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Designing a digitalized cell for remanufacturing of automotive frames

Procedia CIRP 2022年 109卷 513-519页

作者： Panos Stavropoulos Alexios Papacharalampopoulos Lydia Athanasopoulou Konstantinos Kampouris Panagiotis Lagios Laboratory for Manufacturing Systems and Automation (LMS) Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras 26504 Greece Gizelis Robotics Schimatari Viotias 32009 Kormatzini Area Greece

Circular economy introduction in industry is globally recognized as one of the most effective green strategies towards sustainability and resources reduction. As the automotive sector has been historically one of the biggest energy consumers, significant efforts are being constantly made to reduce the environmental impact and to increase energy efficiency at the manufacturing stage, both at production and process level. This study focuses on proposing a framework for automotive defective parts identification and remanufacturing in the main production line. In particular, the concept of an automotive microfactory is taken into consideration. To achieve this, a vision-based monitoring system performing the defect identification drives the whole procedure that is considered to be realized through a remanufacturing cell. The software tool is considered to be utilized right before the scheduling process of orders and tasks performed in the remanufacturing cell, taking into account specific performance parameters. The architecture of the software is presented along with some discussion regarding the types of control that can be enforced at both processing and line level, while the use of synthetic data is regarded to test the efficiency of the monitoring system.

关键词： Remanufacturing Automotive Vision-based monitoring Machine Learning Circular Economy

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Efficient Constrained Dynamics Algorithms based on an Equivalent LQR Formulation using Gauss’ Principle of Least Constraint

arXiv

引用

arXiv 2023年

作者： Sathya, Ajay Suresha Bruyninckx, Herman Decré, Wilm Pipeleers, Goele The Division of Robotics Automation and Mechatronics The Department of Mechanical Engineering KU Leuven Leuven Belgium DMMS-M Lab Flanders Make Leuven Belgium TU Eindhoven Netherlands Materialise NV Leuven Belgium

We derive a family of efficient constrained dynamics algorithms by formulating an equivalent linear quadratic regulator (LQR) problem using Gauss’ principle of least constraint and solving it using dynamic programming. Our approach builds upon the pioneering (but largely unknown) O(n + m2d + m3) solver by Popov and Vereshchagin (PV), where n, m and d are the number of joints, number of constraints and the kinematic tree depth respectively. We provide an expository derivation for the original PV solver and extend it to floating-base kinematic trees with constraints allowed on any link. We make new connections between the LQR’s dual Hessian and the inverse operational space inertia matrix (OSIM), permitting efficient OSIM computation, which we further accelerate using matrix inversion lemma. By generalizing the elimination ordering and accounting for MUJOCO-type soft constraints, we derive two original O(n + m) complexity solvers. Our numerical results indicate that significant simulation speed-up can be achieved for high dimensional robots like quadrupeds and humanoids using our algorithms as they scale better than the widely used O(nd2 + m2d + d2m) LTL algorithm of Featherstone. The derivation through the LQR-constrained dynamics connection can make our algorithm accessible to a wider audience and enable cross-fertilization of software and research results between the fields. © 2023, CC BY-SA.

关键词： Dynamic programming

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Tuning Evolutionary Multi-Population Models for High-Dimensional Problems: The Case of the Migration Process

SSRN

引用

SSRN 2024年

作者： Gómez-López, Juan Carlos Castillo-Secilla, Daniel González, Jesús Department of Computer Engineering Automation and Robotics CITIC University of Granada Spain Fujitsu Technology Solutions S.A. CoE Data Intelligence Camino del Cerro de los Gamos 1 Pozuelo de Alarcón Madrid Spain

