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

共 37040 条记录，以下是301-310 订阅

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Research on Precision Machining of CNC Machine Tools Based on Industrial Big data Algorithms 14

Research on Precision Machining of CNC Machine Tools Based o...

引用

14th international conference on Information Technology in Medicine and Education, ITME 2024

作者： Guo, Jun Liu, Yang Liu, Jing Zhu, LinQing Hunan Automotive Engineering Vocational University Zhuzhou China

ISBN: (纸本)9798350356670

Quality and efficiency are eternal themes of intelligent manufacturing, and the key equipment that affects quality and efficiency in metal cutting intelligent manufacturing production lines is CNC machine tools. CNC machine tools belong to precision manufacturing equipment. Although their technical indicators can reach a high level when leaving the factory, their machining accuracy is affected by factors such as fixtures, cutting tools, environmental temperature, vibration, component aging, and workpiece material consistency when applied to actual production lines. Therefore, this article introduces industrial big data algorithms to fully explore and showcase the value contained in industrial big data, effectively forming data-driven, fast iterative, and continuously optimized industrial intelligent systems, thereby effectively solving the problem of machining accuracy stability in intelligent manufacturing systems. The experimental results showed that all 30 workpieces were qualified after processing. © 2024 IEEE.

关键词： Smart manufacturing

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An Operating System for Cloud manufacturing (OSCM) 32nd

An Operating System for Cloud Manufacturing (OSCM)

引用

32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Santamaira, Ricardo Toro Ferreira, Placid M. Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9783031381645;9783031381652

Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of Computer Numerical Control (CNC) and (Programmable Logic Control (PLC) coupled with hardware and process innovations (quick-change tooling, for example) enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. In this paper, we develop an approach to a flexible and extendible architecture for shopfloor information systems. The Operating System for Cloud manufacturing (OSCM) is a full-stack, distributed platform that tracks and facilitates the interaction between manufacturing jobs and resources (physical machines, humans or software apps). It uses an event-based architecture and a message exchange/broker to enable flexible and configurable distribution of to capture, distribute, curate and store information about shop floor events. The event-based architecture makes it easy to provide context to data emanating from the shop floor, while the message broker gives it flexibility, scalability and extendibility. This paper describes the architecture of the operating system and its services. Further, it demonstrates how shop-floor data can be flexibly routed to manufacturing apps that need it before drawing up conclusions.

关键词： Cloud manufacturing Industry 4.0

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Monitoring and Prediction of Supervision System for Industrial and manufacturing Sectors Using Cloud Computing and Blockchain Technology

Monitoring and Prediction of Supervision System for Industri...

引用

2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Chandna, Manisha Chitkara University Institute Of Engineering And Technology Chitkara University Centre For Interdisciplinary Research In Business And Technology Punjab India

ISBN: (纸本)9798350366846

The evolution of the Industry 4.0 is initialized through block chain technology with cloud computing techniques. This includes the integration of digital environment with internet of things (IoT) and cyber security systems. Thus the amalgamation of advanced technologies paved way for the rise of Industry 4.0. This helps in providing security and privacy without any consequences. This helps to save and interchange the information within the network without any centralized organizations. It plays a prominent role in the production and manufacturing sectors by trusting built in the supply chain management system. They adopt numerous advantages in adopting secure financial transactions. This helps to maintain the end-user sustenance. The monitoring and prediction of the supervision system is performed by SCADA (Supervisory Control and data Acquisition) system that employs various intelligent systems in manufacturing and production sectors. This supports in accumulating and processing the real time data in the industries. The supervisory organization shows an essential character in the performance efficiency of the industrial sectors. © 2024 IEEE.

关键词： Industry 4.0

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Transforming Quality 4.0 towards Resilient Operator 5.0 needs 56

Transforming Quality 4.0 towards Resilient Operator 5.0 need...

引用

56th CIRP international conference on manufacturing systems, CIRP CMS 2023

作者： Hattinger, Monika Stylidis, Kostas University West Department of Engineering Science Division of Production Systems TrollhättanSE- 461 32 Sweden Chalmers University of Technology Division of Product Development Department of Industrial and Materials Science GöteborgSE-412 96 Sweden

Quality is one of the most important contributors to products' success in the market and essential input for design and manufacturing. Historically, quality definitions evolved over time but with significant domain-specific differences. One example of these emerging differences is the human-centric, subjective approach to quality. Current Quality 4.0 models, in most cases, are derivatives from the Total Quality Management (TQM) way, solely based on hopes for data-Driven approaches to solving problems, with the lack of a human-centric operator approach. Industry 4.0 and its associated digital technologies promise to change this notion and make formerly subjective quality dimensions measurable on a scale as input for design and manufacturing. This leads to an opportunity to bridge the current gap and streamline the Quality and Operator in a holistic, data-informed, and digital technology-enabled way. This paper introduces a Quality 4.0 transformation as a vision for the future of Human - Machine symbiosis in the context of Operator 5.0 for intelligent manufacturing systems. We discuss what needs to be added to Quality 4.0 to achieve the requirements set for Operator 5.0 This work suggests how to enrich smart manufacturing systems from a human-centric perspective with Operator 5.0 making own, informed decisions based on data, experience, and tacit knowledge. © 2023 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) Peer-review under responsibility of the scientific committee of the 56th CIRP international conference on manufacturing systems 2023.

