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Mechanical Properties of 3d-Printed Biomimetic Nacreous Composites: Experiments and Theory

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SSRN 2024年

作者： Yan, Yi Zhao, Zheng-Ze Li, Jia Gao, Yuan Feng, Xi-Qiao Deng, Zi-Chen Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Institute of Biomechanics and Medical Engineering AML Department of Engineering Mechanics Tsinghua University Beijing100084 China School of Mechanical Engineering Hebei University of Technology Tianjin300401 China

Nacre exhibits exceptional mechanical properties, which are attributed to its brick–mortar microstructure with an integration of stiff mineral platelets and soft organic interfaces. The rapidly developing 3D printing technique has been used to make nacre-inspired composites with similar brick–mortar structure. To date, however, there is still lack of a theoretical model to exactly predict the entire deformation and failure process of biomimetic nacreous composites consisting of various constituent phases with different geometric sizes. Based on a mesomechanical theoretical model, we derive the stress–strain response and macroscopic strength of the brick–mortar structure under uniaxial tension. The brick–mortar structure shows three typical failure modes, according to the occurrence of strain hardening and platelet fracture. Furthermore, we investigate how the mechanical properties of the brick–mortar structure depends on its geometry and constituent properties. It is found that increasing the aspect ratio of the platelets and the stiffness of the soft phase both enhance the strength of the brick–mortar structure. However, increasing the aspect ratio of the platelets promotes strain hardening, while increasing the stiffness of the soft phase leads to the disappearance of strain hardening. Finally, we utilize bi-material 3D printing technology to prepare biomimetic nacre samples and conduct uniaxial tensile mechanical tests, confirming the guiding significance of the theoretical model for the design of 3D-printed biomimetic nacreous composites. © 2024, The Authors. All rights reserved.

关键词： Biomimetics

Symplectic Stiffness Method for the Buckling Analysis of Hierarchical and Chiral Cellular Honeycomb Structures

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SSRN 2023年

作者： Jin, Mingzhu Hou, Xiuhui Zhao, Wenhao Deng, Zichen School of Aeronautics Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China

Structure-function-material integration design based on the buckling instability of flexible cellular structures has attracted much attention. According to the reversible and repeatable characteristics of buckling deformation, phonon bandgap switches, the autonomous drive of soft robots, programmable logic controllers, and reusable spacecraft landing buffer devices can be designed. Based on the Hamiltonian variational principle and symplectic eigen expansion, a theoretical analysis model of the symplectic stiffness method is established, and analytical expressions for the stiffness of several typical buckling modes of elastically supported beams are obtained. The analytical closed-form expressions for the macroscopic buckling strength of honeycombs are obtained according to the bending moment equilibrium equation. The buckling modes of regular hexagonal, hierarchical hexagonal, and tri-chiral honeycombs are obtained by numerical simulation and experimental verification, and some novel higher-order modes are observed. Uniaxial compression experiments reveal that these higher-order buckling modes can be triggered by adjusting the geometric parameters of the unit cells. The high agreement between experimental, numerical, and existing literature results for the failure surfaces of cellular structures verifies the effectiveness of the proposed symplectic stiffness method. In a word, the buckling modes can be designed to meet different functional requirements by changing the initial configuration of flexible cellular structures. This provides a new idea and theoretical guidance for the development of metamaterials and structures with tailored properties. © 2023, The Authors. All rights reserved.

关键词： Stiffness

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

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Chinese Journal of Aeronautics 2020年第2期33卷 427-438页

作者： Yongbo LI Xiaoqiang DU Fangyi WAN Xianzhi WANG Huangchao YU MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an 710072China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China Institute of Unmanned System National University of Defense TechnologyChangsha 410073China

Rotating machinery is widely applied in industrial *** diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial ***,Deep Learning(DL)-based fault diagnosis method becomes a hot *** Neural Network(CNN)is an effective DL method to extract the features of raw data *** paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT)***,IRT technique is utilized to capture the IRT images of rotating ***,the CNN is applied to extract fault features from the IRT *** the end,the obtained features are fed into the Softmax Regression(SR)classifier for fault pattern *** effectiveness of the proposed method is validated using two different experimental *** show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.

关键词： Convolutional neural network Feature extraction Infrared thermography(IRT) Intelligent fault diagnosis Rotating machinery

Adaptive Backward Stepwise Selection-Based Sparse Identification of Nonlinear dynamics

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SSRN 2024年

作者： Jiang, Feng Du, Lin Xue, Qing Grebogi, Celso Deng, Zichen MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China School of Mathematics and Statics Northwestern Polytechnical University Xi’an710072 China MOE Key Laboratory for Complexity Science in Aerospace Northwestern Polytechnical University Xi’an710072 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Sparse identification of nonlinear dynamics (SINDy) has seen significant advancements in recent years, whereas determining hyperparameters within the SINDy remains challenging. In this study, we propose the Adaptive Backward Stepwise Selection of SINDy (ABSS-SINDy) to directly address the above issue, offering insight into the conditions under which SINDy excels and potential areas of failure from the perspective of backward stepwise selection. We validate the improved approach through a series of numerical experiments encompassing planar dynamic and three-dimensional chaotic systems, thereby illustrating the efficacy of the ABSS-SINDy. The findings underscore the capability of ABSS-SINDy to autonomously determine optimal hyperparameters within the SINDy framework, transcending a previously implicit and limiting assumption, thus extending the application capabilities of SINDy. © 2024, The Authors. All rights reserved.

