{"id":91255,"date":"2012-08-15T12:00:00","date_gmt":"2012-08-15T12:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/approximation-and-robustness-of-dynamic-production-networks\/"},"modified":"2012-08-15T12:00:00","modified_gmt":"2012-08-15T12:00:00","slug":"approximation-and-robustness-of-dynamic-production-networks","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/approximation-and-robustness-of-dynamic-production-networks\/","title":{"rendered":"Approximation and Robustness of Dynamic Production networks"},"content":{"rendered":"

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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
\n
\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 \"AI-Powered\n <\/picture>\n <\/div>\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
\n
\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 \"Industrial\n <\/picture>\n <\/div>\n
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Industrial Application of Immersive Technologies<\/h4>\n
Exploring XR solutions for training, instruction, design review, and assembly planning<\/div>
Andreas Straube<\/a> \n \"ORCID<\/a>, Faikar Zakky Haidar<\/a> \n \"ORCID<\/a>, Matheus Lenzi dos Santos<\/a> \n \"ORCID<\/a>, Kussai AI Jairoud<\/a> \n \"ORCID<\/a>, Eduardo Koscianski<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In recent years, the decreasing cost and improved usability of immersive hardware and software have made extended reality (XR) increasingly attractive for industrial applications. Stand-alone systems with inside-out tracking and camera-based pass-through enable accessible mixed reality (MR) solutions. At the same time, emerging no-code software platforms allow engineers to create XR environments without programming expertise, broadening adoption across production settings. This paper explores key industrial application areas of immersive technologies through selected commercially available XR software solutions for product and process training, spatial instructions and guides, collaborative design review, and assembly and production planning. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 38-47 | DOI 10.30844\/I4SE.26.3.4<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Learning\n <\/picture>\n <\/div>\n
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Learning Factories for the Future of Manufacturing in Brazil<\/h4>\n
Advancing manufacturing through technology and skills development<\/div> <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>\nManufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory F\u00e1brica do Futuroin S\u00e3o Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Serious\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Serious Gaming and the Energy Transition<\/h4>\n
Collaborative knowledge generation and interactive understanding of complex interrelationships<\/div>
Janine Gondolf<\/a> \n \"ORCID<\/a>, Gert Mehlmann<\/a>, J\u00f6rn Hartung<\/a>, Bernd Schweinshaut<\/a>, Anne Bauer<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 62-69<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Industrial\n <\/picture>\n <\/div>\n
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Industrial Transformation via a Machining Learning Factory<\/h4>\n
A learning module to foster competencies for a sustainability-driven transformation<\/div>
Oskay Ozen<\/a> \n \"ORCID<\/a>, Victoria Breidling<\/a> \n \"ORCID<\/a>, Stefan Seyfried<\/a> \n \"ORCID<\/a>, Matthias Weigold<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Sustainability-enhancing transformation processes are necessary in all sectors if we are to remain within planetary boundaries. This also applies to the industrial sector as a significant emitter of greenhouse gases. Employees need new competencies to master this complex task of industrial transformation. These range from CO2 equivalents accounting to the development and evaluation of transformation scenarios, including technical measures. The learning module developed here addresses these competency requirements and uses the example of the ETA factory to show how a competency-oriented learning module for industrial transformation can be structured. It essentially comprises four phases: data collection and CO2 equivalents accounting, cause analysis, development of measures and evaluation of measures. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 38-47 | DOI 10.30844\/I4SE.26.2.38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Global production networks connect partners with outstanding expertise, and make use of regional cost advantages for purchasing and production operations. This development leads to an increasing structural complexity of the networks, which is accompanied by a closer collaboration of dynamic logistics processes. Hence, the resulting dynamics of a large-scale production network is characterized by the dynamics of the individual logistics processes, the dynamics of the network structure and dynamics of the external processes that affect the production network. However, in practice a lack of adequate procedures for the analysis and design of these networks can be observed. The presented article addresses this need by introducing tools and methods for the approximation of large-scale production networks, analysis of their dynamics and the robust design of the network resources.<\/p>\n","protected":false},"featured_media":97587,"menu_order":0,"template":"","categories":[],"tags":[],"product_cat":[79304],"topic":[67617,67701],"technology":[67717],"knowhow":[],"industry":[],"writer":[80589,81489,82081,82082,81490,81491],"content-type":[],"potential":[68923],"solution":[],"glossary":[],"class_list":["post-91255","article","type-article","status-publish","has-post-thumbnail","product_cat-articles","topic-adaptability","topic-production-system","technology-simulation-en","writer-bernd-scholz-reiter-en","writer-fabian-wirth-en","writer-michael-kosmykov-en","writer-michael-schoenlein-en","writer-sergey-dashkovskiy-en","writer-thomas-makuschewitz-en","potential-dynamics","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\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scholz-reiter_Approximation_und_Robustheit_dynamischer_Produktionsnetzwerke_IM_2012_4-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/scho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Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Global production networks connect partners with outstanding expertise, and make use of regional cost advantages for purchasing and production operations. This development leads to an increasing structural complexity of the networks, which is accompanied by a closer collaboration of dynamic logistics processes. Hence, the resulting dynamics of a large-scale production network is characterized by the…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/91255","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\/97587"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=91255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=91255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=91255"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=91255"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=91255"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=91255"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=91255"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=91255"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=91255"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=91255"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=91255"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=91255"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=91255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}