{"id":103453,"date":"2024-02-20T14:07:40","date_gmt":"2024-02-20T13:07:40","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103453"},"modified":"2024-05-14T04:34:11","modified_gmt":"2024-05-14T02:34:11","slug":"simulation","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/tools\/simulation\/","title":{"rendered":"Simulation"},"content":{"rendered":"\n\n\n
\n
\n \n
\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Biomechanical\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Biomechanical Simulation Pipeline for Exoskeletons<\/h4>\n
A digital tool for the targeted development of support systems<\/div>
Robert Eberle<\/a> \n \"ORCID<\/a>, Maximilian Ebenbichler<\/a> \n \"ORCID<\/a>, Benjamin Reimeir<\/a> \n \"ORCID<\/a>, Lennart Ralfs<\/a> \n \"ORCID<\/a>, Robert Weidner<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Support systems like exoskeletons can reduce physical strain on workers in industrial workplaces. To facilitate their development, a simulation pipeline was created. This pipeline employs musculoskeletal human models coupled with an exoskeleton model, enabling detailed analyses of the biomechanical interaction between humans and exoskeletons. By implementing exoskeleton structures and integrating them into existing musculoskeletal models, the pipeline aims to optimize exoskeleton development while simultaneously enhancing their biomechanical effectiveness. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 30-36 | DOI 10.30844\/I4SE.25.5.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Assistance\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Assistance for Simulation in Production and Logistics<\/h4>\n
A literature-based classification<\/div>
Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Felix \u00d6zkul<\/a>, Robin Sutherland<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Despite the commercial availability of simulation tools, using of discrete-event simulation for complex production and logistics systems is becoming increasingly challenging. It requires extensive expertise, high data quality, and considerable time and financial resources. For many years, therefore, there has been high demand for methodological and organizational support for the conduction of simulation studies. This article is based on an analysis of relevant publications and aims to classify previous research on improving the use of simulation. It also raises the question of the need for assistance in applying discrete event simulation and identifies areas for action. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 66-76 | DOI 10.30844\/I4SE.25.5.64<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Camera-Based\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Camera-Based Ergonomics Assessment<\/h4>\n
Developing a method for use in manual assembly<\/div>
Jannik Liebchen<\/a> \n \"ORCID<\/a>, Burak Vur<\/a> \n \"ORCID<\/a>, Michael Freitag<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Targeted ergonomic design of workplaces and processes can counteract the challenges of manual assembly and improve working conditions. However, current expert ergonomics assessments are time-consuming and resource-intensive. This article presents an automated assessment method based on the Rapid Upper Limb Assessment (RULA). Results from a laboratory study within an assembly scenario are consistent with expert evaluations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 120-126 | DOI 10.30844\/I4SE.25.5.116<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Enabling\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n
\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
\n \n
\n \n \n \n \"The\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n
\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
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Virtual\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Virtual Exhibition as a Digital Twin<\/h4>\n
A framework for decision-making for virtual representations<\/div>
Isger Glauninger<\/a> \n \"ORCID<\/a>, Markus Sch\u00fcrmann<\/a>, Matthias M\u00fchl<\/a>, Christian van Husen<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Transforming formats such as showrooms, laboratories or exhibitions into a virtual presence offers both opportunities and challenges. Particularly with cyber-physical systems (CPS), which rely heavily on user interaction, extensive adaptations must be made in order to maintain their purpose and function virtually. As part of this research project, digital solutions from different technologies and fields of application were transferred to a virtual exhibition. On this basis, the influence of the digital transformation on the interactivity and emulation of the solutions was analyzed. This article presents a framework that supports practitioners in the implementation of virtual representations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 110-116<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Open-Source\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Open-Source and Cost-Effective Digital Twin<\/h4>\n
A case study with two weeks to succeed<\/div>
Shantall Cisneros Saldana<\/a> \n \"ORCID<\/a>, Sonali Pratap<\/a>, Parth Punekar<\/a>, Sampat Acharya<\/a>, Heike Markus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digital Twin (DT) adoption remains a challenge due to high costs, complexity and lack of skills. This study proposes a cost-effective, TRL 5-validated DT model that can be built using open-source and office suite tools within just two weeks. Integrating real-time sensor data, predictive analytics, anomaly detection and notification, the model improves efficiency and sustainability in agriculture. Even with cloud service constraints, the system delivers a 7.76% average relative error and rapid, automated notifications. The findings show how open-source in combination with common commercial tools technologies can make advanced digital tools accessible to all, creating scalable, human-centered, and affordable solutions in line with Industry 5.0 principles. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 62-68 | DOI 10.30844\/I4SE.25.3.62<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n11<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103435,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103453","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\/103453","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=103453"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103453\/revisions"}],"predecessor-version":[{"id":103454,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103453\/revisions\/103454"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103435"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}