Evolutionary multi-population procedures have become one of the most outstanding metaheuristics for solving problems characterized by the curse of dimensionality. A crucial aspect of these models is the migration process, defined as the exchange of individuals between subpopulations every few iterations or generations. Among the most important studies that have helped to adjust this process correctly, those conducted by E. Cantú-Paz and M. Tomassini can be highlighted. However, these works date back to 20 years ago, when the capacity to tackle problems was significantly lower than it is today. The constant increase in computational power has allowed addressing current real-life complex problems of great interest more plausibly but involving larger populations than before. With this in mind, this paper aims to study whether the guidelines outlined by Cantú-Paz and Tomassini are still valid today, where both the magnitude of the problems and the size of the population have increased considerably. In addition, it is important to consider how this adjustment affects the performance of the procedure. The results show some interesting and unexpected conclusions, where other issues, such as the number of subpopulations or their size, should be considered when fitting multi-population models. Furthermore, it appears that some of the previously mentioned classical recommendations may not be well suited for high-dimensional problems. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Creating a Dynamic Online Learning Environment with AI Integration

Creating a Dynamic Online Learning Environment with AI Integ...

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International Computer Sciences and Information Technologies (CSIT)

作者： Oleksandra Bulgakova Zarina Viniarska Viacheslav Zosimov P.M Platonov Educational and Scientific Institute of Computer Engineering Automation Robotics and Computer Programming Odesa National University of Technology Odesa Ukraine Department of Applied information systems Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs by integrating advanced AI capabilities for personalized support and adaptive learning. The platform supports user registration and authentication, course and lesson management, and offers interactive features like AI-driven chat support. In this study, we show the importance of creating user-friendly online learning platforms with the implementation of modern AI trends. The development and testing of this platform is ongoing to determine the adaptation of the system to different learning styles.

关键词： Adaptive learning Electronic learning Authentication Passwords Market research Real-time systems Artificial intelligence Information technology Standards Testing

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Investigation on Cell Orientation Induced by Various Femtosecond Laser Ablated Microstructures

Investigation on Cell Orientation Induced by Various Femtose...

引用

IEEE Conference on Nanotechnology

作者： Ye Qiu Haibo Yu Xiaoduo Wang Yuzhao Zhang Jianchen Zheng Jingang Wang Quan Gan Lianqing Liu Wen Jung Li State Key Laboratory of Robotics (SKLR) Chinese Academy of Sciences (CAS) Shenyang Institute of Automation (SIA) Institutes for Robotics and Intelligent Manufacturing (IRIM) Shenyang China University of the Chinese Academy of Sciences Beijing China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China CAS-CityU Joint Laboratory on Robotics CityU Kowloon Tong Hong Kong Department of Mechanical Engineering City University of Hong Kong (CityU) Kowloon Tong Hong Kong SIA CAS and the IRIM Shenyang China

Femtosecond laser ablation technology has been widely used in the past decade for its high precision and flexible processing capabilities in 3D micro/nano machining. This study aims to explore the application of this technology to fabricate microstructures capable of inducing cell orientation in vitro. Three different microstructures were fabricated by varying the frequency of laser pulses and implementing a moving platform that controls the motion of substrates. The results demonstrate that the stripe array structures specifically showed excellent ability in inducing cell orientation and controlling cell growth along the striation structure. This proposed method exhibits major potential as a simple yet powerful tool for investigating cellular responses to external mechanical stimuli.

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Mobile Wall-Climbing Robot for NDT inspection of vertical concrete structures

e-Journal of Nondestructive Testing

引用

e-Journal of Nondestructive Testing 2022年第9期27卷

作者： Božić, Matej Ćaran, Branimir Švaco, Marko Jerbić, Bojan Serdar, Marijana Department of Robotics and Production System Automation University of Zagreb Faculty of Mechanical Engineering and Naval Architecture Zagreb Croatia Department of Materials University of Zagreb Faculty of Civil Engineering Zagreb Croatia

