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Heterogeneous microstructure and mechanical behaviour of Al-8.3Fe-1.3V-1.8Si alloy produced by laser powder bed fusion

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Virtual and Physical Prototyping 2023年第1期18卷 Article: e2155197页

作者： Yu, S.J. Wang, P. Li, H.C. Setchi, R. Wu, M.W. Liu, Z.Y. Chen, Z.W. Waqar, S. Zhang, L.C. Additive Manufacturing Institute Shenzhen University Shenzhen China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics College of Mechatronics and Control Engineering Shenzhen University Shenzhen China School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai China Cardiff School of Engineering Cardiff University Cardiff United Kingdom Department of Materials and Mineral Resources Engineering National Taipei University of Technology Taipei Taiwan Department of Mechanical Engineering Institute of Space Technology Islamabad Pakistan School of Engineering Edith Cowan University Perth Australia

The relationship between processing parameters, microstructure, and mechanical properties of Al-8.3Fe-1.3V-1.8Si alloy processed by laser powder bed fusion is seldom studied. Therefore, fully dense alloys with two parameters were selected to investigate this key issue. The results show that the alloy with low power and scanning speed (S200) shows fan-shell-shaped melt pools and laser tracks while another (S350) shows a deeper and wider melt pool. Both alloys obtain a heterogeneous microstructure without a secondary phase in melt pool (MP) and a nano-sized phase in melt pool boundary (MPB). The difference between solid-solution strengthening and Orowan strengthening in MP and MPB contributes to the difference in compressive yield strength (S200: 380 ± 14 MPa and S350: 705 ± 16 MPa), and heterogeneous nano-hardness results in different crack behaviours and failure strains. This work indicates that adjusting processing parameters is an effective method to control microstructure and mechanical properties of this alloy. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Aluminum alloys

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Cationic redistribution induced spin-glass and cluster-glass states in spinel ferrite

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Physical Review B 2022年第17期106卷 174402-174402页

作者： S. Nayak S. Ghorai A. M. Padhan S. Hajra P. Svedlindh P. Murugavel Pervoskite Materials Laboratory Functional Oxides Research Group (FORG) Department of Physics Indian Institute of Technology Madras Chennai 600036 India Solid State Physics Department of Materials Science and Engineering Uppsala University Box 35 75103 Uppsala Sweden Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea

The effect of the cationic redistribution on the complex spinel structure and magnetic properties were investigated in Zn0.7Cu0.3Fe2O4 ferrite. X-ray photoelectron spectroscopy and x-ray diffraction studies revealed that the system exhibits a mixed spinel structure with Fe3+, Zn2+, and Cu2+ occupying both tetrahedral and octahedral sublattices. The DC magnetization results revealed the absence of long-range magnetic order in the system. Furthermore, the AC susceptibility data analysis using dynamic scaling laws suggests that the system exhibits magnetic relaxation below two different temperatures: (i) a spin-glass–like transition at low temperature (∼49.2K) with critical exponent 10.3 and spin-flip time ∼10−11s, and (ii) a cluster-glass–like transition at higher temperature (∼317K) with critical exponent 4.6 and spin-flip time ∼10−10s. The existence of glassy behavior and magnetic memory effects below the spin-glass transition temperature proves that the system is in nonequilibrium dynamical state. The coexistence of spin-glass and cluster-glass along with the thermal hysteresis between these two transitions could widen the technological applications of these systems.

关键词： Magnetic order Magnetic susceptibility Spin glasses Spinels Magnetization measurements Susceptibility measurements X-ray diffraction X-ray photoelectron spectroscopy

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Optimizing Transistor Selection Using AH-TOR for Low Noise Amplifier Design in Sensing Applications

Optimizing Transistor Selection Using AH-TOR for Low Noise A...

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International Conference on Sensing technology, ICST

作者： Dayarnab Baidya Karthick Thiyagarajan Mitradip Bhattacharjee Yang An Department of Electrical Engineering and Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA design is a complex and time-consuming process due to the conflicting criteria involved. This study utilizes the Analytic Hierarchy Trade-off Ranking Method (AH-TOR), an integrated Multi-Criteria Decision-Making (MCDM) approach, to facilitate the selection of a suitable transistor for LNA design. Four types of transistors were evaluated as alternatives based on four criteria, with the Pseudomorphic High Electron Mobility Transistor (PHEMT) emerging as the optimal choice. The weight of PHEMT was calculated to be 1.0787, and its performance was benchmarked against other existing methods. Further analysis demonstrated the robustness of the AH-TOR method across various scenarios, consistently identifying PHEMT as the most suitable option. These findings highlight the reliability of the AH-TOR method for transistor selection in diverse applications.

关键词： Low-noise amplifiers Wireless sensor networks PHEMTs Decision making Receivers Electron mobility Robustness Sensors Transistors Integrated circuit reliability

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Continual Learning for Video Action Recognition using Parameter Allocation

Continual Learning for Video Action Recognition using Parame...

