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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Tianbo Kang Mian Zhang Ruitong Xie Jiwei Chen Hongbiao Xiang Yuhang Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology) Tianjin China

Planetary gearboxes (PGs) are the core transmission components of complex rotating machinery. The diverse forms of internal gear faults often complicate diagnosis and maintenance work. To improve the fault diagnosis accuracy of PGs, this paper proposes a method for diagnosing PG faults based on deep autoencoders (DAE) and convolutional neural networks (CNN). The method fully utilizes the deep learning capability of DAE and combines it with the precise recognition ability of CNN for fault features. Through precise learning in the encoding layer, it achieves deep dimensionality reduction of fault features and further learns these features after dimensionality reduction by CNN. Compared to traditional CNN, the method reduces the computation parameters and resource consumption, resulting in higher precision intelligent fault diagnosis. Experimental results demonstrate that DAE-CNN exhibits stronger fault diagnosis ability than traditional networks.

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Superbroad-band actively tunable acoustic metamaterials driven from poly(ethylene terephthalate)/carbon nanotube nanocomposite membranes

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Nano Research 2021年第1期14卷 100-107页

作者： Ying Li Wen Ning Qingyu Peng Minglong Yang Dongyi Lei Siyao Guo Peng Liu Kaili Jiang Xiaodong He Yibin Li School of Civil Engineering Qingdao University of TechnologyQingdao 266033China National Key Laboratory of Science and Technology for National Defence on Advanced Composites in special Environments Harbin Institute of TechnologyHarbin 150080China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics&Tsinghua-Foxconn Nanotechnology Research CenterTsinghua University&Collaborative Innovation Center of Quantum MatterBeijing 100084China Shenzhen STRONG Advanced Materials Institute Ltd.Corp Shenzhen 518000China

Actively tunable acoustic metamaterials have attracted ever increasing ***,their tunable frequency range is quite narrow(tens of Hz)even under ultrahigh applied voltage(about 1,000 V).Here,we report a superbroad-band actively tunable acoustic metamaterials with the bandwidth over 400 Hz under a low *** the actively tunable acoustic metamaterials,the acoustic membrane is a laminated nanocomposite consisting of a poly(ethylene terephthalate)(PET)and super-aligned carbon nanotube(CNT)drawn from CN T forest *** laminated nanocomposite membrane exhibits adjustable acoustic properties,whose modulus can be adjusted by applying external electric *** maximum frequency bandwidth of PET/CN T nanocomposite membrane reaches 419 Hz when applying an external DC voltage of 60 *** actively tunable acoustic metamaterials with superbroad-band and lightweight show very promising foreground in noise reduction applications.

关键词： poly(ethylene terephthalate)/carbon nanotube nanocomposite membrane active control acoustic metamaterials noise reduction

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An optimal design and fuzzy control method for a new flapping-wing robot

An optimal design and fuzzy control method for a new flappin...

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Electrical engineering and control science (IC2ECS), International Conference on

作者： Chunxiao Zhang Yansen Zhao Ting Zhang Qi Liu Bin Li Yue Ma Mengze Suo Changrui Li Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Bionic flapping-wing robot has the advantages of good maneuverability, low noise and strong stealth performance. It is widely used in military intelligence collection, field rescue and investigation. In this paper, an optimal design method of bionic flapping-wing robot non-phase difference driving mechanism is proposed. By setting the constraints of flapping angle amplitude, fuselage height and slider stroke, the scale parameters of the mechanism are optimized by using the minimum transmission angle; on this basis, the effectiveness of fuzzy PD control in improving the tracking accuracy of the drive system is verified by means of ADAMS and MATLAB / Simulink electromechanical coupling simulation. This research has practical value for designing new flapping-wing robot and improving its control quality.

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A Model-Data Compound Driven Method For Compensating Robot Tracking Error

A Model-Data Compound Driven Method For Compensating Robot T...

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IEEE International Conference on mechatronics and Automation

作者： Pai Peng Qi Liu Bin Li Wei Peng Xinjie Wang Jinyuan Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Industrial robots are widely used in industrial production because of their high work efficiency and high flexibility, but their low tracking accuracy makes them unable to be applied on a large scale in high-precision manufacturing. Considering the modeling error of robot, the problem of low tracking accuracy should be solved by combining model-based methods and data-based methods. Thus, a model-data compound approach is proposed for compensating robot tracking error. This method comprehensively considers two error compensation methods of model-driven and data-driven, which constructed the input matrix by using the model-based information and the data-based information. The former includes the desired position, velocity, acceleration, torque and friction torque; and the latter includes the actual torque. For the purpose of predicting the correlation between the joint error and the historical joint motion state, the historical information of the joint motion is also added in the input matrix for improving the error prediction accuracy. Then, a convolutional neural network prediction model is constructed for predicting and compensating the tracking error of each actuated joint. The experimental results show that the proposed method has good prediction accuracy and compensation effect. Compared to joint control without using the proposed method, the root mean square of joint tracking errors was reduced by up to more than 69%.