关键词： dynamics

Inverse Design of a Nurbs-Based Chiral Metamaterial Via Machine Learning for Programmable Mechanical Deformation

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SSRN 2024年

作者： Hou, Xiuhui Zhao, Wenhao Zhang, Kai Deng, Zichen School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China School of Aeronautics Northwestern Polytechnical University Xi’an710072 China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Liaoning Dalian116024 China

Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometrical design instead of the chemical composition. To make the mechanical deformation programmable, the Non-Uniform Rational B-Spline (NURBS) curves are taken to replace the traditional ligament boundaries of the chiral structure. The Neural Networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency. For the problem of finding structure configuration with specified mechanical properties, such as Young’s modulus, Poisson’s ratio or deformation, an inverse design method using the Neural Network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration. To satisfy some more complex deformation requirements, a non-homogeneous inverse design method is proposed and verified through simulation and experiments. Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials. © 2024, The Authors. All rights reserved.

关键词： Numerical methods

Open-/Closed-loop Active Learning for Data-driven Predictive control

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arXiv

arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

Biologically inspired information integration pooling module of spiking neural networks for rolling bearing fault diagnosis

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Expert systems with Applications 2025年 286卷

作者： Shi, Mingfei Jiang, Feng Li, Yongbo Du, Lin MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an710072 China Unmanned System Research Institute Northwestern Polytechnical University Xi'an710072 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi'an710072 China School of Aeronautics Northwestern Polytechnical University Xi'an710072 China School of Mathematics and Statics Northwestern Polytechnical University Xi'an710072 China MOE Key Laboratory for Complexity Science in Aerospace Northwestern Polytechnical University Xi'an710072 China

Rolling bearings constitute critical components in mechanical systems, and their effective fault diagnosis plays a vital role in ensuring operational safety and reliability. Despite substantial advancements in diagnostic methodologies, achieving high-precision, low-latency fault detection in large-scale rolling bearing datasets remains a persistent challenge. Artificial neural networks to spiking neural networks (ANN-to-SNN) conversion algorithms offer promising solutions for reducing computational costs and hardware adaptation barriers compared to conventional approaches. However, inherent information degradation during conversion processes has been demonstrated to significantly undermine model performance in terms of accuracy, stability, and interpretability. To overcome these limitations, we propose IIPooling, a novel Information-Integration biological neural mechanism-inspired Pooling module designed to enhance global structural feature extraction. The proposed framework incorporates three core neurobiological information processing principles: (1) promotion, (2) lateral inhibition, and (3) longitudinal inhibition, effectively simulating biological neuronal spiking patterns while optimally utilizing ANN activation characteristics. Additionally, we develop two adaptive learning mechanisms — Surrogate Gradient Adaptive Learning and Spiking Spatiotemporal Adaptive Learning — to implement negative feedback mechanism and adaptive parameter adjustment, thereby improving cross-conditional generalization capabilities. Comprehensive evaluations conducted on five bearing datasets (CWRU: DE/FE, PU, and Lab-collected: 6025/N205EW) demonstrate superior performance across four metrics (ACC, AUC, AUPRC, Mean-ACC). Our method achieves state-of-the-art results, notably attaining ACC values of 0.959 (CWRU-DE) and 0.861 (Lab-N205EW) in IIPooling-ST configurations, confirming its effectiveness for real-time, high-accuracy fault detection in complex operational environments. © 2025

关键词： Failure rate

Uncertainty analysis of hybrid vibration energy harvester

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Uncertainty analysis of hybrid vibration energy harvester

第八届国际振动工程会议

作者： Yu Shi Xiaxia Duan Ying Zhang School of Mathematics and Statistics Northwestern Polytechnical University MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University

Owing to various inevitable uncertain errors, the system parameter of the energy harvesting system should be treated as uncertainties in design. The influence of the uncertain system parameters on the hybrid vibration energy harvester(HVEH) is revealed by the uncertainty analysis. Firstly, the HVEH with an uncertain parameter is transformed into an equivalent deterministic system by the orthogonal polynomial approximation, and the ensemble mean response of the equivalent system is proposed to explore the dynamic behavior of stochastic HVEH. Then,the effectiveness of the orthogonal polynomial approximation is verified by numerical ***, the stochastic dynamics of the HVEH under uncertain parameters is investigated in detail through bifurcation diagrams, top Lyapunov exponent, etc. The results reveal the mechanism of the influence of random parameters on dynamic behavior and output voltage, which is significant to the design of the energy harvester.

关键词： Uncertainty analysis of hybrid vibration energy harvester

A new insight on balanced augmented Lagrangian method

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arXiv

arXiv 2021年

作者： Bai, Jianchao School of Mathematics and Statistics The MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an710129 China

Motivated by the recent work [He-Yuan, Balanced Augmented Lagrangian Method for Convex Programming, arXiv: 2108.08554v1, 19 Aug 2021], a novel Augmented Lagrangian Method (ALM) has been proposed for solving a family of convex optimization problem subject to equality or inequality constraint. This new method is also extended to solve the multi-block convex optimization problem. Preliminary and some new convergence results are established with the aid of variational analysis for the saddle point of the problem and the first-order optimality conditions of involved subproblems. © 2021, CC BY.

关键词： Convex optimization