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IEEE India Conference (INDICON)

作者： Lukmanul Hakeem Aromal C J Sushant Kumar Sumit Datta School of Computer Science & Engineering Digital University Kerala (Former IIITM Kerala) Trivandrum India School of Electronic Systems & Automation Digital University Kerala (Former IIITM Kerala) Trivandrum India Center for Mechatronics Indian Institute of Technology Kanpur Kanpur Uttar Pradesh India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Conventional machine learning approaches have shown great promise in video action recognition tasks. However, these approaches have a fatal flaw: where in they fail in dynamically adapting to new stimuli in an ever evolving environment without losing any of it's previously learned knowledge. This missing ability in general machine learning is called Continual learning, a key technique in attaining Artificial General Intelligence systems (AGI's) making them capable of adapting to real-world scenario's. Catastrophic forgetting is the obstacle that hinders machine learning models from achieving continual learning. There are various approaches for embedding continual learning ability in a standard neural network architecture. In computer vision, even though continual learning has been explored in tasks like image classification, object detection and video recognition, most studies explore a replay-based continual learning approach that has a computational overhead of having to store samples from previous tasks. Here, we propose a continual learning video action recognition architecture based on a Parameter Allocation approach. The proposed method can continually learn video recognition tasks without losing its previously acquired knowledge in comparison to the traditional models and is also free from storing exemplars from previous tasks.

关键词： Continuing education Target recognition Computational modeling Neural networks Machine learning Computer architecture Object detection Multitasking Resource management Standards

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Exploring Neural Networks for Forward Kinematics of the Robotic Arm with Different Length Configurations: A Comparative Analysis

Exploring Neural Networks for Forward Kinematics of the Robo...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia

Our work investigates the utilization of Artificial Neural Networks (ANNs) to address the complexities associated with Forward Kinematics (FK) problems within the field of robotics. We undertake an extensive comparative analysis to assess how ANNs perform under different circumstances with robotic arms of varying lengths and impact the overall system’s functionality. The training, testing, and validation of ANNs are carried out using MATLAB for a simulated 2-DoF serial robotic arm involving three distinct datasets: fixed step size, random step size, and sinusoidal step size. Three training optimizers, namely Levenberg Marquardt (LM), Bayesian Regularization (BR), and Stochastic Conjugate Gradient (SCG), are considered within the ANN architecture. Based on Mean Square Error (MSE) values, the numerical findings reveal the potential of ANN in estimating forward kinematic solutions of complex robotic manipulators with different arm lengths and reducing computational complexity.

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A Soft Robotic System Imitating the Multimodal Sensory Mechanism of Human Fingers for Intelligent Grasping and Recognition

SSRN

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SSRN 2024年

作者： Xu, Jinsui Xu, Boyi Zhan, Hao Xie, Zhijie Tian, Zhen Lu, Yifan Wang, Zheping Yue, Honghao Yang, Fei South China University of Technology China State Key Laboratory of Robotics and System School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Wu Xianming School of Intelligent Engineering South China University of Technology Guangzhou510641 China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150042 China Science and Technology on Space Physics Laboratory China Academy of Launch Vehicle Technology Beijing100000 China

The sensory function is crucial for achieving precise feedback control and environmental interaction in soft robots. Therefore, the multimodal sensory capabilities that mimic human fingers have always been a research hotspot. This study proposes a design method for a soft robotic gripper that can emulate the multimodal perception mechanism of human fingers and achieve high-precision recognition of grasped objects using machine learning algorithms. In addition, a finger texture structure inspired by human fingerprints is designed using a negative pressure-induced buckling method to enhance the soft gripper's ability to perceive minute surface textures. Inspired by slow adapting (SA) receptors and fast adapting (FA) receptors of human fingers, this study designs a capacitive curvature sensor (CS) and a triboelectric texture sensor (TS) to detect the size, material, and texture of objects. Furthermore, a non-contact medium identification sensor (MIS), having a sensitivity of more than 1300 times higher compared to the traditional MISs, is fabricated using the principle of electromagnetic resonance. It is shown that by employing a deep learning-based multi-channel feature fusion network, the soft grasping system with multimodal sensing can achieve an object recognition accuracy of 98.43%. This work provides an innovative approach for the application of soft robots in various fields, such as logistics sorting and food safety. © 2024, The Authors. All rights reserved.

