{"id":103465,"date":"2024-02-20T13:06:12","date_gmt":"2024-02-20T12:06:12","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103465"},"modified":"2024-05-14T04:40:36","modified_gmt":"2024-05-14T02:40:36","slug":"digital-twin","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/industry-4-0\/digital-twin\/","title":{"rendered":"Digital Twin"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\n
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Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Jan Schickram<\/a>, Tareq Albeesh<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Empathic\n <\/picture>\n <\/div>\n
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Empathic Assembly Assistance<\/h4>\n
Combining AI-based data analysis and empathic human digital twins<\/div>
Matthias L\u00fcck<\/a> \n \"ORCID<\/a>, Katharina H\u00f6lzle<\/a> \n \"ORCID<\/a>, Christian Saba-Gayoso<\/a> \n \"ORCID<\/a>, Joachim Lentes<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial companies in Germany face demographic change and stagnating productivity in an increasingly complex world. Manual assembly remains essential for complex, low-volume products, yet productivity and quality lag due to human variability. This paper introduces a concept and demonstrator for an empathic assembly assistance system that merges a human digital twin and AI-based screwdriver data analytics within a modular architecture. Tightening anomalies are classified, linked to inferred worker states and translated into information and recommendations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 6-13 | DOI 10.30844\/I4SE.25.5.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Enabler for the Digital Twin<\/h4>\n
Requirements for Technical Documentation 4.0<\/div>
Christian Koch<\/a>, Lukas Schulte<\/a>, Ren\u00e9 W\u00f6stmann<\/a>, Jochen Deuse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 4 | Pages 76-85<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Supply Chain Twin: The Pathway to Success<\/h4>\n
A catalyst for increasing competitiveness <\/div>
G\u00f6khan Cenk<\/a> \n \"ORCID<\/a>, Jonas Andersson<\/a>, Tobias Engel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Companies face a variety of challenges when optimizing global supply chains. Economic interests must be balanced with legal requirements, such as the German Supply Chain Due Diligence Act (SCDDA) and the European Sustainability Reporting Standards (ESRS). A digital supply chain twin (DSCT) enables the visualization of value creation networks and supports key business functions, such as purchasing, supply chain management, distribution, service, and sales. By leveraging immersive technologies, the DSCT helps generate sustainable competitive advantages across the entire supply network. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 52-60 | DOI 10.30844\/I4SE.25.3.52<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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The Core Principles of the Digital Twin<\/h4>\n
Transformingorder processes and the automation pyramid<\/div>
Wilmjakob Herlyn<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>The digital twin [DT] is considered a key technology of Industry 4.0. Its basic concept is now being successfully applied in practice, as demonstrated by the commissioning of Mercedes' Factory56 in 2022. New identification technologies, tracking systems and communication solutions faciliate new ways of controlling production and managing material flows, particularly at the shop floor level. With precise technical data permanently available not only for products, but also for material availability and order fulfillment status, production processes can be managed more dynamically and efficiently. This is precisely where the concept of the DT comes into play, enabling the immediate use and evaluation of this data.Its relevance continues to grow, especially in the context of make-to-order production, the rising variety of product configurations, and the globalization of production and supply networks. This article introduces the basic concept of the DT and illustrates how it connects to ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 92-101<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Twins for Production and Logistics Systems<\/h4>\n
Challenges and focus areas in implementation and use<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Nicolas Wittine<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n For a successful implementation as well as sustainable use and maintenance of digital twins for production and logistics systems, it is necessary to identify relevant use cases and master the associated challenges. This paper analyzes scientific literature on common applications and challenges in the implementation of digital twins for the planning and operation of production and logistics systems. To confirm the practical relevance of the results, the results of an empirical survey have also been included. The findings are used to derive key focus areas for the successful implementation and long-term use of digital twins in production and logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 42-49 | DOI 10.30844\/I4SE.25.3.42<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Real-Time\n <\/picture>\n <\/div>\n
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Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO\u2082 footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Enabling the Future of Manufacturing with Digital Twins<\/h4>\n
Opportunities and obstacles<\/div>
Javad Ghofrani<\/a> \n \"ORCID<\/a>, Darian Lemke<\/a>, Tassilo S\u00f6ldner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 72-81<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Twins for Production<\/h4>\n
RAPIDZ \u2014 Resource analysis and process integration through digital twins<\/div>
Christian Salzig<\/a> \n \"ORCID<\/a>, Julia Burr<\/a> \n \"ORCID<\/a>, Sophie Hertzog<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In today\u2019s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 6-12 | DOI 10.30844\/I4SE.25.3.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"STAG\n <\/picture>\n <\/div>\n
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STAG \u2014 Bridging Data from Shop Floor to IT World<\/h4>\n
An automated mapping approach for improved access to shop floor data<\/div>
Oliver Amft<\/a> \n \"ORCID<\/a>, Dovydas Girdvainis<\/a> \n \"ORCID<\/a>, Christoph Rathfelder<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Collecting data from different sources on the shop floor and making it accessible to different IT systems is one of the core tasks during the process of factory digitization. Due to the different protocols and interfaces, the data collection task comes with unique challenges. With the Sensor Technology Adapter Gateway (STAG), we present a solution that closes the gap between the shop floor and the IT system\u2019s backend. STAG is an industry-grade middleware that automates translations between data models and protocols. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 14-22 | DOI 10.30844\/I4SE.25.3.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n5<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103459,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103465","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=103465"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103465\/revisions"}],"predecessor-version":[{"id":103466,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103465\/revisions\/103466"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103459"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}