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Study on wear mechanism of small ball-end grinding wheel and surface integrity of complex component under high-speed machining condition

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Wear 2025年 570卷

作者： Qin, Biao Liu, Henan Cheng, Jian Tian, Jinchuan Sun, Jiangang Chen, Mingjun School of Mechatronics Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450000 China

The small ball-end fine diamond grinding wheel is well-suited for machining complex components with small curved surface structures, demonstrating high-precision and high-quality machining capabilities. By increasing the speed of the workpiece, the issue of poor grinding performance caused by the small diameter of the ball-end grinding wheel can be effectively overcome. However, this strategy inevitably increases wheel wear and affects surface integrity. To address these challenges, this paper systematically investigates the grinding characteristics of the small ball-end grinding wheel. First, grinding experiments were performed to investigate the influence of workpiece speed, feed rate, and grinding depth on the material removal rate, wheel wear, and surface integrity. Then, the wear mechanism of the grinding wheel was analyzed. The wear modes of the grinding wheel at high workpiece speeds include metal adhesive wear and abrasive grain fracture, which enhance the self-sharpening of the grinding wheel while accelerating its wear. Furthermore, the surface morphology creation mechanism of fused silica under different material removal modes was revealed. Moreover, the correlation between material removal rate and surface roughness caused by wheel wear was revealed. A high-speed grinding strategy was subsequently proposed based on the relationship between the grinding characteristics and grinding parameters of the small ball-end grinding wheel under various grinding conditions. Finally, the feasibility of this grinding strategy was validated through its application to the machining of the complex curved fused silica hemispherical resonator (HSR). Experimental results indicate that under the high workpiece speed condition, the best surface roughness of HSR decreases from 90.5 nm to 62.2 nm, while the maximum grinding wheel wear increases from 9.7 μm to 11.5 μm, with minimal impact on surface integrity. Therefore, the high-speed grinding strategy proposed in this study ho

关键词： Fused silica

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A Vision-Based Two-Stage Fruit Outer Diameter Detection Method

A Vision-Based Two-Stage Fruit Outer Diameter Detection Meth...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Hao, Yifei Yan, Shuaizheng Wu, Zhengxing He, Bingwei Li, Chenghua Yu, Junzhi Fuzhou University Department of Mechanical Engineering Fuzhou350108 China Institute of Automation Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Guangzhou Shenghui Cleaning Service Co. Ltd. Guangzhou511436 China BIC-ESAT College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

ISBN: (纸本)9781665481090

Regarding the mobile monitoring system in agricultural automation, the outer diameter monitoring of greenhouse vegetables has significant implications for accurately regulating fruit growth and cultivation plans. In this paper, a novel vision-based two-stage outer diameter detection method is proposed, including the segmentation and outer diameter calculation of greenhouse tomato images. To address the issues of multiple tomatoes in a single image and the similarity in color between tomatoes and the background, a YOLOv8 network embedded with convolutional block attention module is constructed for detection and segmentation in the first stage. Additionally, to solve the problem of inaccurate outer diameter detection caused by different tomato poses and pedicles, a defined central axis method is proposed, which can adaptively filter out the maximum outer diameter candidates and calculate accurate values utilizing depth information in the world coordinate system. The backbone network is fine-tuned on the pre-trained YOLOv8 model with 1000 tomato images. Extensive measurement experiments, involving 215 tomatoes in 80 images, is conducted to validate the effectiveness of our proposed approach. Compared to the ground truth, we obtain a measurement error of 4.27 mm, the coefficient of determination value R2 is 0.9349. © 2024 IEEE.

关键词： Fruits

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Bridging VLM and KMP: Enabling Fine-grained robotic manipulation via Semantic keypoints Representation

arXiv

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arXiv 2025年

作者： Zhu, Junjie Liu, Huayu Wang, Jin Wen, Bangrong Huang, Kaixiang Li, Xiaofei Zhang, Haiyun Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China School of Robotics Ningbo University of Technology Ningbo315211 China Robotics Research Center of Yuyao City Ningbo315400 China

From early Movement Primitive (MP) techniques to modern Vision-Language Models (VLMs), autonomous manipulation has remained a pivotal topic in robotics. As two extremes, VLM-based methods emphasize zero-shot and adaptive manipulation but struggle with fine-grained planning. In contrast, MP-based approaches excel in precise trajectory generalization but lack decision-making ability. To leverage the strengths of the two frameworks, we propose VL-MP, which integrates VLM with Kernelized Movement Primitives (KMP) via a low-distortion decision information transfer bridge, enabling fine-grained robotic manipulation under ambiguous situations. One key of VL-MP is the accurate representation of task decision parameters through semantic keypoints constraints, leading to more precise task parameter generation. Additionally, we introduce a local trajectory feature-enhanced KMP to support VL-MP, thereby achieving shape preservation for complex trajectories. Extensive experiments conducted in complex real-world environments validate the effectiveness of VL-MP for adaptive and fine-grained manipulation. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Dynamic Blockchain-Empowered Trustworthy End-Edge Collaborative Computing via Rotating Multi-Agent DRL

