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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37467 条记录，以下是551-560 订阅

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Human-Centric Smart manufacturing: A Framework Utilizing Digital Twins and Cyber-Physical Production systems 5th

Human-Centric Smart Manufacturing: A Framework Utilizing Dig...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Filho, Marcelo Feliciano Szejka, Anderson Luis Loures, Eduardo Rocha Pontif Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil

ISBN: (纸本)9783031807749;9783031807756

The shift to Industry 5.0 emphasizes human-machine collaboration, personalization, sustainability, and worker support. This paper explores integrating Digital Twins (DT) and Cyber-Physical Production systems (CPPS) within a human-centric Smart manufacturing framework. Digital Twins offer real-time prediction, simulation, and optimization, enhancing production efficiency and adaptability. CPPS merges computational and physical processes, supporting human-machine collaboration and improved decision-making. The main objective is to enhance sustainability, efficiency, and human-centricity in data-driven manufacturing. This research reviews current technologies, proposes a novel framework integrating DT and CPPS, and assesses implementing a Smart Product (SP) within a Smart Factory (SF) scenario. Key innovations include designing and integrating SPs, developing smart manufacturing processes, and applying DT for enhanced SP functionality. The findings demonstrate improvements in production efficiency, customization, human-machine collaboration, and sustainability, aligning with Industry 5.0 principles. The study concludes with recommendations for future research to integrate ergonomics, cybersecurity, and ethical considerations in smart manufacturing.

关键词： Digital twins Cyber-Physical Production systems (CPPS) Industry 5.0 Smart manufacturing Human-Centric manufacturing

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Efficient Visualization of Association Rule Mining Using the Trie of Rules 16

Efficient Visualization of Association Rule Mining Using the...

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Kudriavtsev, Mikhail McCarren, Andrew Lee, Hyowon Bezbradica, Marija Dublin City University Dublin Ireland Insight Centre for Data Analytics Dublin City University Dublin Ireland Adapt Research Centre Dublin City University Dublin Ireland

ISBN: (纸本)9789897587160

Association Rule Mining (ARM) is a popular technique in data mining and machine learning for uncovering meaningful relationships within large datasets. However, the extensive number of generated rules presents significant challenges for interpretation and visualization. Effective visualization must not only be clear and informative but also efficient and easy to learn. Existing visualization methods often fall short in these areas. In response, we propose a novel visualization technique called the"Trie of Rules." This method adapts the Frequent Pattern Tree (FP-tree) structure to visualize association rules efficiently, capturing extensive information while maintaining clarity. Our approach reveals hidden insights such as clusters and substitute items, and introduces a unique feature for calculating confidence in rules with compound consequents directly from the graph structure. We conducted a comprehensive evaluation using a survey where we measured cognitive load to calculate the efficiency and learnability of our methodology. The results indicate that our method significantly enhances the interpretability and usability of ARM visualizations. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Association rules

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Enabling Digital Twin Using MEMS and Sensor Technology with Historic data: From data Fusion to knowledge Fusion 6

Enabling Digital Twin Using MEMS and Sensor Technology with ...

引用

6th international conference on Electronic engineering and Informatics, EEI 2024

作者： Xie, Haiyan Sally Shi, Owen Gandla, Sai Ram Bhattacharya, Mangolika Illinois State University Dept. of Technology NormalIL United States Univ. of Illinois at U-C Dept. of Mathematics ChampaignIL United States Illinois State University Dept. of Cs NormalIL United States

ISBN: (纸本)9798350353594

Microelectromechanical systems and Sensor Technology (MEMS-ST) can be used together with historical data to enable digital twins. This paper presents a novel framework that transitions from the data fusion of MEMS-ST to the knowledge fusion necessary for estimating the strength capacity of highway bridges. This innovative approach provides critical information for bridge design, transportation planning, and the assessment of existing pavement and infrastructure. The unique framework integrates real-time data from MEMS-ST systems installed on a bridge with historical records and engineering standards, addressing a significant gap in current methodologies. Through a comprehensive literature review, this study evaluates the existing reports and articles on developing bridge digital twins to measure live loads on highways. Utilizing datasets from sensors and the National Bridge Inventory, the proposed framework processes data based on the Load and Resistance Factor Design method and continuous multivariate probability distributions. The innovative application of the Dempster-Shafer Theory within the knowledge fusion framework distinguishes this work, enabling the combination of different sources (86, 126% increase) of evidence into a cohesive global belief. The results demonstrate that this framework can automate the analysis of vast amounts of data, improve efficiency (615,000 bridges in NBI and 93.1M truck data), and provide more reliable insights compared to traditional methods. Specifically, it enhances the safety and stability of bridge structures (Credibility 1.088;Plausibility 1.089) while offering new ways to design and maintain bridges more effectively. This paper sheds light on bridge digital twin systems. © 2024 IEEE.

