{"id":103417,"date":"2024-02-20T13:31:55","date_gmt":"2024-02-20T12:31:55","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103417"},"modified":"2024-05-13T19:50:16","modified_gmt":"2024-05-13T17:50:16","slug":"assembly","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/assembly\/","title":{"rendered":"Assembly"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"Applied\n <\/picture>\n <\/div>\n
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Applied AI for Human-Centric Assembly Workplace Design<\/h4>\n
An ethics-informed approach<\/div>
Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Michael Jonek<\/a> \n \"ORCID<\/a>, Sascha Niethammer<\/a>, Henning Vogler<\/a>, Martin Manns<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection\u00ae. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844\/I4SE.26.1.58<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Quiz:\n <\/picture>\n <\/div>\n
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Quiz: Manufacturing in Space<\/h4>\n
Test your knowledge!<\/div> <\/td>\n <\/tr>\n <\/table>\n
\nWeightless production\u2014just science fiction or already reality? Thanks to new space technologies, the first production processes are now emerging in space that enable materials and structures to be created that are virtually impossible to manufacture on Earth. From ultra-pure fibers to 3D printing of organs, weightlessness is opening up completely new perspectives for industries\u2014and bringing space manufacturing closer to the present than many people think. <\/div>\n <\/div>\n <\/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 \"Training\n <\/picture>\n <\/div>\n
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Training in the Industrial Metaverse<\/h4>\n
Buzzword or opportunity?<\/div>
Leon Schellhammer<\/a> \n \"ORCID<\/a>, Lucas Waag<\/a>, Mert Cumert<\/a>, Dieter Uckelmann<\/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>Metaverse-based training programs offer a realistic and risk-free learning environment that is particularly valuable in industrial contexts, e.g. in immersive training and the simulation of workflows. Challenges remain in the areas of data protection, technological acceptance and integration into existing systems.\u00a0Using a carefully crafted questionnaire, four expert interviews were conducted to investigate whether the metaverse can innovate training programs effectively and lastingly. Its standardized format yields comparable, reliable data while allowing for an accurate evaluation of the results. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 102-108<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Boosting Competitiveness in Small Batch Production<\/h4>\n
Scalable and flexible body-in-white production line with collaborative mobile robots<\/div>
Walid Elleuch<\/a>, Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Martin Manns<\/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>Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the pillars of a resilient production system. This article presents a conceptual solution for flexible Body-in-White sheet metal production with autonomous collaborative robotic systems to make product costs affordable for a higher competitive advantage. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 60-67<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"The\n <\/picture>\n <\/div>\n
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The \u201cInTraLab\u201d Learning Factory<\/h4>\n
Gaining experience and knowledge in digitally transformed work environments<\/div>
Norbert Gronau<\/a> \n \"ORCID<\/a>, Malte Rolf Teichmann<\/a>, Malte Teichmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Learning factories offer a practical environment for simulating production processes in which learners can acquire skills through the direct application of new technologies. The Industrial Transformation Lab (InTraLab) models hybrid production processes by combining real-world demonstrators and virtual simulations. This enables learners to acquire the skills that are crucial for the digitally transformed world of work. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 46-51<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
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Digital Twins Using Semantic Modeling and AI<\/h4>\n
Self-learning development and simulation of industrial production facilities<\/div>
Wolfram H\u00f6pken<\/a> \n \"ORCID<\/a>, Ralf Stetter<\/a> \n \"ORCID<\/a>, Markus Pfeil<\/a> \n \"ORCID<\/a>, Thomas Bayer<\/a> \n \"ORCID<\/a>, Bernd Michelberger<\/a>, Markus Till<\/a>, Timo Schuchter<\/a>, Alexander Lohr<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The AI-driven, self-learning digital twin continuously adapts to real system behavior, ensuring an optimal representation of the production process. A comprehensive semantic model serves as the foundation for advanced artificial intelligence (AI) approaches. Insights derived from AI methods are integrated into this model, enhancing the interpretability and explainability of AI systems. Techniques from the field of eXplainable AI (XAI) facilitate the automated description of AI models and their findings, as well as the development of self-explanatory models. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 30-36<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Work-Integrated\n <\/picture>\n <\/div>\n
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Work-Integrated Learning in Industry 4.0<\/h4>\n
A qualitative analysis of various assistance systems in assembly<\/div>
Kathleen Warnhoff<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In the era of Industry 4.0, many industrial companies are facing major transformations. In the process of digitalization, factory management is adopting new technologies such as cognitive assistance systems, which has led to changes in work processes. Regarding assembly in the metal and electrical industries, it is unclear to what extent this development has promoted work-integrated learning. Therefore, the topic of this paper is a qualitative analysis that explores employees' perceptions of the learning opportunities and risks presented by cognitive assistance systems. Results: Not all assembly employees benefit equally from these new developments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 20-29 | DOI 10.30844\/I4SE.25.2.20<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Assembly\n <\/picture>\n <\/div>\n
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Assembly in Transition<\/h4>\n
Empirical results of digitalization<\/div>
Mathias K\u00f6nig<\/a> \n \"ORCID<\/a>, Herwig Winkler<\/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>Assembly is an important part of industrial production and is also characterized by a high proportion of manual work. Manufacturing companies have an intrinsic interest in increasing personnel productivity and preventing unit labor costs from rising. Many thus hope to gain economic benefits by implementing digitalization projects. The potential of digitalization in assembly must be exploited to achieve these goals. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 1 | Pages 42-49<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Setting\n <\/picture>\n <\/div>\n
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Setting Up Assembly Assistance Systems<\/h4>\n
System for the efficient configuration of assembly instructions and assistance functions<\/div>
Dennis Keiser<\/a>, Dario Niermann<\/a> \n \"ORCID<\/a>, Michael Freitag<\/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 industrial assembly, humans are working more closely with machines due to assembly assistance. However, despite their great potential, the implementation of digital systems is time-consuming, which entails high training requirements. Small and medium-sized businesses, in particular, are reaching their limits. A newly developed setup system is designed to facilitate the introduction and use of such assembly assistance systems and increase their acceptance. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 32-39<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n3<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103410,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103417","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\/103417","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=103417"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103417\/revisions"}],"predecessor-version":[{"id":103418,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103417\/revisions\/103418"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103410"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}