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A combined experimental and analytical method to determine the EHL friction force distribution between rollers and outer raceway in a cylindrical roller bearing

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Friction 2023年第8期11卷 1455-1469页

作者： Yu HOU Xi WANG Di YANG Zeliang XIAO School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-performance Mechanical Equipment Changsha University of Science and TechnologyChangsha 410114China

Friction force is a crucial factor causing power loss and fatigue spalling of rolling element bearings.A combined experimental and analytical method is proposed to quantitatively determine the elastohydrodynamic lubrication(EHL)friction force distribution between rollers and outer raceway in a cylindrical roller bearing(CRB).An experimental system with the instrumented bearing and housing was developed for measuring radial load distribution and friction torque of bearings.A simplified model of friction force expressed by dimensionless speed,load,and material parameters was *** inequality constrained optimization problem was established and solved by using an experimental data-driven learning algorithm for determining the uncertain parameters in the *** effect of speed,load,and lubricant property on friction force and friction coefficient was discussed.

关键词： cylindrical roller bearing(CRB) elastohydrodynamic lubrication(EHL) friction force distribution friction torque combined method

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Controllable optical bistability in a Fabry-Pérot cavity with a nonlinear three-dimensional Dirac semimetal

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Chinese Physics B 2024年第3期33卷 441-446页

作者：袁红霞李佳雪马奇军田海山叶云洋罗文昕吴杏华蒋乐勇 School of Physics and Electronics Hunan Normal UniversityChangsha 410081China School of Intelligent Manufacturing and Electronic Engineering Wenzhou University of TechnologyWenzhou 325035China Key Laboratory for Microstructural Functional Materials of Jiangxi Province College of ScienceJiujiang UniversityJiujiang 332005China

Optical bistability(OB)is capable of rapidly and reversibly transforming a parameter of an optical signal from one state to another,and homologous nonlinear optical bistable devices are core components of high-speed all-optical communication and all-optical *** this paper,we theoretically investigated the controllable OB from a Fabry-Pérot(FP)cavity with a nonlinear three-dimensional Dirac semimetal(3D DSM)in the terahertz *** OB stems from the third-order nonlinear bulk conductivity of the 3D DSM and the resonance mode has a positive effect on the generation of *** FP cavity structure is able to tune the OB because the transmittance and the reflectance can be modulated by the Fermi energy of the 3D *** believe that this FP cavity configuration could provide a reference concept for realizing tunable bistable devices.

关键词： optical bistability Dirac semimetal Fabry-Pérot cavity

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Fault Diagnosis of Bearing with Small Sample based on Siamese Networks and Metric Learning 3

Fault Diagnosis of Bearing with Small Sample based on Siames...

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3rd International Conference on electronic Informationtechnology and Smart Agriculture, ICEITSA 2023

作者： Wang, Qingnan Tu, Jihui School of Intelligent Manufacturing Huaihua University Hunan Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua University Huaihua China School of Electronic and Information Yangtze University Jingzhou China

ISBN: (纸本)9798400716775

Bearing faults, characterized by their low probability occurrence and limited sample availability, present challenges in accurate diagnosis. Traditional data-driven diagnostic methods exhibit reduced accuracy and generalization in small datasets. This paper presents a Siamese networks model based on metric learning, which is used for bearing faults classification. Vibration signals are preprocessed using continuous wavelet transform (CWT), and features are extracted through the MobileNetV3 backbone network. The model employs Euclidean distance measurement for training and classification, demonstrating high accuracy under limited sample data. © 2023 ACM.

关键词： Failure analysis

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FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZATION UNDER RAINY CONDITION

FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZA...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Xinda Cheng, Xu Ju, Xinjie Wang, Xuesong Li, Jianbing College of Electronic Science and Technology National University of Defense Technology Hunan Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Hunan Changsha410073 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan430205 China

ISBN: (纸本)9781839539954

Polarization and waveform optimization can significantly improve the detection ability of fully polarized radar. However, in practical scenarios, atmospheric particles frequently induce depolarization effects on radar waves, thereby influencing precise target detection, a phenomenon predominantly observed with raindrop particles. This paper establishes a target detection model for fully polarized radar in rainy condition. Subsequently, a joint transceiver iterative optimization algorithm is devised, utilizing the maximum output signal-to-interference plus noise ratio (SINR) of the system as the benchmark, and imposing dual constraints of energy and resemblance on the transmitting waveform. Ultimately, the effectiveness of the proposed method under rainy condition is validated through simulation, and recommendations for engineering applications are provided based on the analysis. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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Two-Stage Hand-Eye Calibration based on Variance Minimization Principle

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ding, Tao Tian, Dazhuang Wu, Hao Zhu, Dahu Li, Wenlog Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive Engineering Wuhan430070 China Wuhan University of Technology Hubei Digital Manufacturing Key Laboratory School of Mechanical and Electronic Engineering Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Provincial Engineering Research Center of Robot and Intelligent Manufacturing Wuhan430070 China Huazhong University of Science and Technology State Key Laboratory of Intelligent Manufacturing Equipment and Technology Wuhan430074 China

Hand-eye calibration in industrial scenarios greatly determines the efficiency and effectiveness of 3D vision-guided robotic tasks. Most of the existing hand-eye calibration methods, however, rely on collecting large-scale point cloud samples to guarantee high measurement accuracy, which is time-consuming and particularly influenced by measurement defects such as local missing, noise, and uneven density. To overcome this challenging problem, we present a novel two-stage hand-eye calibration method based on the variance minimization principle. In the first stage, a robust function weighted variance minimization (RFWVM) registration algorithm is proposed for fine registration of defective point clouds, aiming at solving the coarse hand-eye calibration matrix with few-shot point clouds. In the second stage, an objective function of the calibration error correction algorithm is established via the point-to-plane distance variance minimization, which is intended to suppress the influence of measurement defects. Through these two stages, the globally optimal hand-eye matrix is solved by iteratively correcting the coarse hand-eye calibration matrix. A series of experiments validate the universality, accuracy, and robustness of the proposed method. © 1963-2012 IEEE.