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A review for design,mechanism,fabrication,and application of magnetically responsive microstructured functional surface

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International Journal of Extreme Manufacturing 2025年第1期7卷 96-128页

作者： Jian Wang Xingyi Song Chaochao Wang Yumei Zhou Ri Chen Yong Yang Bin Liu Yihao Zheng Hui Li Wei Zhou Lelun Jiang Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument School of Biomedical EngineeringShenzhen Campus of Sun Yat-sen UniversityShenzhen 518107People’s Republic of China School of Mechatronic Engineering Guangdong Polytechnic Normal UniversityGuangzhou 510665People’s Republic of China School of Mechanical and Electrical Engineering Guangdong Polytechnic of Industry and CommerceGuangzhou 510550People’s Republic of China Mechanical&Materials Engineering Department Worcester Polytechnic InstituteWorcesterMA 01609United States of America Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen 518060People’s Republic of China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361005People’s Republic of China

Magnetically responsive microstructured functional surface(MRMFS),capable of dynamically and reversibly switching the surface topography under magnetic actuation,provides a wireless,noninvasive,and instantaneous way to accurately control the microscale engineered *** the last decade,many studies have been conducted to design and optimize MRMFSs for diverse applications,and significant progress has been *** review comprehensively presents recent advancements and the potential prospects in *** first classify MRMFSs into one-dimensional linear array MRMFSs,two-dimensional planar array MRMFSs,and dynamic self-assembly MRMFSs based on their ***,an overview of three deformation mechanisms,including magnetically actuated bending deformation,magnetically driven rotational deformation,and magnetically induced self-assembly deformation,are *** main fabrication strategies employed to create MRMFSs are summarized,including replica molding,magnetization-induced self-assembly,laser cutting,and ferrofluid-infused ***,the applications of MRMFS in droplet manipulation,solid transport,information encryption,light manipulation,triboelectric nanogenerators,and soft robotics are ***,the challenges that limit the practical applications of MRMFSs are discussed,and the future development of MRMFSs is proposed.

关键词： functional surfaces microstructures magnetic actuation manipulation

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Automated Creation of Topological Structure Models of Parallel Mechanisms for Kinematic Performance Evaluation

Automated Creation of Topological Structure Models of Parall...

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International Conference on Advanced Robotics and mechatronics (ICARM)

作者： Hongye Wu Ping Lin Yue Ma Bin Li Qi Liu Hongqing Li Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

Considering the diversity of topological structures of parallel mechanisms (PMs), this paper presents an approach for the automated generation of topological structure models based on CAD technology. In this method, joints and links are treated as fundamental elements in the skeleton model of a mechanical system, leading to the development of a library of fundamental components readily available to compose a variety of limbs and PMs. A connection graph and its data structure are then developed for describing, in a hierarchical manner, the assembly information among the fundamental components. Equipped with the knowledge and information mentioned above, the limbs and PMs can finally be assembled automatically by identifying and imposing the geometrical constraints that have been already embedded in the models. The merit of this method lies in that 3D schematic diagrams (termed topological structure models) of various limbs and PMs can be created in an illustrative manner by properly selecting a set of fundamental components and then setting their connections, leading to the digitalized and visible topological structure models that are readily for kinematic performance evaluation. Combined with the CAD-CAE integration technology for the mechanical structure design and static/dynamic performance evaluation, a digital design and simulation framework can be formulated for the rapid design of parallel robotic equipment.

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Research on Behavioral Decision Reward Mechanism of Unmanned Swarm System in Confrontational Environment 7th

Research on Behavioral Decision Reward Mechanism of Unmanned...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zhang, Tingting Lan, Yushi Song, Aiguo School of Command and Control Engineering Army Engineering University Nanjing210017 China The 28th Research Institute of China Electronics Technology Group Corporation National Defence Science and Technology Key Laboratory Nanjing210017 China School of Instrumental Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9783030786144

Unmanned swarm system is regarded as a multi-agent system. At present, many kinds of research use deep reinforcement learning algorithms to solve the problem of multi-agent autonomous behavioral decision. The multi-agent deep deterministic policy gradient (MADDPG) algorithm proposed by Google’s DeepMind team pioneered the application of multiple agents in collaborating and confronting complex scenarios, and can be used to solve the problem of generating an autonomous strategy for unmanned swarm in continuous action space. Experiments show that this algorithm has an unsatisfactory convergence and stability in a competitive environment. The key to multi-agent strategy optimization is the design of the reward function. Thus, the paper improves the algorithm of the reward model for multi-agent behaviors in the confrontational environment. First, the advantages of the MADDPG algorithm for implementing multi-agent behavioral decision in a confrontational environment and the training and execution framework of the reward function are introduced. Second, a per-distance reward mechanism and the addition of distance parameters are proposed to improve the efficiency of the agents’ behavioral decision and solve the delayed reward problem. The global sharing value reward strategy is changed to the experience pool sharing value reward strategy to avoid the phenomenon of lazy agents and improve the efficiency of the agents’ cooperation. Finally, after placing the improved reward mechanism into the MADDPG algorithm, experimental results show that the improved reward function has improved convergence, the algorithm training cycle is shortened, the function curve stabilizes faster, and multiple agents have better performance in the confrontational environment. This paper provides reference methods for intelligent behavioral decisions of the unmanned swarm system. © 2021, Springer Nature Switzerland AG.