关键词： Microchannels

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Implementation of Zero Defect manufacturing using quality prediction: a spot welding case study from Bosch 5

Implementation of Zero Defect Manufacturing using quality pr...

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5th international conference on Industry 4.0 and Smart manufacturing (ISM)

作者： Psarommatis, Foivos Zhou, Baifan Kharlamov, Evgeny Univ Oslo Ctr Scalable Data Access SIRIUS Gaustadalleen 23B N-0373 Oslo Norway Univ Politecn Valencia Res Ctr Prod Engn & Management CIGIP Valencia Spain Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Bosch Ctr Artifician Intelligence Renningen Germany

Product quality is a vital aspect in the operation of manufacturing systems, manufacturers need to implement at least one quality assurance method to assure the desired quality. The most recent approach for quality assurance is named Zero Defect manufacturing (ZDM). The scope of the current paper is the implementation of ZDM approach in the automotive industry and specifically for the spot-welding process. Using a machine learning method that is utilizing linear regression and LSTM and consuming data from production such as sensors and other engineering data to predict the quality of future spot welds. Using this quality prediction there is a root cause analysis to identify why the spot weld will fail and the appropriate prevention actions are proposed. The next step is the training and validation of the machine learning model and the calculation of the accuracy of the model. Once the accuracy of the model is validated a series of simulations, using a dynamic scheduling tool, are performed in order to calculate a series of KPIs to evaluate the impact of the proposed method to the production. (C) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Zero Defect manufacturing ZDM Machine learning Quality prediction Prevention LSTM Simulation Industry 4.0

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A Comprehensive Analysis of knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： knowledge graph

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Electronic Information Intelligent Decision and Control Technology Integrating Big data and Intelligent Algorithms

Electronic Information Intelligent Decision and Control Tech...

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2024 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2024

作者： Bai, Guojing Chongqing Communication Design Institute Co.Ltd Chongqing400050 China

ISBN: (纸本)9798350378917

As the Internet of Things (IoT) and artificial intelligence (AI) technologies are rapidly developing, intelligent decision-making and control systems have become a very important part of many fields, including electronic information. This article proposes an electronic information intelligent decision-making and control technology that integrates big data analysis and intelligent algorithms, aiming to better build intelligent decision-making and control systems. By proposing a BP (Back propagation) neural network based on genetic algorithm optimization, this paper analyzes the optimization process of this algorithm model and constructs a complete intelligent decision-making and control system structure based on big data and intelligent algorithms. Moreover, through multidimensional comparative experiments, this article demonstrates the effectiveness and universality of our method. In the final application test in smart home systems, taking the failure rate as an example, the average failure rate of the experimental group was 4.36%, and the average failure rate of the control group was 5.71%. Therefore, this study found that the integration of big data and intelligent algorithms could effectively build electronic information intelligent decision-making and control systems. © 2024 IEEE.

关键词： Smart manufacturing

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SeMatS 2024 - Proceedings of the 1st international Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, co-located with the 20th international conference on Semantic systems, SEMANTiCS 2024

SeMatS 2024 - Proceedings of the 1st International Workshop ...

引用

1st international Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024

The proceedings contain 10 papers. The topics discussed include: PBF-AMP-onto: an ontology for powder bed fusion additive manufacturing processes;an ontology for units of measures across history, standards, and scientific and technology domains;top level ontologies: desirable characteristics in the context of materials science;PolyMat - bringing semantics to polymer membrane research;implementing semantic technologies in materials science and engineering;enhancing semantic interoperability across materials science with HIVE4MAT;the landscape of ontologies in materials science and engineering: a survey and evaluation;leveraging large language models for automated knowledge graphs generation in non-destructive testing;and battery manufacturing knowledge infrastructure requirements for multicriteria optimization based decision support in design of simulation.

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Monitoring of manufacturing systems: Applying Deep Learning Algorithms to Improve Gas Turbine Performance and Sustainability 12

Monitoring of Manufacturing Systems: Applying Deep Learning ...

