{"id":109077,"date":"2025-06-13T00:14:44","date_gmt":"2025-06-12T22:14:44","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=109077"},"modified":"2025-06-18T11:55:40","modified_gmt":"2025-06-18T09:55:40","slug":"learning-factories","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/learning-factories\/","title":{"rendered":"Hybrid Learning Landscapes for Technical Concepts"},"content":{"rendered":"

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The Cross-Regional and Cross-Phase Interface (LPI) promotes the digitalization of vocational and technical teaching through sustainable and effective impulses. It builds on the systematic preparation and provision of relevant expertise from the LPI network (TU Munich, TU Darmstadt, University of Stuttgart and University of Education Schw\u00e4bisch Gm\u00fcnd). The joint project is realized in cooperation with the project teams within the competence network lernen.digital. The excellent cross-phase networking approach promotes the exchange of best practices and innovative solutions, resulting in sustainable added value for all participants. The LPI project works to …<\/div>\n

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
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Industry 4.0\u2014Progress and Digitalization in Limbo<\/h4>\n
Status of sustainable transformation and digitalization in production engineering<\/div>
Christian Donhauser<\/a> \n \"ORCID<\/a>, Daniel Riepl<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 56-60<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Application\n <\/picture>\n <\/div>\n
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Application Potentials of Chinese Knowledge Platforms<\/h4>\n
Digital platforms for knowledge transfer in research and education<\/div>
Yunhao Su<\/a>, Martin Braun<\/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>Knowledge drives innovation, which is why digital platforms are increasingly used for knowledge transfer. The People\u2019s Republic of China (PRC) is a global leader in digitalization and digital platforms are central to Chinese knowledge transfer and innovation systems. This study supplements theoretical concepts of knowledge transfer with empirical findings on the (further) development of relevant knowledge platforms. It examines the influence of specific design features on the functionality and quality of digital knowledge platforms. A literature review identifies seven condensed success criteria. Nine leading Chinese knowledge platforms are categorized based on their transfer logic and functional scope. Online survey participants assess the platform-specific manifestations of the identified criteria and highlight potential and areas for improvement in platform-based knowledge transfer. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 84-93<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"VR\n <\/picture>\n <\/div>\n
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VR Training for Multimodal Cobot Interaction<\/h4>\n
Virtual learning environments for collaborative robots<\/div>
Christoph S. Zoller<\/a>, Justus Langer<\/a>, Kristoffer Waldow<\/a> \n \"ORCID<\/a>, Merle Meyer<\/a>, Arnulph Fuhrmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The VIRAMM research project is developing and prototyping a VR-based training concept for the integration of collaborative robots (cobots) in assembly-oriented U-cells. Since the benefits of cobots depend heavily on process, layout, and role integration, VIRAMM addresses the previously lacking consistent scenario design for variant comparisons with Key Performance Indicator (KPI)-based evaluation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 106-112<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Decentralized\n <\/picture>\n <\/div>\n
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Decentralized Coordination of AMRs<\/h4>\n
Regulations for Autonomous Mobile Robots<\/div>
Manuel Savadogo<\/a>, Malte Stonis<\/a> \n \"ORCID<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a>, J\u00fcrgen Hupp<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>The increasing automation of intralogistics requires flexible and resilient control concepts for Autonomous Mobile Robots (AMR). While centralized coordination approaches enable stringent control, they quickly reach their limits in terms of scalability and robustness. This paper therefore presents regulations for the decentralized coordination of AMR within the framework of the ORPHEUS project. The focus is on translating known decentralized decision-making principles into a rule framework tailored to industrial material flow scenarios, addressing both operational task assignment and safety-related conflict situations. ORPHEUS thus makes a significant contribution to the methodological structuring, parameterization, and practical transferability of decentralized coordination logics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 96-105<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Open-Source\n <\/picture>\n <\/div>\n
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Open-Source Implementation of the Industrial Metaverse<\/h4>\n
Case study and best practices<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Tim Johannsen<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>The digital transformation of small and medium-sized enterprises (SMEs) in the manufacturing sector is hampered by vendor lock-in, high cloud costs, and stringent data sovereignty requirements when implementing Industrial Metaverse solutions. Although the Industrial Metaverse is quickly becoming a key concept in Industry 5.0, SMEs are often at a disadvantage when using proprietary solutions. This paper demonstrates how Industrial Metaverse applications can be realized by combining proven communication standards with open web technologies, thereby reducing barriers. This makes immersive applications for training, maintenance, and monitoring feasible even in SMEs. Using an open-source-based prototype as a best-practice implementation, the paper illustrates how the Industrial Metaverse can be made technologically and economically accessible to SMEs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 3 | Pages 68-73<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Immersive\n <\/picture>\n <\/div>\n
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Immersive Human Digital Twins for Industry 4.0<\/h4>\n
Supporting adaptive human-centric production by integrating cognitive and physical states<\/div>
Tajbeed A. Chowdhury<\/a> \n \"ORCID<\/a>, Martina Lehser<\/a> \n \"ORCID<\/a>, Eric Wagner<\/a> \n \"ORCID<\/a>, Paul Motzki<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The rapid advancement of immersive technologies has created new opportunities to transform human-machine collaboration in industry. This paper presents an immersive platform with a digital twin that combines both physical and cognitive characteristics of human dynamics. By integrating multimodal sensing, human biomechanics, and cognitive state into digital twin technology, the proposed system enhances operational safety and ensures better ergonomics. The main argument is that human digital twins are not only desirable but essential for next-generation industrial systems. We discuss the limitations of existing human modeling approaches, outline the conceptual foundations of human digital twins, and demonstrate their industrial relevance across safety, productivity, ergonomics and sustainability. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 6-13 | DOI 10.30844\/I4SE.26.3.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The L\u00e4nder- und Phasen\u00fcbergreifende Interface (LPI) (engl. Cross-Regional and Cross-Phase Interface) promotes the sustainable digitalization of vocational and technical education through the systematic provision of expertise and innovative networking formats. The focus is on hybrid learning landscapes (HLL), which interlink physical and digital learning spaces to create individualized, practical learning environments. Innovative approaches such as learning factories, VR\/AR and learning analytics are integrated. <\/p>\n","protected":false},"featured_media":108836,"menu_order":0,"template":"","categories":[79167,79298],"tags":[79449,79627],"product_cat":[79304],"topic":[68206,79319],"technology":[79493,67596,67634,68059],"knowhow":[],"industry":[],"writer":[],"content-type":[83932],"potential":[67894,67726],"solution":[],"glossary":[],"class_list":["post-109077","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","tag-digitalisierung-en","tag-industrie-4-0-en","product_cat-articles","topic-industry-4-0","topic-platforms","technology-digitalization","technology-software-en","technology-tools","technology-training","content-type-article","potential-innovation-en","potential-training","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Anselmann_Beitragsbild-64x36.webp",64,36,true]},"uagb_author_info":{"display_name":"Chloe Hayward","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"The L\u00e4nder- und Phasen\u00fcbergreifende Interface (LPI) (engl. 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