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Energy flow characteristics of friction-induced nonlinear vibrations in a water-lubricated bearing-shaft coupled system

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Acta Mechanica Sinica 2021年第4期37卷 679-704,I0003页

作者： Li Qin Hongling Qin Jing Tang Xing Key Laboratory of Metallurgical Equipment and Control Technology School of Machinery&AutomationWuhan University of Science and TechnologyWuhan 430081China Hubei Key Laboratory of Hydroelectric Machinery Design&Maintenance Three Gorges UniversityYichang 443002China Maritime Engineering Group CMEESchool of EngineeringFaculty of Engineering&Physical SciencesUniversity of SouthamptonSouthampton SO 171BJUK

Based on the energy flow theory of nonlinear dynamical system,the stabilities,bifurcations,possible periodical/chaotic motions of nonlinear water-lubricated bearing-shaft coupled systems are investigated in this *** is revealed that the energy flow characteristics around the equlibrium point of system behaving in the three types with different friction-para-mters.(a)Energy flow matrix has two negative and one positive energy flow factors,constructing an attractive local zero-energy flow surface,in which free vibrations by initial disturbances show damped modulated oscillations with the system tending its equlibrium state,while forced vibrations by external forces show stable oscillations,(b)Energy flow matrix has one negative and two positive energy flow factors,spaning a divergence local zero-energy flow surface,so that the both free and forced vibrations are divergence oscillations with the system being unstable,(c)Energy flow matrix has a zero-energy flow factor and two opposite factors,which constructes a local zero-energy flow surface dividing the local phase space into stable,unstable and central subspace,and the simulation shows friction self-induced unstable vibrations for both free and forced *** a set of friction parameters,the system behaves a periodical oscillation,where the bearing motion tends zero and the shaft motion reaches a stable limit circle in phase space with the instant energy flow tending a constant and the time averaged one tending *** simulations have not found any possible chaotic motions of the *** is discovered that the damping matrices of cases(a),(b)and(c)respectively have positive,negative and zero diagonal elements,resulting in the different dynamic behavour of system,which gives a giderline to design the water-lubricated bearing unit with expected performance by adjusting the friction parameters for applications.

关键词： Nonlinear friction-induced vibrations Nonlinear energy flows Nonlinear water-lubricated bearing-shaft systems Bifucation friction parameters Energy flow matrices Periodical oscilation

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Biointelligent Waste-to-X systems: A novel concept for sustainable, decentralized and interconnected value creation

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Procedia CIRP 2023年 116卷 576-581页

作者： Johannes Full Arber Shoshi Edgar Gamero Yannick Baumgarten Kristin Protte Steffen Kiemel Nadine Silber Jana Hessel Stephanie Eigner Thomas Bauernhansl Alexander Sauer Robert Miehe Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 70569 Stuttgart Germany Institute for Energy Efficiency in Production (EEP) Faculty of Energy- Process- and Bio-Engineering University of Stuttgart Nobelstr. 12 70569 Stuttgart Germany Institute of Industrial Manufacturing and Management (IFF) Faculty of Engineering Design Production and Automotive Engineering University of Stuttgart Allmandring 35 70569 Stuttgart Germany

Climate change, increase in waste generation, depletion of natural resources and the insufficient use of renewable resources are major issues the world has to deal with. The development and implementation of sustainable waste management strategies are required in order to establish a circular economy and to harness new sources of renewable energy. A possible solution is the concept of biointelligence, which describes a highly digitalized as well as bio-hybrid mode of interconnected production. Following nature's example, synergies between technical processes in terms of energy and materials are to be identified and utilized. Furthermore, availabilities of sustainable waste sources and consumer needs must be aligned in order to achieve a technology-based demand economy. After recent literature outlined a basic model of a biointelligent value creation system consisting of numerous on-site units, this paper introduces the concept of biointelligent Waste-to-X systems as a technological substantiation. A description of the system model as well as example technologies are presented in order to serve as a basis for their implementation.

