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

共 36972 条记录，以下是311-320 订阅

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Connecting Autonomous engineering Domains with the Shared Language of Deep Neural Networks 2

Connecting Autonomous Engineering Domains with the Shared La...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Bale, Ajay Sudhir Rajani, M. Taj, Nazneen Selva Agnes, S. Amala, G. Lal, Keerthana New Horizon College of Engineering Dept. of ECE Bengaluru India Vijay Vittala Institute of Technology Dept. of CSE Bengaluru India AIML Vijay Vittala Institute of Technology Dept. of CSE Bengaluru India Amrita School of Engineering Dept. of ECE Bengaluru India

ISBN: (纸本)9798350349221

When it comes to analyzing data, Deep Learning is a major hub. Problems with picture categorization, feature extraction, feature identification, feature learning, and feature classification are some of which DL helps solve to a large extent. It works well for impractical tasks that need learning in successive neural network layers. For unstructured data sets, DL algorithms are suggested since they can extract hundreds of characteristics. The primary emphasis of this research has been on TL as a tool for solving problems in one area and then applying that knowledge to other, related areas. Deep Neural Networks (DNNs) use DL to optimize and reinterpret partial or missing input. Health care systems, agriculture, regressing issue solutions, and other pre-processing approaches may all benefit from analysis and study of DL algorithms. In several fields, including transportation, agriculture, and manufacturing, autonomous systems are making great strides. Unfortunately, there is a lack of idea-sharing that may lead to innovation since each sector is too focused on its own demands. Integrating the common vocabulary of deep neural networks (DNNs) into autonomous engineering domains is the central proposal of this article. © 2024 IEEE.

关键词： Deep neural networks

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Conceptual Framework for Development of Intelligent Control systems for Thermoplastics Injection Molding 32nd

Conceptual Framework for Development of Intelligent Control ...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Ogorodnyk, Olga SINTEF Mfg Raufoss Norway

ISBN: (纸本)9783031382406;9783031382413

Injection molding is one of the most widely used processes for production of plastic parts. It allows manufacturing of products of various shapes and sizes with high efficiency and production rates. However, setting up process parameters for manufacturing high-quality items and no in-process variation is often a tricky task, which is usually solved using the trial-and-error method and the experience of injection molding machine operators. Such an approach has several disadvantages, as it requires a significant amount of time, also it might lead to production of scrap. An intelligent monitoring and control system for thermoplastics injection molding can be implemented to avoid this. The article presents a conceptual framework for development of such system using a systems engineering (SE) approach. The system would allow the collection, storage and analysis of injection molding process data in order to predict the quality of produced parts without the need for the application of the trial-and-error method. Machine Learning methods are proposed to be a part of the computational core. The article lists stakeholders' needs relevant for the system, defines the problem at hand, introduces what needs to be done and what has been done to find a relevant solution. Lack of use of the SE during design and development of similar systems might lead to creation of a system that is not workable, lacks required functionality or parts of it fail to work together.

关键词： Injection Molding Intelligent Control Machine Learning systems engineering

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Studying on design and manufacture of the customized cam using artificial intelligence 4

Studying on design and manufacture of the customized cam usi...

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4th international conference on Advanced manufacturing Technology and Electronic Information, AMTEI 2024

作者： Zhao, Yan Dong, Guangcheng Hao, Wenyue School of Information Engineering Baise University Guangxi Autonomous Region Baise533000 China School of Material Science and Engineering Baise University Guangxi Autonomous Region Baise533000 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

A virtual product creation technology was explored, focusing on the customized cam with a rise-dwell-fall-dwell (RDFD) motion program taking high and low speeds into account using artificial intelligence which is necessary to transform the related data of the product development (represented by CAD models) into the product. The digital solution which is the manufacturing process of numbers and can completely describe the working components to eventually realize the physical shape of the product was mentioned. The database carrying different interpolation functions for designing some special curvilinear paths meeting special kinematic requirements of the two types of cams was set up. The entire process starts with Computer-Aided Process Planning (CAPP) and then there is Computer-Aided manufacturing with Numeric Control (NC). A further discussion for customized parts design and manufacturing in specialized fields has been conducted based on digital modeling technology and expert systems with the development of artificial intelligence in engineering applications. © 2025 SPIE.