关键词： Textures

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PR2: A Physics- and Photo-realistic Humanoid Testbed with Pilot Study in Competition

arXiv

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arXiv 2024年

作者： Liu, Hangxin Xie, Qi Zhang, Zeyu Yuan, Tao Leng, Xiaokun Sun, Lining Zhu, Song-Chun Zhang, Jingwen He, Zhicheng Su, Yao Beijing100080 China Institute for Artificial Intelligence School of Artificial Intelligence Peking University Beijing100871 China Department of Automation Tsinghua University Beijing100084 China Department of Computer Science Harbin Institute of Technology Harbin150001 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150080 China Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215000 China

This paper presents the development of a Physicsrealistic and Photo-realistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied Artificial Intelligence (Embodied AI) and robotics. PR2 offers high-quality scene rendering and robot dynamic simulation, enabling (i) the creation of diverse scenes using various digital assets, (ii) the integration of advanced perception or foundation models, and (iii) the implementation of planning and control algorithms for dynamic humanoid robot behaviors based on environmental feedback. The beta version of PR2 has been deployed for the simulation track of a nationwide full-size humanoid robot competition for college students, attracting 137 teams and over 400 participants within four months. This competition covered traditional tasks in bipedal walking, as well as novel challenges in loco-manipulation and language-instruction-based object search, marking a first for public college robotics competitions. A retrospective analysis of the competition suggests that future events should emphasize the integration of locomotion with manipulation and perception. By making the PR2 testbed publicly available at https://***/pr2-humanoid/PR2-Platform, we aim to further advance education and training in humanoid robotics. © 2024, CC BY.

关键词： Anthropomorphic robots

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Towards a Benchmark EO Semantic Segmentation Dataset for Uncertainty Quantification

Towards a Benchmark EO Semantic Segmentation Dataset for Unc...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Dawood Wasif Yuanyuan Wang Muhammad Shahzad Rudolph Triebel Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Institute of Robotics and Mechatronics German Aerospace Center Weßling Germany

In order to achieve the objective of accurate and reliable use of deep neural networks for Earth Observation in large-scale scene understanding and interpretation, a large and diverse dataset with proper quantification of uncertainty is required. In this work, we exemplify the lack of a benchmark dataset and present the progress of a novel benchmark dataset for uncertainty quantification of deep learning models in the classic problem of building segmentation from overhead imagery. We present a synthetic dataset where synthetic UAV images were rendered from 3D mesh models of Berlin, Germany. The building masks were extracted from precise LoD-2 building models of the same area. We compare and contrast the performances of baseline methods for semantic segmentation and various uncertainty quantification techniques on this dataset. The experiments show that U-Net is the most accurate model with mIoU of 0.812. Moreover, the Bayesian model is found to be the most reliable uncertainty quantification method on our dataset, with the least ECE.

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Investigating Neural Network Hyperparameter Variations in Robotic Arm Inverse Kinematics for Different Arm Lengths

Investigating Neural Network Hyperparameter Variations in Ro...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis EI Manar Le Belvédère Tunis Tunisia

This research delves into the evaluation of Artificial Neural Networks (ANNs) in tackling the intricate domain of inverse kinematics problems within the context of robotic systems. Compared to direct kinematics, inverse kinematics presents significant challenges, especially when dealing with different arm lengths, which can impact the system's overall functionality. In this study, we conduct a comprehensive comparative analysis to assess the performance of ANNs across various robotic arm lengths. Following the forward kinematics of a simple 2-DoF robotic manipulator, three distinct datasets (fixed step size, random step size, and sinusoidal step size) are generated for training, testing, and validation phases. For every dataset, three optimizers, namely Levenberg Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient, are considered with different hyperparameters to investigate the performance of the ANN models. Based on the validation of MSE values, ANN models with different hyperparameters are found to be a promising approach in estimating the inverse kinematic solutions for different arm lengths with data complexities. Such solutions can be further extended to higher DoF robotic solutions and save the computational burden of evaluating analytical solutions.

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Artificial Neural Networks for the Forward Kinematics of a SCARA Manipulator: A Comparative Study with Two Datasets

Artificial Neural Networks for the Forward Kinematics of a S...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International Conference in

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

In the field of robotics and automation, the precise determination of manipulator positions is a fundamental aspect with widespread applications. Despite the significance of analytical methods, solving the Forward Kinematics (FK) for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can be complex and computationally intensive. Our research introduces a method to address the FK problem for the four degrees of freedom (4-DoF) SCARA manipulator through Artificial Neural Networks (ANNs). We utilize two different generated datasets, one with a fixed step size and another one based on a sinusoidal signal, for training the ANNs. This choice of datasets allows us to assess the ANN s' generalization capabilities across a range of operating conditions. Thereafter, the study is planned to investigate the ANN's performance while employing three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a comprehensive comparison of the performance of different ANN models, different training algorithms, and the two adopted generated datasets, this study provides valuable insights into selecting the most optimal training configurations for the SCARA robot's FK solutions. We employ the Mean Squared Error (MSE) and the error histogram as two performance metrics to assess the accuracy of various ANNs. The results reveal that optimal MSE outcomes were obtained when utilizing a balanced architecture with three hidden layers in the analysis of the sinusoidal-signal-based datasets.

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