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Chi Zhang, Peifeng Yu, Haibin Li, Yonghui Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China University of Chinese Academy of Sciences Beijing China University of Sydney School of Electrical and Information Engineering Sydney Australia

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes. © 2025 Institute of El

关键词： Markov processes

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Reinforcement Learning for Dynamic Task Execution: A Robotic Arm for Playing Table Tennis

Reinforcement Learning for Dynamic Task Execution: A Robotic...

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IEEE International Conference on robotics and Biomimetics

作者： Zida Zhao Haodong Huang Shilong Sun Jing Jin Wenjie Lu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Harbin Institute of Technology Shenzhen State Key Laboratory of Robotics and Systems (HIT) Suzhou Research Institute of HIT

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Dynamic tasks like table tennis pose a significant challenge for robots, although relatively easy for humans to learn. These tasks require precise control of rapid movements and timing in situations where the estimation of the flying ball and the robot's state is imprecise. Traditional model-based control methods struggle to accurately model and adapt to these dynamic changes, leading to difficulties and challenges in practical applications. To address this issue, a table tennis hitting method for a robotic arm based on reinforcement learning (RL) is proposed in this paper. Firstly, a simulation model of a robotic arm with eight degrees of freedom for playing table tennis was constructed. Secondly, a simple and effective reward function was designed to accomplish the task of hitting the table tennis ball with the robotic arm. Finally, two sets of experiments involving single and random ball launches were designed. Experimental results demonstrate that the proposed method enables the robotic arm to accurately hit the table tennis ball, showcasing the potential of RL in dynamic tasks.

关键词： Adaptation models Sports equipment Filtering Estimation Reinforcement learning Manipulators Robustness Timing PD control Sports

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Decentralized Reinforcement Learning for Multiple Robotic Fish in Cooperative Pursuit Task

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Feng, Yukai Wu, Zhengxing Wang, Jian Li, Sijie Huang, Yupei Yu, Junzhi Tan, Min Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

The control of multi-robot systems, particularly in the pursuit-evasion (PE) with multiple robots, has gained significant attention in both academic and non-academic settings. However, the collaborative operation of multi-robotic fish systems encounters substantial challenges due to the complex underwater environment and unique movement mode. In this paper, we propose a multi-agent reinforcement learning (MARL) approach to develop a viable strategy for underwater cooperative pursuit. Initially, considering the hydrodynamic model and motion characteristics of robotic fish, we construct a specific simulation environment with multiple fish-like agents, which provides a highly realistic state transition model. Next, we develop a MARL-based strategy learning framework that incorporates appropriate reward functions and agent actions for policy learning. Finally, a series of comprehensive simulations and practical experiments are conducted to validate the effectiveness of the proposed method and confirm its successful application in underwater pursuit scenarios. These findings offer valuable insights for further research in underwater multiple robot systems. © 2016 IEEE.

关键词： Chatbots

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Multi-Objective Optimization of Electromagneticthermal Coupling Performance for High-torquedensity PMSM based on Multi-dimensional Correction Techniques

Multi-Objective Optimization of Electromagneticthermal Coupl...

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International Conference on Electrical Machines, ICEM

作者： Zequan Li Jin Wang Lu Sun Haoyu Kang Liyang Liu Libing Zhou State Key Laboratory of Advanced Electromagnetic Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China State Key Laboratory of Advanced Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Robotics and Autonomous Systems Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

To address the precision limitation of traditional thermal models and the computational inefficiency of electromagnetic-thermal coupling finite element models, this work proposes a multidimensional correction-based electromagnetic-thermal coupling design optimization method for electric machines. Firstly, this method corrects the sensitive parameters in the analytical lumped parameters thermal network model (LPNM) based on prior information from historical experimental data to ensure the accuracy for thermal performance analysis. Subsequently, to enhance computational efficiency, a large number of low-fidelity (LF) samples are obtained using the corrected thermal model in a serial electromagnetic-thermal field calculation manner, complemented by a small number of high-fidelity (HF) samples obtained from the electromagnetic-thermal coupling simulation model. Then, by employing a transfer learning technique, the data information from the low- to high-fidelity sample sets is comprehensively utilized to train a multi-fidelity (MF) surrogate model for the final optimization. The proposed method applied to a high-torque-density aerospace propulsion permanent magnet synchronous motor (PMSM) with short-term strong overload capability, it not only significantly improves the accuracy of LPNM, but also reduces the training sample acquisition time by over 50% under the same model accuracy. Finally, a prototype is tested to validate the effectiveness of the proposed method.