Concrete structures, such as bridges or viaducts, play an important role in global road infrastructure. These types of structures are relatively expensive to build and they are susceptible to outer external influences, which in time deteriorate and lead to the reduction of their structural resistance. To reduce this effect, regular inspection is needed, which is often done manually by using specialized equipment to reach certain parts of bridges and viaducts. This process is both expensive and dangerous for the inspectors to conduct. Within the research project ASAP (Autonomous System for Assessment and Prediction of Infrastructure Integrity) in order to overcome these challenges, we have developed a prototype of a wall-climbing robot (WCR) for nondestructive testing (NDT). In this paper, different iterations of the developed WCR prototypes are presented. In four consecutive prototype designs, we have evaluated and upgraded the adhesion and locomotion system. Finally, a fifth prototype that carries the NDT equipment is presented. The final version of the WCR is equipped with robust and flexible adhesion that enables the robot to adhere to different types of surfaces. We have also addressed the challenges of integrating NDT equipment into the robot. To successfully conduct an inspection, besides the WCR, a safety system, control, and power systems are needed, which are further presented and discussed. © 2022 The Authors.

关键词： Mobile robots

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Development of IOT based Battery Management System for EV

Development of IOT based Battery Management System for EV

引用

Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Sachin P Jadhav Aabid C Mulla Sachin G Argade Swapnil S Jadhav Dushyant Patil Harshal Dattatray Vaidya Department of Electrical Engineering Dr.D.Y. Patil Institute of Technology Pimpri Pune India BITS Pilani Hyderabad Campus Department of Electrical Engineering JSPM’s Rajarshi Shahu college of Engineering Pune India Department of Automation& Robotics Dr.D.Y. Patil Institute of Technology Pimpri Pune India Department ofl Engineering Science Marathwada Mitramandal s IOT Lohgaon Pune

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Electric Vehicles in automobile sectors is one of the largest growing in World. Now peoples are facing problems in electric vehicles. Batteries generate heat during charging and discharging. Effective thermal management is crucial to prevent overheating, which can lead to reduced battery life, safety concerns, and, in extreme cases, thermal runaway. Real-time monitoring and control of temperature are essential to address thermal issues. This study underscores the significance of temperature, depth of discharge, and charging patterns concerning the prolonged deterioration of lithium-ion batteries in electric vehicles (EVs). Critical battery parameters like temperature, voltage, State of Charge (SoC), and State of Health (SoH) can be better monitored, analyzed, and controlled in real time with IOT technologies are integrated into Battery Management Systems (BMS).. By facilitating proactive measures such as real-time diagnostics and alerts, this amalgamation assists users and service providers in swiftly addressing abnormal conditions, safeguarding the battery, and extending its operational life. Additionally, the integration of IoT technology in this study facilitates efficient charging and energy management, presenting a holistic solution to enhance the durability and effectiveness of electric vehicle batteries. In this study matlab simulation with hardware implementation is carried out and results are analysed in real time using IOT.

关键词： Temperature sensors Temperature measurement Battery management systems Electric vehicles Thermal management Real-time systems Batteries State of charge Voltage control Monitoring

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VibNet: Vibration-Boosted Needle Detection in Ultrasound Images

arXiv

引用

arXiv 2024年

作者： Huang, Dianye Li, Chenyang Karlas, Angelos Chu, Xiangyu Au, K.W. Samuel Navab, Nassir Jiang, Zhongliang Technical University of Munich Germany Arcisstraße 21 Munich80333 Germany The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China The Multi-scale Medical Robotics Centre Hong Kong Technical University of Munich Germany

