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检索条件"任意字段=3rd International Conference on Computational and Experimental Methods in Mechanical Engineering"

共 1766 条记录，以下是51-60 订阅

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Finite Element Analysis and experimental Study of ss316l Manufactured by Powder Bed Fusion 3rd

Finite Element Analysis and Experimental Study of ss316l Man...

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3rd international conference on Innovation in engineering, ICIE 2024

作者： Thangaraju, Vinothraj Usha Jayaseelan, Joel Srinivasan, Sai Srish Machado, Jose Michael, Anthony Xavior School of Mechanical Engineering Vellore Institute of Technology Vellore632014 India University of Minho Braga Portugal

ISBN: (纸本)9783031626838

Additive technology is filling the technological and economical gap from conventional manufacturing methods. ss316l is the most appropriate material for applications in strategic industries due to its enhanced corrosion properties like sensitization and toughness even at very low temperatures. Thus, exploring the mechanical properties and characterization of this material by additive manufacturing method can give an insight into its usability and applications. This paper focuses on the experimental study of ss316l obtained by Laser powder bed fusion techniques, which is one of the additive manufacturing processes. mechanical properties were investigated by microhardness, residual stress and tensile tests in as printed and heat treated conditions. Metallurgical characterization was performed by optical microscope and field emission scanning electron microscope to investigate the microstructure with low and high magnifications corresponding Edx elemental analysis was examined and x-ray defragmentation was studied, revealing the presence of an FCC crystal structure in all as-built and heat-treated conditions. The experimental results were validated with finite element modeling. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Finite element method

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3rd Biennial international conference on Future Learning Aspects of mechanical engineering, FLAME 2022

3rd Biennial International Conference on Future Learning Asp...

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3rd Biennial international conference on Future Learning Aspects of mechanical engineering, FLAME 2022

ISBN: (纸本)9789819947577

The proceedings contain 70 papers. The special focus in this conference is on Future Learning Aspects of mechanical engineering. The topics include: Importance of Performance and Emission Characteristics in Biodiesel;experimental Investigation of Parallel Fan Arrangement for Variable Air Volume in HVAC Systems;comparison of Basalt/Kevlar/Glass Fibre Duralumin Laminate-Reinforced Composite Using Nanoclay Filler;comprehensive Study on Solar Adsorption Cooling System;absorption Cooling System Powered by a Low Concentration Collector: A Case Study;analyzing the Factors Influencing the Market Feasibility of Alternative Fuel-Based Vehicles;hydrogen as a Fuel For Power Generation—A Review;mix Oil Biodiesel Blend’s Performance Characteristics with Energy Audit;portable Solar Stirling Engine;analyzing Barriers of Industry 4.0 Enabled Sustainable Manufacturing to Achieve Circular Economy;prioritization of Factors for Robust Manufacturing System: A Case of Disk Brake;maintaining Comfort Air Conditioning System Inside a Four Wheeler Using Phase Change Material;comparative Study of Phenotypic and Genotypic methods for Biofilm Detection on Medical Devices: An Empirical Approach;synthesis and Analysis of Vital Social Sustainability Indicators Using Pareto Analysis;numerical Analysis of Cavity Flow at Different Angles of Attack;investigation of Carbon Nanotubes and Titanium Dioxide Doped Biodiesel on the Performance and Emission Characteristics of Four-Stroke Diesel Engine;a Study of the Effects of Low Quantity Lubrication on Machine Efficiency;a Time Saving Image Segmentation Technique and Its Application to Detect Cotton Leaf Spot;COMSOL Multiphysics Simulation of Microwave System-Based Household Welding Approach;Influence of Nanoparticles in Application of Solar Collector-Based Energy Harvesting (SCBEH): A Review;a Review on Compliant Microgripper.

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mechanical Evaluation of Bone Scaffolds' Behavior under Compression 46

Mechanical Evaluation of Bone Scaffolds' Behavior under Comp...

