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A novel domain decomposition-based model for efficient dynamic predictions of large composite machine tools

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Science China(Technological Sciences) 2024年第6期67卷 1765-1782页

作者： YU YangBo JI Yu Lei CHEN YanRen XU Kun BI QingZhen state key laboratory of Mechanical System and vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai200240China

We propose a large combined moving component composed of carbon fiber reinforced polymer(CFRP)laminates for making lightweight machine tools with high dynamic *** accurate dynamic prediction of composite machine tools is essential for the new generation machine *** paper aims to address two challenges in numerical dynamic modeling and the design of composite machine tools to enhance development efficiency.(1)Anisotropic composite laminates,which form the composite machine tool,exhibit coupling in various *** propose the generalized continuity condition of the boundary to tackle this dynamic modeling challenge.(2)Composite machine tools feature numerous composite-metal coupled *** mechanical model correction of isotropic metals is performed to address their *** take the example of a five-axis gantry machine tool with composite moving parts,establish a dynamic model for efficient prediction,and verify it through simulation and *** proposed method yields remarkable results,with an average relative error of only 3.85%in modal frequency prediction and a staggering 99.7%reduction in solution time compared to finite element *** further discuss the dynamic performance of the machine tool under varied stacking angles and layer numbers of the composite machine *** propose general design criteria for composite machine tools to consider the modal frequency and manufacturing cost of machine tools.

关键词： machine tool dynamics carbon fiber reinforced polymer composite laminates

A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis

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Science China(Technological Sciences) 2024年第8期67卷 2594-2618页

作者： ZHONG Tao QIN ChengJin SHI Gang ZHANG ZhiNan TAO JianFeng LIU ChengLiang state key laboratory of Mechanical System and vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

As a critical component of a tunnel boring machine(TBM),the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter ***,under conditions of strong noise and complex working environments,traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification *** address these issues,we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network(RDMA-WDAN)for TBM main bearing fault *** approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules,achieving better domain adaptation despite significant domain *** residual denoising component utilizes a convolutional block to extract noise features,removing them via residual ***,the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features,concentrating on deep fault *** training,a weighting mechanism is introduced to prioritize domain samples with clear fault *** optimizes the deep feature extractor to obtain common features,enhancing domain adaptation.A low-speed and heavy-loaded bearing testbed was built,and fault data sets were established to validate the proposed *** experiments show that in noise domain adaptation tasks,proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%,23.088%,43.133%,16.344%,5.022%,and 9.233%over dense connection network(DenseNet),squeeze–excitation residual network(SE-ResNet),antinoise multiscale convolutional neural network(ANMSCNN),multiscale attention module-based convolutional neural network(MSAMCNN),domain adaptation network,and hybrid weighted domain adaptation(HWDA).In combined noise and working condition domain a

关键词： tunnel boring machine(TBM) main bearing fault diagnosis domain adaptation antinoise cross working RDMA-WDAN

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A novel efficient energy absorber with free inversion of a metal foam-filled circular tube

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Applied Mathematics and Mechanics(English Edition) 2024年第1期45卷 1-14页

作者： Jianxun ZHANG Jinwen BAI state key laboratory for Strength and vibration of Mechanical Structures School of Aerospace EngineeringXi'an Jiaotong UniversityXi'an 710049China

In this paper, a novel efficient energy absorber with free inversion of a metal foam-filled circular tube(MFFCT) is designed, and the axial compressive behavior of the MFFCT under free inversion is studied analytically and numerically. The theoretical analysis reveals that the energy is mainly dissipated through the radial bending of the metal circular tube, the circumferential expansion of the metal circular tube, and the metal filled-foam compression. The principle of energy conservation is used to derive the theoretical formula for the minimum compressive force of the MFFCT over free inversion under axial loading. Furthermore, the free inversion deformation characteristics of the MFFCT are analyzed numerically. The theoretical steady values are found to be in good agreement with the results of the finite element(FE) analysis. The effects of the average diameter of the metal tube, the wall thickness of the metal tube, and the filled-foam strength on the free inversion deformation of the MFFCT are considered. It is observed that in the steady deformation stage, the load-carrying and energy-absorbing capacities of the MFFCT increase with the increase in the average diameter of the metal tube, the wall thickness of the metal tube, or the filled-foam strength. The specific energy absorption(SEA) of free inversion of the MFFCT is significantly higher than that of the metal tube alone.