关键词： Waste-to-X waste management biointelligence biointelligent production HyBECCS biological transformation additive biomanufacturing biology-technology-interface carbon negative hydrogen

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Mechanistic modeling of cutting forces in milling of unidirectional Glass Fiber Reinforced Polymer (UD-GFRP)

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Procedia CIRP 2025年 133卷 424-429页

作者： Matthias Nutte Edouard Rivière-Lorphèvre Valentin Dambly Pedro-José Arrazola Ismail Lazoglu Aurélie Granjon François Ducobu Machine Design and Production Engineering Lab Research Institute for Science and Material Engineering UMONS Mons 7000 Belgium Mechanical and Manufacturing Department Faculty of Engineering Mondragon Unibertsitatea Loramendi 4 Arrasate-Mondragón 20500 Spain Manufacturing and Automation Research Center Koc University Istanbul 34450 Turkey Sobelcomp SPRL. Rue de l’économie 13 4431 Loncin Belgium

Mechanistic models, established on experimental data, are recognized for their precision and simplicity in implementation. In this context of milling, the modeling of cutting forces, specifically radial and tangential components, has been extensively explored for unidirectional carbon fiber-reinforced polymer (UD-CFRP) parts. These models account for the anisotropic nature of fiber-reinforced polymers (FRPs), with cutting coefficients that vary based on the instantaneous cutting angle. However, due to the inherent flexibility of these materials, primarily attributed to their low thickness, axial cutting forces must also be incorporated to accurately represent the phenomena occurring during the machining process. This study develops a model for calculating cutting forces in three spatial directions for UD-GFRPs (unidirectional Glass Fiber-reinforced polymers). Building on an existing model from the literature for UD-CFRP, this new version introduces several modifications. First, the model validated for CFRPs must be extended to GFRPs. Secondly, the model must be extended from two dimensions to three dimensions. Finally, to ensure that the model includes only the actual contributions of the trimming operation, machining tests will be performed using only the rolling teeth. The cutting coefficients are modelled as a periodic function with a fundamental period of π. A first-order Fourier series was chosen for this purpose. Fourier series parameters are identified from the milling forces measured during a set of slotting operations at three feed rates and four fiber orientations. While the average levels of cutting forces are accurately modelled for both slotting and shoulder milling (with 50% immersion in up milling), the modeling of force amplitudes still requires improvement.

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Biomechanical Assessment of the Influence of Inlay/Onlay design and Material on Stress Distribution in Nonvital Molars

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European Journal of General Dentistry 2021年第3期10卷 158-169页

作者： Darwich, Ayham Aljareh, Abeer Aladel, Omar Szávai, Szabolcs Nazha, Hasan Department of Biomedical Engineering Faculty of Biomedical Engineering Al-Andalus University for Medical Sciences Tartous Syrian Arab Republic Department of Industrial Automation Faculty of Technical Engineering University of Tartous Tartous Syrian Arab Republic Department of Fixed Prosthodontics Faculty of Dentistry Damascus University Damascus Syrian Arab Republic Department of Machine and Product Design Faculty of Mechanical Engineering and Informatics University of Miskolc Miskolc Hungary

Objectives The aim of this study was to evaluate the influence of inlays/onlays with or without pulp extension from different materials on stress distribution in endodontically treated molars by three-dimensional finite element analysis (3D FEA). Materials and Methods We used 3D mandibular molar models in this study. The models represented mesio-occluso-distal (MOD) cavities restored by inlays, onlays that covered buccal cusps, and onlays that covered all cusps with pulp extension (modified inlay/onlay) or without pulp extension (conventional inlay/onlay). Three materials (L: lithium disilicate glass-ceramic, P: polymer-infiltrated ceramic network [PICN], and C: nanofilled composite resin) were utilized. A force of 600 N was applied vertically and obliquely. Stress distribution in FEA models was analyzed using the von Mises theory. Results The results revealed that an oblique load generated higher stresses than vertical load. Composite resin restorations transmitted almost all the stress to the neighboring tooth structures, while lithium disilicate ceramic restorations absorbed most of the stresses. Moreover, modified inlays and onlays with pulp extension proved better than conventional inlays/onlays in terms of stress redistribution in dental structures. Onlays showed a better pattern of stress distribution than inlays within the restoration and the restored tooth. Conclusions According to stress distribution in dental structures, modified lithium disilicate ceramic onlays with pulp extension have been found to be the best choice to restore endodontically treated molars among the studied restorations. © 2021 Wolters Kluwer Medknow Publications. All rights reserved.

关键词： finite element analysis inlay onlay restoration design restoration materials

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An Adaptive Deep Learning Method Integrating Transfer Learning and Concept Drift Detection for Wind Power Forecasting

An Adaptive Deep Learning Method Integrating Transfer Learni...