引用

2024 12th international conference on systems and Control, ICSC 2024

作者： Madjour, Hassina Zermane, Hanane Batna2 University Laboratory of Automation and Manufacturing Engineering The Department of Industrial Engineering Algeria

ISBN: (纸本)9798331531812

Gas turbines are considered one of the most important energy-generating systems in the world, covering the energy shortage resulting from the increasing electricity demand. Although the energy they produce is clean, they face a major environmental challenge, and this is due to the pollution they cause through the gases emitted during their operation. Carbon Oxides (CO) and Nitrogen Oxides (NOx) gas are some of the most important of these gases, and measuring their proportions in emissions has become an urgent matter. This measure contributes to many positive improvements. First, preserving the environment and air pollution by reducing the emission rates of greenhouse gases, which affect warming. Second, knowledge of the proportions of these gases makes us fully aware of the system's status, which allows us to develop methods for monitoring it using modern technologies. A prognosis of the proportions of emitted gases is one of the good solutions that contribute to the sustainability of gas turbines. In this work, we have proposed an intelligent model to predict the proportion of (CO) and (NOx), based on the Long Short-Term Memory (LSTM) this algorithm gave good results compared to the convolution neural network (CNN 1D). © 2024 IEEE.

关键词： Low emission

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Enhancement of Deadlock Detection and Recovery Policy for Flexible manufacturing systems by Using Resource Flow Graph Technique 4

Enhancement of Deadlock Detection and Recovery Policy for Fl...

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4th IEEE international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2024

作者： Tseng, Ching-Yun Chen, Ju-Chin Li, Hsin-Yi Dai, You-Jhen Pan, Yen-Liang National Kaohsiung University of Sciences and Technology Department of Electronic Engineering Kaohsiung Taiwan National Kaohsiung University of Sciences and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan R.O.C. Air Force Academy Department of Avionic Engineering Kaohsiung Taiwan Artificial Intelligence Technology Characteristic Class Jhuci Senior High School Chiayi Taiwan Chaoyang University of Technology Department of Aeronautical Engineering Taichung Taiwan

ISBN: (纸本)9798350360721

It is necessary to solve the unexpected deadlock problem during competition between different manufacturing processes of Flexible manufacturing systems (FMSs). Once system deadlock occurs, the lack of shared resources and machines causes process outages. To prevent such errors, we introduced a Resource Flow Graph (RFG) based deadlock detection and recovery policy. This method was used to analyze the flow relation between operation and resource to generate corresponding control transitions. However, in related research on designing optimal deadlock solutions, the marking explosion problem often arises as the size and complexity of the model increase. Thus, we improved RFG-based methods by considering the subnet Pre-Idle Places of Petri Net (PN) model and reduced the scale of RFG. This method reduced the computational complexity and costs. We also introduced three classic PN models of FMS to demonstrate the proposed control policy in this study. © 2024 IEEE.

关键词： Petri nets

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REDUCING data REQUIREMENTS FOR WIENER SERIES IDENTIFICATION WITH GAUSSIAN PROCESS PRIORS

REDUCING DATA REQUIREMENTS FOR WIENER SERIES IDENTIFICATION ...

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ASME international Design engineering Technical conferences / 43rd Annual Computers and Information in engineering conference (IDETC-CIE) / 19th international conference on Multibody systems, Nonlinear Dynamics, and Control (MSNDC)

作者： Massingham, Joseph Nielsen, Ole Butlin, Tore Univ Cambridge Cambridge England Bose Corp Framingham MA USA

ISBN: (纸本)9780791887387

System identification of nonlinear dynamical systems aims to predict the output of a system for a given input. In many engineering applications, the underlying physics are not fully understood and so there is no analytical solution. The Wiener series is a classical data-driven technique that decomposes the system response into a set of orthogonal functionals of increasing order. Unlike standard black-box algorithms, such as neural networks, the series is highly interpretable and can offer insight into the nonlinearities present. To date, in order to calculate higher order terms in the Wiener series, vast quantities of data are needed. In this paper, a novel formulation of the Wiener series is developed in the frequency domain which applies to general stochastic inputs with an arbitrary spectrum. It is enhanced by placing Gaussian process priors over the Wiener kernels to enforce prior knowledge of their structure. This significantly reduces the quantity of data required for inference and has the benefit of enabling the calculation of the third order kernel for systems with long memory. The benefits were demonstrated in initial investigations using an idealised nonlinear oscillatory system. Decomposition of the system response into Wiener functionals also sheds light on the learnability of nonlinear dynamical systems, which could be used to assess the value of collecting additional data.

关键词： Artificial neural networks System identification Algorithms Nonlinear dynamical systems Physics engineering systems and industry applications

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