关键词： metal foam-filled circular tube(MFFCT) free inversion load-carrying capacity energy absorption

Enhanced Flexoelectricity in Barium Titanate-Cellulose Composite Thin Films

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Acta Mechanica Solida Sinica 2024年第4期37卷 521-527页

作者： Wensi Xing Hongyu Cao Xin Zhang Xu Liang Jianwei Song Shengping Shen Chemomechanics and Intelligent Media laboratory State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of Aerospace EngineeringXi’an Jiaotong UniversityXi’an710049China

Biopolymers,the potential flexoelectric materials,are environment-friendly,degradable,lightweight,cost-effective,and possess remarkable processing properties catering to the requirements of advanced ***,the flexoelectric coefficient of biopolymers is normally much weaker than that of ceramic materials,limiting their potential applications for designing high-performance green electromechanical coupling *** improve the flexoelectric response in biopolymers,we composited barium titanate(BTO)with 2,2,6,6-tetramethylpiperidine-1-oxyl-oxidized cellulose nanofibrils(TOCNF)to enhance the flexoelectric response of *** to the high permittivity and flexoelectric effect of BTO,the relative dielectric constant and flexoelectric coefficient of 33.3 wt%BTO-TOCNF films reached 30.94@1 kHz and 50.05±1.88 nC/m@1 Hz,which were almost 172 times and 27 times higher than those of TOCNF,*** composite thin film contains high dielectric constant and flexoelectric coefficient,as well as excellent *** study provided a straightforward and efficient method for improving the flexoelectric effect of biopolymers,and demonstrated its great potential applications in flexoelectric-based devices.

关键词： Flexoelectricity Cellulose BaTiO_(3) Composite Thin films

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

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Science China(Technological Sciences) 2024年第2期67卷 395-407页

作者： ZHANG JieXin NIE PingYun ZHANG Bo state key laboratory of Mechanical System and vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai200240China

Passivity-based controllers are widely used to facilitate physical interaction between humans and elastic joint robots,as they enhance the stability of the interaction ***,the joint position tracking performance can be limited by the structures of these controllers when the system is faced with uncertainties and rough high-order system state measurements(such as joint accelerations and jerks).This study presents a variable structure passivity(VSP)control method for joint position tracking of elastic joint robots,which combines the advantages of passive control and variable structure *** method ensures the tracking error converges in a finite time,even when the system faces *** method also preserves the passivity of the ***,a cascaded observer,called CHOSSO,is also proposed to accurately estimate high-order system states,relying only on position and velocity *** observer allows independent implementation of disturbance compensation in the acceleration and jerk estimation *** particular,the observer has an enhanced ability to handle fast time-varying disturbances in physical human-robot *** effectiveness of the proposed method is verified through simulations and experiments on a lower limb rehabilitation robot equipped with elastic joints.

关键词： motion control elastic joint robot finite-time convergence high-order system state estimation physical human-robot interaction

A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

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Applied Mathematics and Mechanics(English Edition) 2024年第10期45卷 1685-1704页

作者： Jing'ang ZHU Yiheng XUE Zishun LIU International Center for Applied Mechanics State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace EngineeringXi'an Jiaotong University

Soft materials, with the sensitivity to various external stimuli, exhibit high flexibility and stretchability. Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters. However,identifying multiple parameters under complex deformations remains a challenge, especially with limited observed data. In this study, we develop a physics-informed neural network(PINN) framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels. To improve accuracy, we utilize scaling techniques to normalize network outputs and material parameters. This framework effectively solves forward and inverse problems, extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical *** explore different approaches for imposing boundary conditions(BCs) to assess their impacts on accuracy. To enhance efficiency and generalization, we propose a transfer learning enhanced PINN(TL-PINN), allowing pre-trained networks to quickly adapt to new scenarios. The TL-PINN significantly reduces computational costs while maintaining accuracy. This work holds promise in addressing practical challenges in soft material science, and provides insights into soft material mechanics with state-of-the-art experimental methods.