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46th Annual international conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ntousi, Ourania A. Pleouras, Dimitrios S. Siogkas, Panagiotis K. Roumpi, Maria Kontaxis, Lykourgos C. Bourantas, George Deligianni, Despoina Kostopoulos, Vassilis Pakos, Emilios E. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR45110 Greece University of Patras Laboratory of Biomechanics and Biomedical Engineering Dept. of Mechanical Engineering and Aeronautics RionGR26500 Greece University of Patras Dept of Agriculture MessolonghiGR30200 Greece University of Patras Dept. of Mechanical Engineering and Aeronautics Rio Achaia26504 Greece University of Ioannina School of Medicine Department of Orthopaedic Surgery and Traumatology Epirus Ioannina45221 Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Biomedical Research Institute-FORTH GR45110 Greece

ISBN: (纸本)9798350371499

This study aims to comprehensively evaluate the mechanical performance of bone scaffolds, focusing on a compression test displacement rate 0.5mm/s and 2mm/min. The research employs the Yeoh 3rd order model for the hyperelastic PLA scaffolds to simulate the compression tests, which are validated using experimental results. More specifically, the stress- strain curves derived from the in-silico analysis exhibit strong agreement with the experimental results, highlighting the reliability of the computational approach. Two distinct scaffold geometries were employed and a comparative analysis of their responses to specific loading conditions had been performed. The investigation reveals that the PCL-50 scaffold demonstrates promising mechanical behavior in terms of better endurance in such loading conditions. © 2024 IEEE.

关键词： Stress-strain curves

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Empirical Evaluation of Real-Time Diabetic Retinopathy Disease Detection using IoT Enabled Medical Imaging Processing Strategy 3

Empirical Evaluation of Real-Time Diabetic Retinopathy Disea...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shifani, S. Agnes Saraswathy, N. Margaret Clemencia, A. Santhi, G.B. Giri, Jayant Al-Mousa, Mohammad Rasmi Saveetha University Saveetha School of Engineering Department of Ece Chennai India Department of Chemistry Thanjavur India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

Diabetic Retinopathy (DR) poses a significant risk to vision among diabetic patients, necessitating early and accurate detection for effective intervention. This research introduced a novel methodology to detect the diabetic retinopathy in an efficient way. In contrast to the existing technique called Convolutional Neural Network (CNN), the proposed approach called Hybrid Learning assisted Diabetic Retinopathy Predictor (HLDRP) allows for easy and quick implementation and offers patients direct access to diagnostic equipment. By leveraging a hybrid learning model that incorporates both supervised and unsupervised learning techniques, HLDRP enhances the accuracy and reliability of DR diagnosis. The IoT component facilitates continuous monitoring and data acquisition from patients, while the hybrid learning model analyzes this data to detect early signs of DR and predict disease progression. experimental results indicate that HLDRP offers superior diagnostic performance compared to traditional methods, with increased sensitivity and specificity. This integrated approach not only improves early detection but also enables personalized treatment planning, potentially transforming the management of diabetic retinopathy through innovative technology and advanced analytics. Through comprehensive evaluation using a diverse dataset of retinal images, HLDRP demonstrates significant improvements in diagnostic performance, including increased sensitivity and specificity compared to traditional detection methods. The results underscore HLDRP's potential to facilitate early intervention and personalized treatment, providing a valuable tool for healthcare professionals in managing and preventing diabetic retinopathy. © 2025 IEEE.