关键词： data-Informed Design Human-Machine systems Industry 5.0 Operator 5.0 Quality 4.0 Smart manufacturing systems

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A Quality-Oriented Decision Support Framework: Cyber-Physical systems and Model-Based Design to Develop Design for Additive manufacturing Features 20th

A Quality-Oriented Decision Support Framework: Cyber-Physica...

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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： Sassanelli, Claudio Borzi, Giovanni Paolo Quadrini, Walter De Marco, Giuseppe Mossa, Giorgio Terzi, Sergio Politecn Bari Dept Mech Math & Management I-70125 Bari Italy EnginSoft Spa Ind Business Unit 4 0 I-35129 Padua Italy Politecn Milan Dept Management Econ & Ind Engn I-20135 Milan Italy Kilometro Rosso Spa Living Space Addit Technol I-24126 Bergamo Italy

ISBN: (纸本)9783031625800;9783031625787;9783031625770

Metal Additive manufacturing (AM) is a complex operation, which requires the fine-tuning of hundreds of processes parameters to obtain repeatability and a good quality design at dimensional, geometric, structural levels. Therefore, to be used as final product, metal AM parts must go through advanced quality control processes. This implies large capital equipment investment in measurement systems (i.e., tomography and lengthy inspection operations that adversely impact costs and lead times). A large amount of data can be collected in metal AM processes, as most industrial AM systems are equipped with sensors providing log signals, images and videos. This paper develops and proposes an innovative quality-oriented decision support framework, composed by a-Model-based Design tool providing Design for Additive manufacturing features, and a Cyber-Physical System created by integrating an AM asset with a real-time smart monitoring software application. Such framework caters to process engineers and quality managers needs to improve a set of quality and economic KPIs.

关键词： metal additive manufacturing model-based design design guideline cyber-physical systems digital innovation hub

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

16th International Conference on Knowledge Discovery and Inf...

引用

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

ISBN: (纸本)9789897587160

The proceedings contain 123 papers. The topics discussed include: supervised machine learning models and schema matching techniques for ontology alignment;framework for a knowledge-based course recommender system focused on IT career needs;optimization of methods for querying formal ontologies in natural language using a neural network;personalized asthma recommendation system: leveraging predictive analysis and semantic ontology-based knowledge graph;elementary multiperspective material ontology: leveraging perspectives via a showcase of EMMO-based domain and application ontologies;positive-unlabeled learning using pairwise similarity a parametric minimum cuts;efficient visualization of association rule mining using the rules;predicting post myocardial infarction complication: a study dual-modality and imbalanced flow cytometry data;a model for designing personalized and context-aware nudges;automatic transcription systems: a game changer for court hearings;scientific claim verification with fine-tuned NLI models;and assessing the use of online platforms in sharing tacit knowledge in innovation networks.

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AutoML with Focal Loss for Defect Diagnosis and Prognosis in Smart manufacturing 10

AutoML with Focal Loss for Defect Diagnosis and Prognosis in...

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10th IEEE international conference on Cybernetics and Intelligent systems and IEEE conference on Robotics, Automation and Mechatronics, CIS-RAM 2023

作者： Han, Qing Xia, Yu Shi, Xiupeng Zeng, Zeng School of Microelectronics Shanghai China

ISBN: (纸本)9798350303087

Reliable defect diagnosis and prognosis for production lines are of great benefit in a range of advanced solutions towards smart manufacturing and yield optimization. However, inherent challenges exist, especially imbalanced data, missing value, and knowledge-related feature engineering. This study proposes a domain-specific automated machine learning (AutoML) method to self-learn the optimal models for defect diagnosis and prognosis. Firstly, a tree-based AutoML with focal loss (termed AutoGBM) is proposed based on XGBoost and Bayesian Optimization to improve imbalanced learning performance. Secondly, the AutoGBM system includes a hybrid feature learning mechanism based on fast feature filtering and recursive elimination, by which the key factors for defect diagnosis can be unveiled. Meanwhile, the proposed feature learning can identify the optimal interpolation preprocess to tackle missing data. Thirdly, based on Bayesian optimization, hyperparameter autotuning is conducted to generate the best prognostic results. Herein, SECOM data is used for test-bedding. Experimental results have demonstrated that AutoGBM is reliable and promising to diagnose key factors and predict defects in an automated way, as well as achieve adaptive model updates according to changes in manufacturing lines. © 2023 IEEE.

