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10th CIRP Global Web Conference on Material Aspects of Manufacturing Processes

作者： Bakopoulou, Katerina Michalos, George Mparis, Konstantinos Gkournelos, Christos Dimitropoulos, Nikos Makris, Sotiris University of Patras Department of Mechanical Engineering and Aeronautics Laboratory for Manufacturing Systems and Automation Patras26500 Greece

This paper discusses a human robot collaborative application for the automation of NDT inspection processes of large parts. The system includes a movable structure incorporating a cobot with an additional axis for higher reachability and a control system that can generate robot scanning trajectories for large surfaces. The preliminary analysis indicates that the system can undertake the monotonous and time-consuming scanning process, allowing the NDT experts to focus on the review and interpretation of scanning results. A case study in the steel production sector is used to evaluate the benefits of such systems. © 2022 Elsevier B.V.. All rights reserved.

关键词： Robot programming

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Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation

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Journal of Leather Science and engineering 2022年第1期4卷 90-110页

作者： Praveen Kumar Moganam Denis Ashok Sathia Seelan Department of Design Automation Cyber Physical Systems LaboratorySchool of Mechanical EngineeringVellore Institute of TechnologyVellore 632014India

Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.

关键词： Convolution neural networks Machine learning classifier Leather defects Multi class classification Class activation map Segmentation

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A comparative study of data-driven battery capacity estimation based on partial charging curves

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Journal of Energy Chemistry 2024年第1期88卷 409-420,I0010页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Xianggong Zhang Enhu Li Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina Wuhan Institute of Marine Electric Propulsion China State Shipbuilding Corporation LimitedWuhan 430064HubeiChina Gresgying Digital Technology Ltd. Xi’an 710086ShaanxiChina

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

关键词： Lithium-ion battery Partial charging curves Capacity estimation Data-driven Sampling frequency

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Creating a semi-opened micro-cavity ovary through sacrificial microspheres as an in vitro model for discovering the potential effect of ovarian toxic agents

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Bioactive Materials 2023年第8期26卷 216-230页

作者： Min Ye Yiran Shan Bingchuan Lu Hao Luo Binhan Li Yanmei Zhang Zixuan Wang Yuzhi Guo Liliang Ouyang Jin Gu Zhuo Xiong Ting Zhang Biomanufacturing Center Department of Mechanical EngineeringTsinghua UniversityBeijing100084China Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing Beijing100084China Biomanufacturing and Engineering Living Systems Innovation International Talents Base(111 Base)Beijing100084China MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing100084China

The bio-engineered ovary is an essential technology for treating female *** the development of relevant in vitro models could be a critical step in a drug ***,we develop a semi-opened culturing system(SOCS)strategy that maintains a 3D structure of follicles during the *** on the SOCS,we further developed micro-cavity ovary(MCO)with mouse follicles by the microsphere-templated technique,where sacrificial gelatin microspheres were mixed with photo-crosslinkable gelatin methacryloyl(GelMA)to engineer a micro-cavity niche for follicle *** semi-opened MCO could support the follicle growing to the antral stage,secreting hormones,and ovulating cumulus-oocyte complex out of the MCO without extra *** MCO-ovulated oocyte exhibits a highly similar transcriptome to the in vivo counterpart(correlation of 0.97)and can be ***,we found that a high ROS level could affect the cumulus expansion,which may result in anovulation *** damage could be rescued by melatonin,but the end of cumulus expansion was 3h earlier than anticipation,validating that MCO has the potential for investigating ovarian toxic agents in *** provide a novel approach for building an in vitro ovarian model to recapitulate ovarian functions and test chemical toxicity,suggesting it has the potential for clinical research in the future.

关键词： Bio-engineered ovary SOCS Semi-opened MCO Ovarian model Drug study

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Exploring Image Generation for UAV Change Detection

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1061-1072页

作者： Xuan Li Haibin Duan Yonglin Tian Fei-Yue Wang Peng Cheng Laboratory Shenzhen 518000China IEEE State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083 Department of Automation University of Science and Technology of ChinaHefei 230027 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense TechnologyChangsha 410073China

Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training and ***,sufficient labeled images with different imaging conditions are *** by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated *** simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection ***,we propose an image translation framework that translates simulated images to synthetic *** framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training *** experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.

关键词： Change detection computer graphics image translation simulated images synthetic images unmanned aerial vehicles(UAVs)

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A process-level LCA for evaluating the contribution of digitalization in the greening of a manufacturing system

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Procedia CIRP 2024年 126卷 904-908页

作者： Alexios Chaloulos Paolo Catti Nikolaos Nikolakis Kosmas Alexopoulos Laboratory for Manufacturing Systems & Automation Department of Mechanical Engineering and Aeronautics University of Patras Rio Campus Greece

Life Cycle Assessment (LCA) methodology is usually applied to products’ lifecycle and value chains to identify their environmental performance and identify circular economy strategies and options. As a result, different product configurations and strategies may be evaluated and compared, ensuring a positive environmental balance. However, LCA is not usually focused on a manufacturing process level. In this study, LCA is performed at a process level to assess the correlation between digitalization and green manufacturing. The GaBi professional database was used for assessing the manufacturing gate-to-gate processes.

关键词： Digitalization Green manufacturing Life cycle assessment Process

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automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial Processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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An interactive Augmented Reality based framework assisting operators in human-robot collaborative assembly operations 17

An interactive Augmented Reality based framework assisting o...

引用

17th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2023

作者： Aivaliotis, S. Papavasileiou, A. Konstantinou, C. Anastasiou, T. Gkournelos, C. Koukas, S. Makris, Sotiris Laboratory for Manufacturing Systems & Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece Netcompany-Intrasoft 2B Rue Nicolas Bove Luxembourg City1253 Luxembourg

This paper proposes an interactive Augmented Reality based framework assisting operators in Human-Robot Collaborative environments where resources collaboration is required for different tasks execution. Based on its integration with the shopfloor's Digital Twin and Robotic Operation System (ROS), this framework achieves the efficient HRC in multiple aspects namely: assembly guidance provision, robot controlling, interaction with gripping tools, safety awareness, resilience in case of robot actions failure, cyber security and quality inspection result awareness. The proposed framework is tested and validated in two case studies inspired from the automotive and white good industries. © 2024 Elsevier B.V.. All rights reserved.

关键词： Collaborative robots

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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期17卷 510-524页

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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