关键词： Convolutional neural networks

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Ammunition Coordination Fault Diagnosis Method Based on Wavelet Packet and CPO-LSSVM 3

Ammunition Coordination Fault Diagnosis Method Based on Wave...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Zhu, Xudong Yin, Qiang School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331530280

Aiming at the problems of difficulty in extracting fault features of ammunition coordinator and ow accuracy of fault classification using traditional machine learning methods, the author of the article proposes a fault diagnosis method for ammunition coordinator based on Crested Porcupine Optimizer (CPO) optimized least squares support vector machines (LSSVM). Firstly, a total of 200 sets of experimental acceleration responses of the ammunition coordinator under five scenarios are taken as samples. The wavelet packet node energy value is obtained by wavelet packet decomposition, and subsequently used as the eigenvalue input to the CPO-LSSVM fault diagnosis model for training. The experimental results show that the fault recognition rate of the fault diagnosis model on the test set is 100%;Then, BP neural network, BNN neural network, extreme learning machine and random forest regression prediction model are used for fault identification experiments respectively. The recognition accuracy of the four machine learning classification models on the test set is 9 7. 4 1 9%, 9 4. 0 1 7% , 9 0. 5 9 8% and 9 7. 8 2 2% respectively. The results of pattern recognition show that the CPO-LSSVM fault diagnosis model has higher fault diagnosis rate than the above four, which verifies the accuracy and effectiveness of the proposed method. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Short-term wind power prediction based on improved back propagation neural network 3

Short-term wind power prediction based on improved back prop...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Wang, Huanyou Xie, Guangqi Zhan, Yingying Yang, Xingda School of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China Academy of Electronic Information and Electrical Engineering Xiangnan University Chenzhou China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

In order to improve the accuracy of short-term prediction of wind power, this paper designs a short-term prediction model of wind power based on back propagation neural network. Firstly, the domestic and foreign wind power prediction methods are studied. Based on the advantages of back propagation neural network, the relationship between the historical data of wind farm numerical weather prediction and the wind power at the same time is established. The historical data of the previous 15 days were input for training, so as to obtain the prediction result of wind power on the 16th day. Finally, mean absolute error and root mean square error were used to evaluate the accuracy of the forecast results. The experimental results show that the prediction results of the future wind power of the system are within the error range. The short-term prediction model has a certain reference value for the prediction and analysis of wind power grid connection. © 2025 SPIE.

关键词： Wind forecasting

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Advancing Drug Discovery through a Deep Learning-Based Protein-Ligand Interaction Prediction Tool 3

Advancing Drug Discovery through a Deep Learning-Based Prote...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Hunge, Sudhir S. Yadao, Aman Rajendra Shewate, Varsha Tekdas Lalsare, Prakash Namdeorao Prasannan, Shiji Ramulu, Kothuri Parashu Chintamani College of Science Pg Dept. of Chemistry Maharashtra Pombhurna India Shriram Arts And Science Junior College Dept. Of Chemistry Maharashtra Ramtek India Chintamani College of Science Dept. of Chemistry Maharashtra Pombhurna India Shri Prabhu Ramchandra Junior College Dept. of Chemistry Nanda Tah Korpana Maharashtra India Faculty of Management Studies Parul Institute of Management and Research Parul University Gujarat Vadodara India Indur Institute Of Engineering And Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331515911

In recent years, deep learning has emerged as a promising approach to advance drug discovery by accurately predicting interactions between proteins and small molecules, or ligands. The identification of these interactions is essential in developing drugs, as it informs us about a molecule's potential therapeutic efficacy. Traditional methods, like molecular docking, often struggle with accuracy and computational efficiency, limiting their application in high-throughput settings. In this paper, we propose a novel deep learning-based tool designed to predict protein-ligand interactions with enhanced precision and scalability. Our model leverages convolutional and graph neural networks to capture the complex, three-dimensional nature of these interactions. Using a large, curated dataset, we train and test the model to optimize binding affinity prediction, employing metrics such as mean squared error and accuracy to validate performance. The results show that our approach outperforms conventional techniques and recent machine learning models, demonstrating a significant improvement in predictive reliability. This tool has potential applications in screening vast compound libraries in early-stage drug discovery, reducing time and costs associated with experimental trials. By providing more accurate insights into protein-ligand affinities, our method represents a step forward in computational drug design, with implications for developing targeted therapies across various disease areas. © 2025 IEEE.