关键词： Error correction

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Additive manufacturing of heat-resistant aluminum alloys: a review

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International Journal of Extreme manufacturing 2024年第6期6卷 449-504页

作者： Chaoqun Wu Jianyu Wen Jinliang Zhang Bo Song Yusheng Shi Hubei Province Engineering Research Center of Robot&Intelligent Manufacturing School of Mechanical and Electronic EngineeringWuhan University of TechnologyWuhan 430070People's Republic of China State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China

The capability for synergistic advancements in both making and shaping afforded by additive manufacturing(AM) enables the flexible production of high-performance components. Boosted by the growing demand for heat-resistant aluminum alloys in the moderate-temperature weight-critical applications, AM of heat-resistant aluminum alloys constitutes a burgeoning field. Although numerous advances have emerged in recent years, there remains a gap in the review literature elucidating the newly-developed alloy systems and critically evaluating the efficacy. This state-of-the-art review presents a detailed overview of recent achievements on the heat-resistant aluminum alloy development. It begins with the introduction of various AM technologies and the pros and cons of each technique are evaluated. The enhancement mechanisms associated with printability and high-temperature properties of AM aluminum alloys are then delineated. Thereafter, the various additively manufactured aluminum alloy systems are discussed with regard to the microstructure, heat resistance and high-temperature performance. An emphasis is put on the powder bed fusion-laser beam(PBF-LB) as it has garnered significant attention for heat-resistant aluminum alloys and the vast majority of the current studies are based on this technique. Finally, perspectives are outlined to provide guidance for future research.

关键词： additive manufacturing heat-resistant aluminum alloy alloy development microstructure high-temperature mechanical properties

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Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ouyang, Wenjie Jiao, Yiwen Liu, Yang Li, Yang Hu, Manjiang Qin, Hongmao College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft Netherlands College of Mechanical and Vehicle Engineering Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China

ISBN: (纸本)9798350316308

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the 'long-tail' issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16-72% reward improvement compared to the tree model in different road length. © 2023 IEEE.

关键词： Reinforcement learning

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An Automation Analog IC Parameter Optimizing Design Method Based on Bayesian Optimization 23

An Automation Analog IC Parameter Optimizing Design Method B...

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Proceedings of the 3rd International Conference on electronic Information technology and Smart Agriculture

作者： Xianming Liu Xianwu Mi Yujun Song School of Physics Electronics and Intelligent Manufacturing Huaihua University China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University China and The School of Electronic Science and Engineering Xiamen University China School of Physics Electronics and Intelligent Manufacturing Huaihua University China and Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University China

ISBN: (纸本)9798400716775

The machine learning approach improves design efficiency and reduces design time by simulating automated sizing and placement of integrated circuits ( ICs), but the complexity of solving a strict constraint is not neglected. This paper proposed an automation analog IC parameter design framework based on Bayesian optimization (BO). The BO searches the global optimal design variables, which benefits dealing with multiple constraints optimization problems. To guarantees all the transistor works in normal conditions, the constraint test is introduced during the sampling procedure. At the local search phase, the Gaussian processes (GP) surrogate-based model predicts the circuit's performance instead of SPICE simulation. The BO combined GP approach is friendly to finding the optimal design. The proposed algorithm can handle multi-objective optimization problems. The experiments use a two-stage operational amplifier (Op-amp) and biased-based folded cascode CMOS operational amplifier to test the performance of the algorithm. The experiment results show that the proposed method can speed up the optimization process and perform better than the state-of-the-art surrogate-based optimization method.

关键词： Bayesian Optimization Gaussian Processes Local Optimization Parameter Optimizing Design

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Fault Diagnosis of Bearing with Small Sample based on Siamese Networks and Metric Learning 23

Fault Diagnosis of Bearing with Small Sample based on Siames...

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Proceedings of the 3rd International Conference on electronic Information technology and Smart Agriculture

作者： Qingnan Wang Jihui Tu School of physics Electronics and Intelligent Manufacturing Huaihua University China and Hunan Province Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture China Electronic Information School Yangtze University China

ISBN: (纸本)9798400716775

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Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation

arXiv

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arXiv 2024年

作者： Zhou, Wenlve Zhou, Zhiheng Wang, Tianlei Zeng, Delu School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou510641 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510641 China The Department of Intelligent Manufacturing Wuyi University Jiangmen529020 China

Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. In the context of domain adaptive semantic segmentation, which tackles UDA for dense prediction, the goal is to circumvent the need for costly pixel-level annotations. Typically, various prevailing methods baseline rely on constructing intermediate domains via cross-domain mixed sampling techniques to mitigate the performance decline caused by domain gaps. However, such approaches generate synthetic data that diverge from real-world distributions, potentially leading the model astray from the true target distribution. To address this challenge, we propose a novel auxiliary task called Guidance Training. This task facilitates the effective utilization of cross-domain mixed sampling techniques while mitigating distribution shifts from the real world. Specifically, Guidance Training guides the model to extract and reconstruct the target-domain feature distribution from mixed data, followed by decoding the reconstructed target-domain features to make pseudo-label predictions. Importantly, integrating Guidance Training incurs minimal training overhead and imposes no additional inference burden. We demonstrate the efficacy of our approach by integrating it with existing methods, consistently improving performance. The implementation will be available at https://***/Wenlve-Zhou/Guidance-Training. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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