assist hydrogen concentration in B2/matrix interface during straining, resulted in the HE crack initiation and propagation along the interface. However, the refined lamellar B2 phase increased the length of the B2/matrix interface, thus decrease the level of hydrogen concentration near the interface and shows improved HE resistance.