关键词： Computer aided manufacturing

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Efficient federated knowledge distillation strategy for heterogeneous data in the Internet of Things 3

Efficient federated knowledge distillation strategy for hete...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Zhao, Huiqi Xuan, Qilin Fan, Fang Zhang, Huajie Ma, Yaowen College of Intelligent Equipment Shandong University of Science and Technology Tai'an271000 China

ISBN: (纸本)9781510680449

With the rapid evolution of Internet of Things (IoT) technology and the advent of the big data era, a vast network of interconnected devices and sensors has emerged. Traditional centralized model training methods are insufficient for meeting the data privacy and sharing requirements of IoT systems. Moreover, the inherent heterogeneity of data generated by these devices poses a significant security challenge. In response, this paper presents an innovative federated knowledge distillation strategy tailored for handling heterogeneous data in IoT environments. The proposed approach incorporates knowledge distillation into the federated learning framework, facilitating the transfer of knowledge from a teacher model to a more efficient student model. Communication efficiency is enhanced by transmitting predictive labels instead of the entire model parameters. To further narrow the gap between the teacher and student models and improve generalization, Kullback-Leibler and regular terms are integrated into the federated learning objective function. Additionally, we introduce a CNN-Transformer hybrid model on the client side, comprising a classifier and discriminator network. This architecture improves the quality of predicted labels, addressing training inefficiencies associated with highly heterogeneous data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Distillation

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Exploration of Multi-layers Networks to Elicit and Capture Product Changes 20th

Exploration of Multi-layers Networks to Elicit and Capture P...

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20th international conference on Product Lifecycle Management: Leveraging Digital Twins, Circular Economy, and knowledge Management for Sustainable Innovation (PLM)

作者： Cheutet, Vincent Sekhari, Aicha Cherifi, Chantal Favre, Justin Douass, Mohamed Millescamps, Maxence Univ Claude Bernard Lyon 1 Univ Lyon 2 Univ Lyon DISP UR4570INSA Lyon Villeurbanne France ENS Paris Saclay Gif Sur Yvette France INSA Lyon Villeurbanne France

ISBN: (纸本)9783031625800;9783031625787;9783031625770

Change management is a key process in the Product Development Process. In a context of availability of large data on the process through Enterprise Information systems like PDM (Product data Management) or PLM (Product Lifecycle Management) systems, the structured data network can be analysed to identify key feature to improve the process performance. In this work, we propose a multi-layer network to engineering change management that models product, organisation and change facets together. Once build, this network is analysed with some classical graph tools to identify some key features allowing to elicit some organisational behaviours. This approach is applied on two pedagogic examples.

关键词： engineering change multi-layer network product development process PDM

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Design Principles for data-Driven Decision Evaluation

Design Principles for Data-Driven Decision Evaluation

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2023 international conference on ENTERprise Information systems, CENTERIS 2023 - international conference on Project MANagement, ProjMAN 2023 - international conference on Health and Social Care Information systems and Technologies, HCist 2023

作者： Elgendy, Nada Paivarinta, Tero Elragal, Ahmed Hannula, Karoliina Puolitaival, Kaisa M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Antell Oulu Finland

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems. © 2024 The Author(s).

关键词： Man machine systems

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Development of a Machine Learning Model for Production Facility Predictive Diagnostics

Development of a Machine Learning Model for Production Facil...

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2024 international conference on Industrial engineering, Applications and manufacturing, ICIEAM 2024

作者： Vorsin, Daniil Toichkin, Nikolay Murmansk Arctic University Department Of Informatics And Computer Engineering Apatity Russia

ISBN: (纸本)9798350395013

The usage of artificial intelligence in problems related to industry makes it possible to develop optimization, predictive digital models for their operation in real time. The developed model can conduct predictive diagnostics of equipment's, or can be employed as a system that offers recommendations to the user for subsequent optimization of the technological process Considering the growing complexity of technical equipment and their control systems, it becomes highly imperative to develop an intelligent diagnostic structure for safety management in multi-level industrial systems. The article's relevance stems from the implementation of machine learning algorithms in the industry, enabling the analysis of a considerable amount of sensor-derived data. As a result, this facilitates predictive equipment diagnostics, anomaly prediction, intelligent safety management, uninterrupted operations, and reduced maintenance downtime. © 2024 IEEE.

关键词： Recurrent neural networks

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knowledge EXTRACTION IN ADDITIVE manufacturing: A FORMAL CONCEPT ANALYSIS APPROACH

KNOWLEDGE EXTRACTION IN ADDITIVE MANUFACTURING: A FORMAL CON...

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ASME 2024 international Design engineering Technical conferences and Computers and Information in engineering conference, IDETC-CIE 2024

作者： Ndiaye, Yande Lezoche, Mario Panetto, Herve Lu, Yan Yang, Zhuo Université de Lorraine CNRS CRAN Nancy France GaithersburgMD United States

