{"id":94199,"date":"2021-04-15T12:00:00","date_gmt":"2021-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/digital-twin-in-plastics-technology-766d25ad27ae5fc4bfbff39563c1373b45ab27ba9466d1600304b33d117ca5f4e2766d25ad27ae5fc4bfbff39563c1373b45ab27ba9466d1600304b33d117ca5f488766d25ad27ae5fc4bfbff39563c\/"},"modified":"2025-08-01T11:00:02","modified_gmt":"2025-08-01T09:00:02","slug":"digital-twin-in-plastics-technology-%e2%88%92-lifetime-optimized-production-of-technical-components-by-using-data-driven-methods","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/digital-twin-in-plastics-technology-%e2%88%92-lifetime-optimized-production-of-technical-components-by-using-data-driven-methods\/","title":{"rendered":"Digital Twin in Plastics Technology"},"content":{"rendered":"

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

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

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<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The quality of injection-molded components is becoming increasingly important in polymer technology due to extended areas of application with higher mechanical loads. As established methods of quality assurance are increasingly reaching their limits, the digital twin as a basis for cross-process and cross-company data analysis opens up new possibilities in plastics technology for proactive and predictive monitoring and improvement of process and component quality when processing plastics into technical components.<\/p>\n","protected":false},"featured_media":96025,"menu_order":0,"template":"","categories":[],"tags":[80100,79631,84254,79574,84255],"product_cat":[3300],"topic":[67838],"technology":[71297],"knowhow":[],"industry":[],"writer":[83316,83250,83315,81417,83251],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":{"0":"post-94199","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"tag-digital-twin-en","7":"tag-digitaler-zwilling-en","8":"tag-lifetime-optimized-production-en","9":"tag-machine-learning-en","10":"tag-plastics-technology-en","11":"product_cat-article","12":"topic-digital-twin","13":"technology-machine-learning","14":"writer-hans-peter-heim-en","15":"writer-jacqueline-schmitt-en","16":"writer-jan-christoph-zarges-en","17":"writer-jochen-deuse-en","18":"writer-ralph-richter-en","19":"product","20":"first","21":"instock","22":"downloadable","23":"virtual","24":"sold-individually","25":"taxable","26":"purchasable","27":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/schmitt-Digitaler-Zwilling-in-der-Kunststofftechniky-IM_2021_2-1400x788-64x36.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":"The quality of injection-molded components is becoming increasingly important in polymer technology due to extended areas of application with higher mechanical loads. As established methods of quality assurance are increasingly reaching their limits, the digital twin as a basis for cross-process and cross-company data analysis opens up new possibilities in plastics technology for proactive and…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94199","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\/96025"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94199"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94199"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94199"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94199"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94199"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94199"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94199"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94199"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94199"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94199"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94199"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94199"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94199"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}