关键词： Couplings Accuracy Computational modeling Transfer learning Data models Thermal analysis Computational efficiency Iterative methods Optimization Design optimization

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Modelling and experimental investigation of micro-dimpled structures milling with spiral trajectory tool reciprocating motion

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Chinese Journal of Aeronautics 2025年第2期 581-600页

作者： Guangzhou WANG Linjie ZHAO Qi LIU Xiguang LI Yazhou SUN Mingjun CHEN School of Mechatronics Engineering Harbin Institute of Technology Centre for Precision Manufacturing DMEMUniversity of Strathclyde State Key Laboratory of Robotics and System Harbin Institute of Technology

To mill fine and well-defined micro-dimpled structures, a machining manner of spiral trajectory tool reciprocating motion, where the tool repeats the process of ‘feed milling±retract±cutting feed±feed milling again' along the spiral trajectory, was proposed. From the kinematics analysis, it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating motion. To reveal this causation, simulation modelling and experimental studies were carried out. A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies(constant-angle and constant-arc length) and parameters(discrete angle, discrete arc length, and pitch) on surface texture and residual height of micro-dimpled structures. Subsequently, microdimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion. A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy, surface morphology and surface roughness of milled dimples. Meanwhile, the errors and factors affecting the above three aspects were analyzed. The results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled structures. Spiral trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional surfaces. And an appropriate machining trajectory can be generated based on the optimized trajectory parameters, thus contributing to the improvement of machining quality and efficiency.

关键词：

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A Four-Quadrant Load Matching Method to Balance Power and Performance for Hydraulic Valve-Controlled Servo Actuators

SSRN

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SSRN 2024年

作者： Zhu, Qixin Yu, Bin Ba, KaiXian Sun, Qianran Kong, XiangDong Yuan, LiPeng School of Mechanical Engineering Yanshan University Qinhuangdao066004 China State Key Laboratory of Fluid Power & Mechatronic Systems Zhejiang University Hangzhou313200 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

The hydraulic valve-controlled servo actuator (HVSA) is a crucial component of high-end equipment, such as robots. And the parameters of HVSA, which are critical to its performance, are calculated using the load matching method. With the demand for high-power density in equipment, the oil return pressure and asymmetric hydraulic cylinders are used. However, the traditional load matching method faces the above changes with limitations such as low calculation accuracy, low matching efficiency, and single matching mode. So, this paper designs a four-quadrant load matching method, and contains three main tasks: (i) matching equations with more system parameters are re-derived;(ii) a four-quadrant load characteristic equivalence method is designed;(iii) a matching index is designed to flexible matching the parameters of the HVSA based on power and performance. Finally, a case results show that the HVSA calculated by this method is superior in terms of mass, energy efficiency, and driving capability. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Ma, Yongxin Xu, Jie Yuan, Shenghai Zhi, Tian Yu, Wenlu Zhou, Jun Xie, Lihua school of Mechanical Engineering Shandong University Jinan China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China

SLAM plays a crucial role in automation tasks, such as warehouse logistics, healthcare robotics, and restaurant delivery. These scenes come with various challenges, including navigating around crowds of people, dealing with flying plastic bags that can temporarily blind sensors, and addressing reduced LiDAR density caused by cooking smoke. Such scenarios can result in over-degeneracy, causing the map to drift. To address this issue, this paper presents a multi-map LiDAR-inertial system (MM-LINS) for the first time. The front-end employs an iterated error state Kalman filter for state estimation and introduces a reliable evaluation strategy for degeneracy detection. If over-degeneracy is detected, the active map will be stored into sleeping maps. Subsequently, the system continuously attempts to construct new maps using a dynamic initialization method to ensure successful initialization upon leaving the over-degeneracy. Regarding the back-end, the Scan Context descriptor is utilized to detect inter-map similarity. Upon successful recognition of a sleeping map that shares a common region with the active map, the overlapping trajectory region is utilized to constrain the positional transformation near the edge of the prior map. In response to this, a constraint-enhanced map fusion strategy is proposed to achieve high-precision positional and mapping results. Experiments have been conducted separately on both public datasets that exhibited over-degenerate conditions and in real-world environments. These tests demonstrated the effectiveness of MM-LINS in over-degeneracy environment. Our codes are open-sourced on Github. IEEE

关键词： Optical radar

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