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第43届中国控制会议

作者： Jingdi Wang Yundong Cao Huaikang Jin Shouyuan Fan Bin Wang Huaiping Jin Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Huaneng Renewables Co. Ltd.Yunnan Branch China Energy Engineering Group Yunnan Electric Power Design Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the continuous advancement of new energy technologies,wind power is becoming an increasingly crucial source of clean *** to the stochastic and fluctuating nature of wind power data,there is a significant challenge known as concept drift,where the data distribution changes over *** poses a substantial obstacle to accurate prediction,leading to performance degradation of *** address this issue,an adaptive deep-learning wind power prediction method combining transfer learning and concept drift detection is *** method incorporates the concept of online transfer learning,transferring offline-trained model parameters to adapt quickly to new data distributions during the online ***,a combination of an online moving window and Kullback-Leibler(KL) divergence is employed to capture and analyze changes in the online predictor's output layer network *** facilitates a more precise detection of concept drift in wind power prediction,determining whether model updates are *** dynamically updating model parameters and effectively handling concept drift,the approach can maintain high accuracy and stability in wind power *** effectiveness and superiority of the proposed method are validated through the analysis of real-world wind power field data.

关键词： Wind Power Forecasting Concept Drift Transfer Learning Online Updating Gated Recurrent Unit

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Multi-stage Attention Mechanism based Deep Learning for Wind Power Forecasting

Multi-stage Attention Mechanism based Deep Learning for Wind...

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第43届中国控制会议

作者： Guanzhi Yang Yundong Cao Huaikang Jin Shouyuan Fan Bin Wang Huaiping Jin Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Huaneng Renewables Co. Ltd.Yunnan Branch China Energy Engineering Group Yunnan Electric Power Design Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The accurate prediction of wind power is of great significance for the stable operation of the power ***,due to the inherent intermittency and randomness of wind speed,existing time series prediction models struggle to capture the complex nonlinear relationship between wind speed and wind power,resulting in low prediction ***,this study proposes a multi-stage attention long short-term memory network(MA-LSTM) prediction ***,convolution and attention mechanisms are fused to form the first stage of attention,extracting multi-dimensional features of dynamic changes in wind power ***,the feature vectors with spatial importance weights are input into the LSTM network for temporal feature ***,a second stage attention mechanism assigns different weights to hidden state features at various time ***,to counteract the low quality of the second stage attention extraction hidden state caused by the randomness of wind power data,a random difference structure is employed,forming multi-stage attention and LSTM units in parallel to develop the proposed *** proposed method is evaluated using a real wind farm dataset,and the experimental results show that the MA-LSTM model can better capture the spatiotemporal characteristics of historical wind power data and provide higher quality time step hidden features,thereby significantly improving the accuracy of wind power prediction.

关键词： Wind power prediction Long short-term memory network Attention mechanism Hidden state quality Spatial and temporal correlation

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Deep Learning Wind Power Forecasting Method Integrating Architecture Comparison-based Evolutionary Neural Architecture Search

Deep Learning Wind Power Forecasting Method Integrating Arch...

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第43届中国控制会议

作者： Wang Zhou Yundong Cao Huaikang Jin Shouyuan Fan Bin Wang Huaiping Jin Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Huaneng Renewables Co. Ltd.Yunnan Branch China Energy Engineering Group Yunnan Electric Power Design Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Wind energy has become an important part of the power system due to its versatility,low cost,and environmental ***,the instability and volatility of wind energy pose a huge challenge to the grid-connected operation of wind power,affecting the effective scheduling of the power *** this situation,accurate forecasting of wind power becomes crucial,and deep neural networks have been widely used in this field due to their excellent ability to process high-dimensional nonlinear spatiotemporal ***,excellent deep neural network architectures are often manually designed by users with extensive expertise,which is time-consuming and results in high *** order to automatically design suitable deep neural network architectures for different wind power forecasting tasks,this paper proposes a deep learning wind power forecasting method based on architecture comparison evolutionary neural architecture *** method first encodes neural network architectures and then uses evolutionary algorithms to search for the optimal *** them,to address the problem of slow fitness calculation in evolutionary algorithms,architectures comparison-based the method is used instead of fitness calculation for architecture search,greatly accelerating search *** effectiveness and superiority of the proposed method were verified through case studies on actual wind farm datasets.