关键词： Efficiency

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Trajectory Optimization and control Method Based on Multistage Nonlinear Model Predictive control

Trajectory Optimization and Control Method Based on Multista...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Qingchun Zheng Zhi Peng Peihao Zhu Ran Zhai Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

To improve the trajectory optimization efficiency of robot manipulators, this paper proposes a novel approach for trajectory optimization and control of manipulators based on multistage nonlinear model predictive control (msNLMPC). This approach is realized by using a neural state space model as the prediction model of nonlinear model predictive control (NLMPC). The simulation results show that the actual trajectory of the manipulator in our proposed scheme coincides with the desired trajectory. The manipulator achieves the control task of following the optimal trajectory. Our proposed method improves the trajectory optimization efficiency of the robot manipulator.

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Modulation of Ti2Ni morphology in laser powder bed fused Ni-Ti alloys for excellent shape memory effect

SSRN

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SSRN 2024年

作者： Ma, Bowen Lu, Haizhou Li, Jiayin Chen, Dongxu Jiang, Yuchuan Yang, Chao Li, Dongdong Zhang, Yuping State Key Laboratory for Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan China School of Mechatronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China National Engineering Research Center of Near-net-shape Forming for Metallic Materials South China University of Technology Guangzhou510640 China School of Chemistry and Chemical Engineering Huazhong University of Science and Technology Wuhan China

NiTi alloy produced by laser powder bed fusion (LPBF) is often limited by uncontrolled non-equilibrium solidification, which generates non-uniform tissue such as Ti2Ni with irreversible effects on its mechanical and functional properties. To address this challenge, a layer-alternating LPBF (ALT-LPBF) method that programs hs changes along the building height was proposed for the first time. ALT-LPBF enables directional control over the morphology of Ti2Ni (from spherical to sub-ellipsoidal) by influencing inhomogeneous residual stress. Precise testing of the compositional fields within the melting pool and grains revealed that both peritectic and eutectic reactions are involved in Ti2Ni development, contributing to a deeper understanding of the mechanisms governing Ti2Ni formation during ALT-LPBF. Additionally, spherical Ti2Ni forms a coherent interface with B2 matrix, reducing stress concentration and promoting work hardening, through an elastic strain field. In contrast, sub-ellipsoidal Ti2Ni forms an incoherent interface, intensifying the stress field, promoting dislocation accumulation and inhibiting plastic deformation. The combination of spherical and sub-ellipsoidal Ti2Ni induced by ALT-LPBF enhances ultimate tensile strength to 850.6 MPa and shape memory effect 98.8%. This work lays the foundation for achieving ideal shape memory effect through ALT-LPBF and expands biomedical devices and smart materials applications. © 2024, The Authors. All rights reserved.

关键词： Tensile testing

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Design of Programmable Droplet Manipulation Platform Based on Magnetic control 12

Design of Programmable Droplet Manipulation Platform Based o...

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12th International Conference on CYBER technology in Automation, control, and Intelligent Systems, CYBER 2022

作者： Zhang, Xianmiao Liu, Jie Liu, Jiying Wang, Yuzhe Zhang, Mian Xiang, Hongbiao School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China Hong Kong Baptist University United International College Division of Culture and Creativity Beijing Normal University Guangdong Zhuhai519087 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Droplet actuation simplifies the handling of various reagents or samples and can be applied to a wide range of fields, including chemistry, biology, biomedical, and others. This paper presents a programmable droplet control system based on a magnetoelastic membrane and electromagnetic pillar array. Different magnetic blocks with different magnetization directions were designed on the silicone rubber membrane, and the magnetoelastic membrane deformed under the magnetic field generated by the array of electromagnetic pillars. By combining the gravitational forces of the droplet and the deformation of the magnetic membranes, the motion of the droplet can be controlled. Furthermore, the surface of membranes was ablated with a laser machine to impart superhydrophobic properties. The simulation results show that with the different magnetic fields, the droplet can move lengthwise, widthwise, and diagonally in the horizontal plane, and multiple droplets can be merged and mixed. In contrast to the traditional droplet control method, the droplet programmable movement control system utilizing superhydrophobic magnetoelastic membranes and an electromagnetic pillar array has better stationarity, flexibility and does not affect the basic properties of the droplets. © 2022 IEEE.

关键词： Silicones

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