关键词： Defects

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A Smart Hybrid Enhanced Recommendation and Personalization Algorithm Using Machine Learning 16

A Smart Hybrid Enhanced Recommendation and Personalization A...

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作者： Nalluri, Aswin Kumar Zhang, Yan School of Computer Science and Engineering California State University San Bernardino 5500 University Parkway San BernardinoCA92407 United States

ISBN: (纸本)9789897587160

In today’s era of streaming services, the effectiveness and precision of recommendation systems are pivotal in enhancing user satisfaction. Traditional recommendation systems often grapple with challenges such as data sparsity in user-item interactions, the need for parallel processing, and increased computational demands due to matrix densification, all of which hinder the overall efficiency and scalability of recommendation systems. To address these issues, we proposed the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA), a cutting-edge machine learning approach designed to revolutionize movie recommendations. SHERPA combines Term Frequency-Inverse Document Frequency (TF-IDF) for content-based filtering and Alternating Least Squares (ALS) with weighted regularization for collaborative filtering, offering a sophisticated method for delivering personalized suggestions. We evaluated the proposed SHERPA algorithm using a dataset of over 50 million ratings from 480,000 Netflix users, covering 17,000 movie titles. The performance of SHERPA was meticulously compared to traditional hybrid models, demonstrating a 70% improvement in prediction accuracy based on Root Mean Square Error (RMSE) metrics during the training, testing, and validation phases. These findings underscore SHERPA’s ability to discern and cater to users’ nuanced preferences, marking a significant advancement in personalized recommendation systems. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Collaborative filtering

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Modular Representation of Components to Enable Generative engineering

Modular Representation of Components to Enable Generative En...

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2024 IEEE international conference on Electrical systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and international Transportation Electrification conference, ESARS-ITEC 2024

作者： Malik, Rohail Ahmad, Muhammad Vepsäläinen, Jari Aalto University Department of Mechanical Engineering Espoo Finland

ISBN: (纸本)9798350373905

Generative design of engineering systems has been applied across various fields, including robotics, electronics, and architecture. Usually involving design grammars, a common challenge in all these approaches has been data and design representation. Design grammars also demand significant human input for development, are limited in scope, and require iterative testing to be fully applicable to a design problem. This paper introduces the concept of engineering component modularity, inspired by the modularity of words. The concept is presented in context of a generative design framework driven by a knowledge graph database. Additionally, a technical representation based on standardized practices is proposed, enabling the creation and simulation of software agnostic system architecture designs. This work is limited to the component representation, with the generation process itself, and the proposed capability to learn from the evaluated results, left for future research. The groundwork established here impacts the development of generative system design frameworks by enabling self-sustainable and robust autonomous design which requires minimal human inter-vention. It thus offers a tool for unbiased, efficient design space exploration. While previous research has addressed generative system design, the novelty of this approach lies in the use of a knowledge graph database in place of design grammar, combined with an integrated standardized simulation framework for more comprehensive evaluation. © 2024 IEEE.

关键词： knowledge graph

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A System-Dynamics-Based Model to Study the Implementation of Big data with ETL Process in the Automobile manufacturing Industry

A System-Dynamics-Based Model to Study the Implementation of...

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2024 Portland international conference on Management of engineering and Technology, PICMET 2024

作者： Negrete-Rodriguez, Mario A. Martinez-Soto, Cesar M. Elizondo-Noriega, A. Guemes-Castorena, David School of Engineering and Sciences Tecnologico de Monterrey N.L. Monterrey Mexico School of Social Sciences and Government Tecnologico de Monterrey N.L. Monterrey Mexico

ISBN: (纸本)9781890843458

Industry 4.0 marks a transformative paradigm, heralding a new era for the production and service sectors by integrating Big data technologies. This progression unlocks latent knowledge within vast datasets, empowering decision-making through advanced analytics. However, the literature lacks a comprehensive understanding of the impact of such integration on organizational structures. Despite Big data's maturation, its economic influence on industry operations is not fully understood and warrants further investigation. Understanding the implications of technology adoption is crucial to minimize the risks associated with capital investments. This study proposes a System-Dynamics-based model of Big data architecture utilizing vehicular sensor data warehouses. The model delineates the application of the Extract, Transform, Load (ETL) process via Amazon Web Services (AWS), employing the Glue service for data integration and Quicksight to visualize insights. This approach aims to understand the advantages and disadvantages of Big data in the automobile manufacturing industry. Also, it provides a strategic foresight into technology investment and enhances the analytical capabilities within the manufacturing domain. © 2024 PICMET.

关键词： Automotive industry

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