关键词： Wind Power Forecasting Evolutionary Neural Architecture Search Architecture Comparator Architecture Encoding

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Collaborative Learning in Bilateral Academic Training on Robotics design and Development

Collaborative Learning in Bilateral Academic Training on Rob...

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STEM Education Conference (iSTEM-Ed), International

作者： Patinya Sang-Aroon Sethakarn Prongnuch Tadchanon Chuman Aphirak Thitinaruemit Kalaphath Kounlaxay Chanida Niyomtum Dept. of Industrial Design and Packaging Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Robotics Engineering Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Electrical Technology Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Souphanouvong University Luangprabang City Lao PDR Education Automation System Co. Ltd. Bangplee Samutprakran Thailand

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This paper presents how collaborative learning can facilitate academic training in robotics design and development, with the objective of establishing a foundation in robotics structure design and microcontroller programming for controlling robots, particularly for bilateral undergraduate students. The students comprise 20 engineering students and 20 architecture students, who are divided into groups of two, consisting of an engineering students and an architecture students each. Group activities focusing on robot structure design and robot Development are provided for the students. The results demonstrate that collaborative learning can effectively facilitate the training activities.

关键词： Training Educational robots Federated learning Microcontrollers Focusing engineering students Programming profession

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Prediction of temperature for various pressure levels using ANN and multiple linear regression techniques: A case study

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Materials Today: Proceedings 2022年 56卷 194-199页

作者： Jain, Sarika Rathee, Sarita Kumar, Ajay Sambasivam, Anivel Boadh, Rahul Choudhary, Tushar Kumar, Parveen Kumar Singh, Pravin Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering & Technology Shree Guru Gobind Singh Tricentenary University Gurugram122505 India Department of Mechanical Engineering Saveetha Engineering College Thandalam Tamilnadu Chennai602105 India School of Basic and Applied Sciences K R Mangalam University Haryana Gurgaon122103 India PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Mechanical Engineering Rawal Institute of Engineering and Technology Haryana Faridabad121004 India Department of Mechanical and Automation Engineering Amity University Jharkhand Ranchi834001 India

In this paper, the estimation capacities of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) are examined to forecast temperature at two pressure level 400 hPa and 980 hPa approximately over Delhi Safdarjung Airport Station. Different meteorological parameters such as temperature, relative humidity, dew point temperature, mixing ratio, potential temperature has been taken as an input for ANN and MLR models. Statistical bases give us forecasting of temperature by using above mentioned parameters. ANN gives us better results in forecasting temperature as compared to MLR and if compared different techniques of ANN then Multiple layer Artificial Neural Network (MLANN) proved to be best as compared to Single layer Artificial Neural Network (SLANN). © 2022

关键词： Multiple linear regression

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Enabling Industry 4.0 impact assessment with manufacturing system simulation: an OEE based methodology

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Procedia CIRP 2022年 107卷 681-686页

作者： Luisa M. Tumbajoy Mariela Muñoz-Añasco Sebastian Thiede Industrial Automation Group Faculty of Electronics and Telecommunications Engineering University of Cauca 190002 Popayán Colombia Chair of Manufacturing Systems Faculty of Engineering Technology Department of Design Production & Management University of Twente De Horst 2 Building 20 Enschede 7522 LW The Netherlands

Increasing digitalization in manufacturing, often associated with terms like Industry 4.0 (I4.0) or Smart Manufacturing, is a topic of crucial concern for manufacturing companies. Different digital technologies (DTs) can be integrated into manufacturing processes and systems aiming at increasing flexibility, product quality or productivity. The type and scope of potential DTs must be carefully selected when planning and improving a manufacturing system. The definition and configuration could be supported by simulation techniques that assess the DTs' impact on the manufacturing system and its final performance. However, parametrizing the DTs into a simulation tool is not straightforward since appropriate models might be challenging to obtain and actual impacts of DTs are uncertain. Against this background, the paper presents methods to enable a simulation-based assessment while considering the impact of not just individual but also a combination of DTs. The paper introduces a framework to define the base characteristics of selected DTs within a manufacturing system and their parameterization into a commercial simulation tool. Furthermore, the usability and expectable results are demonstrated in a case study.

关键词： Smart Manufacturing Industry 4.0 Key Performance Indicator Simulation

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