关键词： Graph neural networks

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experimental Analysis of Pose Estimation Based on ArUco Markers 1

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3rd international conference on Innovation in engineering, ICIE 2024

作者： Pulloquinga, José L. Corrata, Davide Mata, Vicente Valera, Ángel Vallés, Marina Department of Systems Engineering and Automation Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain Department of Management and Engineering Università degli Studi di Padova Via VIII Febbraio 2 Padua35122 Italy Department of Mechanical and Materials Engineering Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

ISBN: (数字)9783031615825

ISBN: (纸本)9783031615818

Pose estimation is a critical task in various domains that requires motion capture systems. In recent years, camera-based motion capture technologies have grown due to advances in artificial vision. Camera-based motion capture systems provide accurate results in pose estimation at a significant cost and demand high computational resources. Fiducial markers, like ArUco, have emerged as cost-effective alternatives, leveraging simplicity, low computational cost, and adaptability. However, there are few experimental analyses about the accuracy of an ArUco-based motion capture system. This study proposes an experimental performance analysis of an ArUco-based motion capture system with respect to a commercial system manufactured by Optitrack. The ArUco-based motion capture system is developed by means of an EPS webcam and a ZED stereo camera. Through a series of experiments, the pose estimation error is evaluated in static and dynamic scenarios in different regions of the visible camera area. Furthermore, the relationship between pose estimation error and the utilisation of a bundle of ArUco markers is analysed. The results show that the proposed ArUco-based motion capture system provides moderate accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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Research and experiment on detection methods for elevator wire rope damage 3

Research and experiment on detection methods for elevator wi...

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3rd international conference on Mechatronics and mechanical engineering, ICMME 2024

作者： Shen, Wenhao Chen, Jie Xia, Liangbo Shu, Wenhua Shanghai Institute of Special Equipment Inspection and Technical Research 915 Jinshajiang Road Shanghai200062 China

ISBN: (纸本)9781510681651

As an important component of the elevator traction system, wire rope carries all the loads of the elevator. How to detect the damage of wire ropes accurately and efficiently is currently an urgent problem that needs to be solved. The country has issued multiple standards to regulate the failure forms and scrapping conditions of steel wire ropes, such as GB/T 38803-2020, GB/T 8903-2018, GB/T 31821-2015. There are currently two main methods for electromagnetic testing of steel wire ropes, local damage detection method and metal cross-sectional area loss detection method. These two detection methods adopt the principles of leakage magnetic detection distance and main magnetic flux detection in electromagnetics. On the basis of studying the damage forms and scrapping conditions of wire ropes, we conduct magnetic induction detection tests through manual damage. The experimental results show that this method can effectively detect the damage degree and location of wire ropes, and has high guiding significance for on-site inspection of elevators. © 2024 SPIE.

关键词： Wire rope

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Multi-objective topology optimization simulation study based on motor 3

Multi-objective topology optimization simulation study based...

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2023 3rd international conference on computational Modeling, Simulation and Data Analysis, CMSDA 2023

作者： Li, Qiding Huang, Zhengping Li, Ketian Wang, Xiying Yu, Cheng Li, Bing Technology Center of GAC Component Co. Ltd. Guangzhou511400 China Department of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou511400 China

OptiStruct software is applied for structural optimization of the motor structure to solve the problem of resonance in a specific operating frequency range and reduce the motor operating noise. Comparing the experimental and simulated modal frequencies, a modified multi-objective topology optimization model is proposed to ensure the validity of the model, and a new optimization function is constructed to optimize the multi-objective topology of the motor housing. The results show that the geometric reconfiguration of the motor housing concerning the optimized model force transmission path achieves the effectiveness of reducing resonance noise. The multi-objective topology optimization model provides a feasible way to solve the motor-specific frequency resonance and operation noise problems. © Published under licence by IOP Publishing Ltd.

关键词： Housing

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