Precise percutaneous needle detection is crucial for ultrasound (US)-guided interventions. However, inherent limitations such as speckles, needle-like artifacts, and low resolution make it challenging to robustly detect needles, especially when their visibility is reduced or imperceptible. To address this challenge, we propose VibNet, a learning-based framework designed to enhance the robustness and accuracy of needle detection in US images by leveraging periodic vibration applied externally to the needle shafts. VibNet integrates neural Short-Time Fourier Transform and Hough Transform modules to achieve successive sub-goals, including motion feature extraction in the spatiotemporal space, frequency feature aggregation, and needle detection in the Hough space. Due to the periodic subtle vibration, the features are more robust in the frequency domain than in the image intensity domain, making VibNet more effective than traditional intensity-based methods. To demonstrate the effectiveness of VibNet, we conducted experiments on distinct ex vivo porcine and bovine tissue samples. The results obtained on porcine samples demonstrate that VibNet effectively detects needles even when their visibility is severely reduced, with a tip error of 1.61 ± 1.56 mm compared to 8.15 ± 9.98 mm for UNet and 6.63 ± 7.58 mm for WNet, and a needle direction error of 1.64±1.86◦ compared to 9.29±15.30◦ for UNet and 8.54±17.92◦ for WNet. Code: https://***/marslicy/VibNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Needles

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Empirical Evaluation of Video Surveillance based Crime and Anomaly Detection System using Hybrid Deep Learning Strategy

Empirical Evaluation of Video Surveillance based Crime and A...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： A. Kishore Kumar P. Suganthi Sakthi Saravanan. N Joel T Sathish Shanmugam Department of Robotics and Automation Sri Ramakrishna Engineering College Vattamalaipalayam Coimbatore Department of CSE Sri Sai Ram Institute of Technology Chennai Department of EEE S. A. Engineering College Chennai Department of ECE R.M.K. Engineering College Chennai Everi India Private Limited Chennai India

In the realm of urban safety and security, the development of efficient and accurate crime detection systems is of paramount importance. This study presents an empirical evaluation of a cutting-edge Video Surveillance-based Crime and Anomaly Detection System (CADS) that harnesses the power of Hybrid Deep Learning, integrating Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN). CADS has been meticulously designed to detect and analyze criminal activities and abnormal behaviors in real-time video surveillance data. It employs a hybrid approach that combines the capabilities of ANN for feature extraction and pattern recognition with the temporal analysis prowess of RNN for identifying sequential anomalies. Through rigorous empirical evaluation, CADS demonstrates remarkable performance in urban crime detection scenarios. The system excels in detecting both overt and subtle criminal activities, providing valuable insights for enhanced safety measures. Notable achievements include the recognition of suspicious behaviors such as crowd contraflow, prolonged parking lot scouting, and even direct assaults. This research delves into the intricate workings of CADS, elucidating its design and implementation in Python on a Windows platform. The proposed model represents a significant advancement in the field of crime and anomaly detection when compared to existing models. In rigorous comparative evaluations, the proposed model has demonstrated its superiority by consistently outclassing all existing models, achieving an impressive accuracy rate of 97.77%. By leveraging CADS, law enforcement agencies and urban security stakeholders can proactively respond to potential threats and anomalies, ultimately contributing to safer and more secure urban environments.

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OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments

arXiv

引用

arXiv 2023年

作者： Zhang, Chubin Yan, Juncheng Wei, Yi Li, Jiaxin Liu, Li Tang, Yansong Duan, Yueqi Lu, Jiwen The Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Department of Automation China The Department of Electronic Engineering Tsinghua University Beijing100084 China Gaussian Robotics Shanghai201100 China Beijing100085 China

Occupancy prediction reconstructs 3D structures of surrounding environments. It provides detailed information for autonomous driving planning and navigation. However, most existing methods heavily rely on the LiDAR point clouds to generate occupancy ground truth, which is not available in the vision-based system. In this paper, we propose an OccNeRF method for training occupancy networks without 3D supervision. Different from previous works which consider a bounded scene, we parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. The neural rendering is adopted to convert occupancy fields to multi-camera depth maps, supervised by multi-frame photometric consistency. Moreover, for semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model. Extensive experiments for both self-supervised depth estimation and 3D occupancy prediction tasks on nuScenes and SemanticKITTI datasets demonstrate the effectiveness of our method. The code is available at https://***/LinShan-Bin/OccNeRF. Copyright © 2023, The Authors. All rights reserved.

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