{"id":113977,"date":"2026-06-10T13:44:20","date_gmt":"2026-06-10T11:44:20","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=113977"},"modified":"2026-06-10T16:45:29","modified_gmt":"2026-06-10T14:45:29","slug":"human-models-optimized-assembly","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/human-models-optimized-assembly\/","title":{"rendered":"Optimized Manual Processes in Automotive Production"},"content":{"rendered":"

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The automotive industry is undergoing a period of profound transformation characterized by increasing product diversity, shorter product lifecycles, and rising demands for flexibility and sustainability [1]. Among other things, these developments are leading to ever-increasing complexity in production planning and presenting new challenges for planning managers. The need for end-to-end, digitally supported planning is correspondingly high in order to efficiently manage the flow of information between development, planning, and production and to make informed decisions as early as the initial development phases [2]. Despite increasing automation, assembly is still characterized by …<\/div>\n

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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✓<\/span><\/td>\n0,00 \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 \"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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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
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Digital Competence Lab (DCL) for Speech Therapy<\/h4>\n
Designing a learning platform to advance digital skills<\/div>
Anika Thurmann<\/a> \n \"ORCID<\/a>, Antonia Weirich<\/a> \n \"ORCID<\/a>, Kerstin Bilda<\/a>, Fiona D\u00f6rr<\/a> \n \"ORCID<\/a>, Lars T\u00f6nges<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The digital transformation of healthcare results in lasting changes in speech therapy. Smart technologies and artificial intelligence (AI) are creating new opportunities to ensure therapy quality, address care bottlenecks, and actively involve patients in exercise processes. At the same time, these developments are expanding the role of speech therapists, who increasingly use digital systems as supportive tools in addition to their core therapeutic tasks. Based on a feasibility study of the AI-supported application ISi-Speech-Sprechen in a real-world setting of complex Parkinson's therapy (PKT), this article outlines the key challenges associated with implementing smart technologies. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 110-118 | DOI 10.30844\/I4SE.26.1.102<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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AI Implementation in Industrial Quality Control<\/h4>\n
A design science approach bridging technical and human factors<\/div>
Erdi \u00dcnal<\/a> \n \"ORCID<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a>, Pavlos Rath-Manakidis<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, Felix Hoenig<\/a>, Christian Meske<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation\/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844\/I4SE.26.1.112<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

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.<\/p>\n","protected":false},"featured_media":113805,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80275,84171,72429,79661,84285,79629,84344,80214,85879],"product_cat":[79304],"topic":[68005,79333],"technology":[67717],"knowhow":[],"industry":[69251,79494],"writer":[85680,85681,81417,85679],"content-type":[83932],"potential":[],"solution":[69358],"glossary":[],"class_list":["post-113977","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-assembly-en","tag-digital-factory-en","tag-digital-human-models","tag-digitale-fabrik-en","tag-ergonomics-en","tag-montage-en","tag-production-planning-en","tag-simulation-en","tag-time-analysis","product_cat-articles","topic-automation","topic-process-optimization","technology-simulation-en","industry-automotive-en","industry-manufacturing-en","writer-barbara-brockmann","writer-beate-stoffels","writer-jochen-deuse-en","writer-tobias-jurk","content-type-article","solution-assembly","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-64x36.webp",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"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,…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/113977","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\/113805"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=113977"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=113977"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=113977"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=113977"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=113977"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=113977"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=113977"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=113977"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=113977"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=113977"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=113977"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=113977"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=113977"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}