{"id":94651,"date":"2022-10-15T12:00:00","date_gmt":"2022-10-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=94651"},"modified":"2024-09-25T13:51:49","modified_gmt":"2024-09-25T11:51:49","slug":"multidimensional-maturity-model-for-digital-twins","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/multidimensional-maturity-model-for-digital-twins\/","title":{"rendered":"Multidimensional Maturity Model for Digital Twins"},"content":{"rendered":"

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n−<\/td>\n26,10 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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AI-Powered Lubrication Strategies for Thread Forming<\/h4>\n
Adaptive spray jet control to increase process reliability and tool life<\/div>
Reinhard Schmied<\/a>, Marco Susic<\/a>, Christian Donhauser<\/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>Thread forming requires precise lubricant application because high contact pressures and process temperatures strongly influence tool loading, friction, and process stability. Although minimum quantity lubrication (MQL) systems are widely used, current spray-based approaches can still suffer from spray losses, insufficient wetting of the thread grooves, and unstable droplet transport. This article presents a concept for adaptive precision lubrication in thread forming based on computational fluid dynamics (CFD)-supported flow analysis, experimental validation, and artificial intelligence (AI)-assisted optimization. The focus is on droplet size, spray jet geometry, nozzle position, ambient flow conditions, and their influence on wetting intensity. Preliminary simulation-based investigations indicate that data-driven optimization can help identify wetting deficiencies and support the development of future control strategies for resource-efficient lubricant application. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2027 | Edition 3 | Pages 76-83<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Optimized\n <\/picture>\n <\/div>\n
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Optimized Manual Processes in Automotive Production<\/h4>\n
A module-based approach for the efficient creation of work system simulations<\/div>
Barbara Brockmann<\/a>, Tobias Jurk<\/a>, Beate Stoffels<\/a>, Jochen Deuse<\/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>In the manufacturing industry, the integration of digital human models into the product development and manufacturing process is becoming increasingly important. Particularly in assembly, which is characterized by a high proportion of manual tasks, motion simulations enable a realistic representation of human work and thus make a significant contribution to the evaluation of motion economy, process validation, and efficiency improvement. However, widespread application in production planning faces various challenges, such as the high initial effort required to create human simulations as well as volatile planning conditions. This article presents a practice-oriented solution from the automotive assembly sector that enables the creation of simulations with reduced effort as well as their early and consistent use in the planning process. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 48-55<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"SmartBending\u2014Inline\n <\/picture>\n <\/div>\n
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SmartBending\u2014Inline Measurement for Process Correction<\/h4>\n
Inline process optimization for error compensation in swivel bending<\/div>
Christian Donhauser<\/a> \n \"ORCID<\/a>, Reinhard Schmied<\/a>, Marco Susic<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Swivel bending is an established forming process that minimizes material loss and enables efficient use of resources. However, the process requires complex optimizations that have traditionally relied heavily on the expertise of machine operators. This results in significant time and material costs, as optimization steps are performed iteratively. Given the shortage of skilled workers, a technological upgrade of the machines in line with Industry 4.0 is necessary. As part of a research project, intelligent sensor technology was used to record critical influencing factors that reveal correlations between product defects and machine deformations. Based on this, a methodology was developed that forms the foundation for inline compensation, enabling the equipment to autonomously adjust process parameters to correct product defects and, in the long term, enable defect-free production from the very first component. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 134-141<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Twin Technology and Architecture<\/h4>\n
A synthesis of concept and practice<\/div>
Arka Mukherjee<\/a> \n \"ORCID<\/a>, Shibaji Chandra<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Digital twins are a key enabling technology of the fourth industrial revolution, integrating physical systems with their digital counterparts to create intelligent, data-driven environments. This conceptual\/practice-oriented paper examines how to establish a modern architectural framework for digital twins leverages modern tech-stack like IoT, Data Fabric, AI\/ML, seamless integration and enterprise grade security. The paper is grounded in an abundance of literature by leading vendors and analysts in space. It offers a comparative study of different vendors implementing the solution stack in the proposed architecture. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 114-122<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Developing\n <\/picture>\n <\/div>\n
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Developing Virtual Reality in Learning Contexts<\/h4>\n
Navigating efficiency, content relevance and scalability<\/div>
Stella Kanatouri<\/a> \n \"ORCID<\/a>, Oliver Sosna<\/a> \n \"ORCID<\/a>, Alexander Kulik<\/a>, Sina C. Truckenbrodt<\/a> \n \"ORCID<\/a>, Friederike Klan<\/a> \n \"ORCID<\/a>, Christian Erfurth<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n While virtual reality can facilitate hands-on learning, its development faces barriers, including high costs and time demands and scalability challenges. This article presents two case studies that illustrate strategies for overcoming such barriers when training the next generation of skilled workers in environmental technologies. By examining approaches for streamlining development and increasing content relevance and scalability, we highlight lessons learned for future practice. We conclude by envisioning a future in which educational institutions can flexibly and cost-effectively prototype virtual reality in learning contexts, ensuring alignment with curricular goals and learners\u2019 needs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 3 | Pages 26-34 | DOI 10.30844\/I4SE.26.3.3<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\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":"

Digital twins are an important part of the Industry 4.0 idea. They mirror physical goods in the digital world and enhance them with additional capabilities and functions for analysis, forecasting and decisionmaking. This paper contributes to the classification and assessment of Digital Twins using a multidimensional maturity model. The presented method “DT-Assess” enables an application-specific assessment of Digital Twins. The developed maturity model consists of seven categories with a total of 31 characteristics to be evaluated. The systematic evaluation in five application scenarios allows, for the first time, a classification of the respective “digital twin” implementation or concept with the aim of identifying further development options and weaknesses.<\/p>\n","protected":false},"featured_media":95957,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[79631,80025],"product_cat":[79304],"topic":[67838,79333],"technology":[67790,67717,68434],"knowhow":[],"industry":[],"writer":[83479,83536,83127,80795,80740,81209],"content-type":[],"potential":[68057],"solution":[],"glossary":[],"class_list":["post-94651","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-digitaler-zwilling-en","tag-kuenstliche-intelligenz-en","product_cat-articles","topic-digital-twin","topic-process-optimization","technology-artificial-intelligence","technology-simulation-en","technology-virtual-reality-en","writer-eike-broda-en","writer-jan-frederik-uhlenkamp-en","writer-jasper-wilhelm-en","writer-klaus-dieter-thoben-en","writer-michael-freitag-en","writer-michael-luetjen-en","potential-management-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Broda_Reifegradmodell_IM-5_2022-1400x788-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Christoph Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Digital twins are an important part of the Industry 4.0 idea. They mirror physical goods in the digital world and enhance them with additional capabilities and functions for analysis, forecasting and decisionmaking. This paper contributes to the classification and assessment of Digital Twins using a multidimensional maturity model. The presented method \"DT-Assess\" enables an application-specific…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94651","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/95957"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94651"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94651"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94651"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94651"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94651"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94651"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94651"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94651"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94651"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94651"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94651"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94651"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94651"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}