Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production: Knowledge Graph and Semantic Based Modeling and O
Abonyi, János, Nagy, László, Ruppert, Tamás
- 出版商: Springer
- 出版日期: 2024-01-02
- 售價: $6,140
- 貴賓價: 9.5 折 $5,833
- 語言: 英文
- 頁數: 271
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031474430
- ISBN-13: 9783031474439
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商品描述
This book presents a comprehensive framework for developing Industry 4.0 and 5.0 solutions through the use of ontology modeling and graph-based optimization techniques. With effective information management being critical to successful manufacturing processes, this book emphasizes the importance of adequate modeling and systematic analysis of interacting elements in the era of smart manufacturing.
The book provides an extensive overview of semantic technologies and their potential to integrate with existing industrial standards, planning, and execution systems to provide efficient data processing and analysis. It also investigates the design of Industry 5.0 solutions and the need for problem-specific descriptions of production processes, operator skills and states, and sensor monitoring in intelligent spaces.
The book proposes that ontology-based data can efficiently represent enterprise and manufacturing datasets.The book is divided into two parts: modelingand optimization. The semantic modeling part provides an overview of ontologies and knowledge graphs that can be used to create Industry 4.0 and 5.0 applications, with two detailed applications presented on a reproducible industrial case study. The optimization part of the book focuses on network science-based process optimization and presents various detailed applications, such as graph-based analytics, assembly line balancing, and community detection.
The book is based on six key points: the need for horizontal and vertical integration in modern industry; the potential benefits of integrating semantic technologies into ERP and MES systems; the importance of optimization methods in Industry 4.0 and 5.0 concepts; the need to process large amounts of data while ensuring interoperability and re-usability factors; the potential for digital twin models to model smart factories, including big data access; and the need to integrate human factors in CPSs and provide adequate methods tofacilitate collaboration and support shop floor workers.商品描述(中文翻譯)
本書提供了一個全面的框架，透過本體建模和基於圖形的優化技術來開發工業4.0和5.0解決方案。有效的信息管理對於成功的製造過程至關重要，本書強調在智慧製造時代，對互動元素進行充分建模和系統分析的重要性。
本書對語義技術及其與現有工業標準、規劃和執行系統整合的潛力進行了廣泛的概述，以提供高效的數據處理和分析。它還探討了工業5.0解決方案的設計，以及對生產過程、操作員技能和狀態以及智能空間中傳感器監控的問題特定描述的需求。
本書提出基於本體的數據可以有效地表示企業和製造數據集。
本書分為兩個部分：建模和優化。語義建模部分提供了本體和知識圖的概述，這些可以用來創建工業4.0和5.0應用，並在一個可重複的工業案例研究中展示了兩個詳細的應用。優化部分則專注於基於網絡科學的過程優化，並展示了各種詳細的應用，例如基於圖形的分析、裝配線平衡和社群檢測。
本書基於六個關鍵點：現代工業中對水平和垂直整合的需求；將語義技術整合到ERP和MES系統中的潛在好處；在工業4.0和5.0概念中優化方法的重要性；在確保互操作性和可重用性因素的同時處理大量數據的需求；數位雙胞胎模型在建模智慧工廠中的潛力，包括大數據訪問；以及在CPS中整合人因的需求，並提供適當的方法以促進合作並支持車間工人。
作者簡介
Janos Abonyi is a full professor at the Department of Process Engineering at the University of Pannonia, where he holds joint appointments in computer science and chemical engineering. He received his MEng and PhD degrees in chemical engineering from the University of Veszprem, Hungary in 1997 and 2000, respectively. In 2008, he earned his Habilitation in the field of Process Engineering, and the DSc degree from the Hungarian Academy of Sciences in 2011. During 1999-2000, he was employed at the Control Laboratory of the Delft University of Technology in the Netherlands. Dr. Abonyi has co-authored over 250 journal papers, book chapters, and five research monographs. He has also authored a Hungarian textbook about data mining. His research interests include complexity, process engineering, quality engineering, data mining, and business process redesign.
Tamas Ruppert is an Associate Professor at the Department of Process Engineering at the University of Pannonia, with a focus oncomputer science. He graduated with bachelor's degrees in Mechanical Engineering and Engineering Information Technology in 2015, and a master's degree in Mechatronic Engineering in 2016. He received his PhD degree in 2020. His research interests cover activity recognition, discrete-event simulators, human-centric solutions, and Operator 4.0.Laszlo Nagy received the bachelor's degree in mechatronics engineering in 2015, the master's degree in mechatronics engineering, in 2017, and the Ph.D. degree, in 2023.
He has five years of experience as an Instrumentation and Controls Field Service Engineer at Siemens, working with industrial gas turbines worldwide.
His research interest covers the areas of semantic networks, modeling of manufacturing systems, and development of complex optimization methods. Furthermore, study the industry 5.0, human-centered approach, using knowledge graphs and ontologies.
作者簡介(中文翻譯)
Janos Abonyi 是泛諾尼亞大學過程工程系的全職教授，同時在計算機科學和化學工程領域擔任聯合職位。他於1997年和2000年分別在匈牙利維斯普雷姆大學獲得化學工程的碩士和博士學位。2008年，他在過程工程領域獲得了Habilitation，並於2011年獲得匈牙利科學院的DSc學位。在1999年至2000年間，他在荷蘭代爾夫特科技大學的控制實驗室工作。Abonyi博士已共同撰寫超過250篇期刊論文、書籍章節及五部研究專著。他還撰寫了一本有關數據挖掘的匈牙利教科書。他的研究興趣包括複雜性、過程工程、質量工程、數據挖掘和業務流程重設。
Tamas Ruppert 是泛諾尼亞大學過程工程系的副教授，專注於計算機科學。他於2015年獲得機械工程和工程資訊技術的學士學位，並於2016年獲得機電工程的碩士學位。他於2020年獲得博士學位。他的研究興趣涵蓋活動識別、離散事件模擬器、人本解決方案和Operator 4.0。
Laszlo Nagy 於2015年獲得機電工程的學士學位，2017年獲得機電工程的碩士學位，並於2023年獲得博士學位。他在西門子擔任儀器與控制現場服務工程師已有五年，負責全球工業燃氣輪機的工作。他的研究興趣包括語義網絡、製造系統建模和複雜優化方法的開發。此外，他還研究行業5.0、人本方法，並使用知識圖譜和本體論。