ISBN: (纸本)9780791888353

In Additive manufacturing (AM), it is still a major challenge to manage part quality, which is heavily influenced by feedstock materials, process settings, and in-process control. Deviations in these factors can lead to defects in the final product, particularly in Laser-Powder Bed Fusion (L-PBF), an AM process that builds parts with high precision but is too complex to control. On that account, in-situ monitoring becomes crucial for evaluating and ensuring process stability during L-PBF builds. By analyzing in-situ data, AM operators can detect process deviations and pause or stop a build as needed. Continuously monitoring in-situ data allows for immediate adjustments of process settings and avoids potential defects in the final part. However, the extensive amount of high dimensional data generated from advanced sensing technologies holds their benefit for anomaly detection and process control. The abundance and complexity of AM process monitoring data require a systematic approach to extract valuable insights and translate them into actionable knowledge. While machine learning-based techniques are nowadays essential for establishing AM process-structure-property relationships, they lack interpretability for quick actions from AM operators. In this context, we introduce a new methodology based on Formal Concept Analysis (FCA) to extract knowledge from AM multi-modal data as a set of association rules that could be used as a knowledge base to guide decisions and provide guidelines for AM operators. Specifically, for L-PBF, melt pool features are first extracted from in-situ melt pool images, and clustered into discrete categories through Machine Learning (ML) techniques. After that, FCA is allied and demonstrated to discover association rules revealing the intricate relationships between ranges of values of these features and process deviations. The final set of rules is analyzed and presented for users to adopt in monitoring their L-PBF AM processes. Copyright © 20

关键词： Formal concept analysis

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Bridging data and AIOps for Future AI Advancements with Human-in-the-loop. The AI-DAPT Concept 30

Bridging Data and AIOps for Future AI Advancements with Huma...

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30th IEEE international conference on engineering, Technology, and Innovation

作者： Koussouris, Sotiris Dalamagas, Theodore Figueiras, Paulo Pallis, George Bountouni, Nefeli Gkolemis, Vasilis Perakis, Kostas Bibikas, Dimitris Agostinho, Carlos Suite5 Data Intelligence Solut Ltd Limassol Cyprus ATHENA Informat Management Syst Inst IMSI Athena Res Ctr Athens Greece Univ Nova Lisboa Ctr Technol & Syst CTS Caparica Portugal Univ Nova Lisboa Intelligent Syst Associate Lab LASI Caparica Portugal Univ Cyprus Nicosia Cyprus UBITECH Athens Greece ZENITH Gas Supply Co Thessaloniki Thessalia Single Member SA Thessaloniki Greece

ISBN: (纸本)9798350362442;9798350362435

The transition of artificial intelligence (AI) from research to deployment has underscored the critical importance of leveraging data effectively in developing and evaluating AI models. Despite their pivotal role in determining performance, fairness, and robustness, data are often undervalued in AI research, lacking a data-centric focus. In response, the AI-DAPT project pioneers a data-centric approach that aligns with AI methodologies to address the pressing need for reliable and trustworthy AI systems. This paper presents the AI-DAPT project and concept, highlighting its innovative solutions. Through advanced techniques like synthetic data generation and hybrid science-guided Machine Learning (ML), AI-DAPT aims to mitigate vulnerabilities in conventional AI paradigms. The developed solutions will be demonstrated in healthcare, robotics, energy, and manufacturing scenarios, hence navigating real-world complexities.

关键词： AI pipelines data pipelines data for AI Hybrid Science-Guided AI

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AutomationML-Based Digital Twin for a Holistic data Exchange Between Virtual and Physical Robotic Cells 33rd

AutomationML-Based Digital Twin for a Holistic Data Exchange...

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33rd international conference on Flexible Automation and Intelligent manufacturing

作者： Babcinschi, Mihail Raffaeli, Roberto Bilancia, Pietro Neto, Pedro Pellicciari, Marcello Univ Coimbra Dept Mech Engn Coimbra Portugal Univ Modena & Reggio Emilia Dept Sci & Methods Engn Reggio Emilia Italy

ISBN: (纸本)9783031744815;9783031744822

Robot-based manufacturing cells exhibit strong capability in performing high customizable working cycles involving operations like machining, assembly, painting, welding and gluing. Designing and optimizing such systems require a holistic approach across domains like device layout, logical connections, control systems, and tasks. The AutomationML file format (AML) within the Reference Architectural Model Industrie (RAMI 4.0) framework enhances interconnection and interoperability among engineering tools but lacks established practices. This paper introduces AML for representing flexible manufacturing cells within the Product-Process-Resource paradigm. AML serves as a comprehensive representation of equipment, processed products, and manufacturing operations, aiding design, configuration, virtual simulation, and optimization before physical implementation. A software tool for robotic systems simulation and programming, i.e. RoboDK, is employed to generate customizable virtual prototypes based on AML content for streamlined simulation and assessment. The same AML file facilitates code transfer to physical cell control units. Demonstrated on a robotic deburring cell, this approach highlights AML's efficacy in data representation between virtual and physical environments, paving the way for digital twin mapping and enhanced manufacturing integration.

关键词： Robotics Interoperability AutomationML RAMI 4.0 Simulation Offline Programming

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