关键词： 35Q74 74A20 74B20 74G75 inverse problem O242 O343 parameter identification physics-informed neural network (PINN) soft material transfer learning

A novel multi-channel CNN-LSTM and transformer-based network for diesel engine misfire diagnosis under different noise conditions

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Science China(Technological Sciences) 2024年第9期67卷 2965-2967页

作者： LI Ou LEI JunBo QIN ChengJin ZHANG ZhiNan TAO JianFeng LIU ChengLiang state key laboratory of Mechanical System and vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Misfire fault, as a major fault of diesel engines, usually occurs because of low fuel quality, insufficient compression, ignition system failure, *** engines may experience issues such as reduced power, increased fuel consumption, and increased noise when misfire happens. Therefore, timely detection and diagnosis of diesel engine misfire faults are of great significance in the industry [1].

关键词： Diesel diagnosis network

Multi-blade rubbing characteristics of the shaft-disk-blade-casing system with large rotation

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Applied Mathematics and Mechanics(English Edition) 2024年第1期45卷 111-136页

作者： Zhiyuan WU Linchuan ZHAO Han YAN Ge YAN Ao CHEN Wenming ZHANG state key laboratory of Mechanical System and vibration Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Systems Engineering Shanghai 201108China

Blade rubbing faults cause detrimental impact on the operation of aeroengines. Most of the existing studies on blade rubbing in the shaft-disk-blade-casing(SDBC) system have overlooked the elastic deformation of the blade, while some only consider the whirl of the rotor, neglecting its spin. To address these limitations, this paper proposes a dynamic model with large rotation for the SDBC system. The model incorporates the spin and whirl of the rotor, enabling the realistic reproduction of multiblade rubbing faults. To verify the accuracy of the SDBC model with large rotation and demonstrate its capability to effectively consider the rotational effects such as the centrifugal stiffening and gyroscopic effects, the natural characteristics and dynamic responses of the proposed model are compared with those obtained from reported research and experimental results. Furthermore, the effects of the rotating speed, contact stiffness,and blade number on the dynamic characteristics of the SDBC system with multi-blade rubbing are investigated. The results indicate that the phase angle between the rotor deflection and the unbalance excitation force increases with the increasing rotating speed,which significantly influences the rubbing penetration of each blade. The natural frequency of the SDBC system with rubbing constrain can be observed in the acceleration response of the casing and the torsional response of the shaft, and the frequency is related to the contact stiffness. Moreover, the vibration amplitude increases significantly with the product of the blade number under rubbing, and the rotating frequency approaches the natural frequency of the SDBC system. The proposed model can provide valuable insight for the fault diagnosis of rubbing in bladed rotating machinery.

关键词： shaft-disk-blade-casing(SDBC) large rotation spin and whirl multi-blade rubbing rotational effect

Unsupervised construction of health indicator for rotating machinery via multi-criterion feature selection and attentive variational autoencoder

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Science China(Technological Sciences) 2024年第5期67卷 1524-1537页

作者： LI XinYu CHENG ChangMing PENG ZhiKe state key laboratory of Mechanical System and vibration Shanghai Jiao Tong UniversityShanghai200240China School of Mechanical Engineering Ningxia UniversityYinchuan750021China

Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating *** interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial ***,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling *** tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features ***,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is *** Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of *** case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation *** effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.

关键词： health indicator(HI) unsupervised learning multi-criterion feature selection global variability attention mechanism