{"id":94311,"date":"2021-08-15T12:00:00","date_gmt":"2021-08-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=94311"},"modified":"2024-04-16T16:32:59","modified_gmt":"2024-04-16T14:32:59","slug":"project-lotus-energetic-optimization-of-parts-drying","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/project-lotus-energetic-optimization-of-parts-drying\/","title":{"rendered":"Project LoTuS &#8211; Energetic Optimization of Parts Drying"},"content":{"rendered":"<hr>\n<div class=\"gito-pub-content-bibliography\">\n<h2>Bibliography <\/h2>\n[1] Bilz, M.: Markt- und Trendanalyse in der Industriellen Teilereinigung 2012. Fraunhofer-Allianz Reinigungstechnik. Berlin 2013.<br \/>\n[2] Potenzial f\u00fcr kosten- und qualit\u00e4tsoptimierte Prozesse. In: JOT Journal f\u00fcr Oberfl\u00e4chentechnik 50 (2010) 5, S. 50-53.<br \/>\n[3] B\u00fcrkle, G.: Reinigung und Reiniger in der Metallindustrie. In: Technische Akademie Esslingen (TAE). URL: www.tae.de\/uploads\/media\/Reinigung_und_Reiniger_in_der_Metallindustrie_-_TAE.pdf, Abrufdatum 22.02.2021.<br \/>\n[4] Seefeldt, F.; W\u00fcnsch, M.; Matthes, U.; Baumgartner, W.: Potenziale f\u00fcr Energieeinsparung und Energieeffizienz im Lichte aktueller Preisentwicklungen. Endbericht 18\/06. Basel Berlin 2007.<br \/>\n[5] Nast, M.; Frisch, S.; Pehnt, M.; Otter, P.: Prozessw\u00e4rme im Marktanreizprogramm: Zwischenbericht zu Perspektivische Weiterentwicklung des Marktanreizprogramms (2010). URL: https:\/\/elib.dlr.de\/82173\/1\/Prozessw%C3%A4rme_im_MAP.pdf, Abrufdatum 22.02.2021.<br \/>\n[6] Daimler AG; Henkel AG; PTW TU Darmstadt; TTD TU Darmstadt; MAFAC GmbH: Workshop Energieeffiziente Bauteilreinigung. Sitzungsprotokoll. Darmstadt 2017.<br \/>\n[7] Durkee, J. B.: Management of industrial cleaning technology and processes. 1st Edition. Amsterdam 2006.<br \/>\n[8] Blesl, M.; Kessler, A.: Energieeffizienz in der Industrie. 2. Auflage. Berlin 2013.<br \/>\n[9] Paul, S.; Paul, R.: Grundlagen der Elektrotechnik und Elektronik 2: Elektromagnetische Felder und ihre Anwendungen. 2. Auflage. Bremen Hamburg 2019.<br \/>\n[10] Mujumdar, A. S. (Hrsg): Handbook of industrial drying. 4th Edition. Boca Raton, USA 2015.<br \/>\n[11] Schraml, P.: Methode zur Reduktion maximaler elektrischer Lasten spanender Werkzeugmaschinen. Technische Universit\u00e4t Darmstadt. Dissertation. Aachen 2018.<br \/>\n[12] Abele, E.: Gemeinsamer Schlussbericht zum Projekt ETA-Fabrik: Energieeffiziente Fabrik f\u00fcr interdisziplin\u00e4re Technologie- und Anwendungsforschung. Darmstadt 2019.<br \/>\n[13] Denkena, B. (Hrsg); Garber, T. (Hrsg): NCplus+ Prozess- und wertsch\u00f6pfungsorientiert gesteuerte Werkzeugmaschine: Abschlussbericht zum BMBF-Verbundprojekt (Berichte aus dem IFW). Garbsen 2013.<br \/>\n[14] Weigold, M.; Hanson, J.; Rinderknecht, S.; Panten, N.; Walther, J.; Grosch, B.: Gemeinsamer Schlussbericht zum Projekt PHI-Factory: Flexible elektrische Fabriknetzf\u00fchrung \u2013 zur system\u00fcbergreifenden Steigerung der Energieeffizienz unter den Anforderungen zuk\u00fcnftiger Verteilnetze mit regenerativer Energieerzeugung: F\u00f6rderbereich: Energieeffizienz in Industrie und Gewerbe, Handel und Dienstleistungen (GHD): Laufzeit: 01.12.2016-31.03.2020. Darmstadt 2020.<\/div>\n<div id=\"download-section\" class=\"gito-pub-download-section\" style=\"text-align:center;margin:20px;\">\n<h2>Your downloads<\/h2>\n<p><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"94311\" data-userid =\"0\" data-filename=\"elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF<\/button><\/div>\n<div class=\"gito-pub-tags-social-share\" style=\"display:flex;justify-content:space-between;\"><div>Tags: <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/simulation-en\/\">Simulation<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Project%20LoTuS%20%26%238211%3B%20Energetic%20Optimization%20of%20Parts%20Drying - https:\/\/industry-science.com\/en\/articles\/project-lotus-energetic-optimization-of-parts-drying\/\" data-action=\"share\/whatsapp\/share\" class=\"icon button circle is-outline tooltip whatsapp show-for-medium\" title=\"Share on WhatsApp\" aria-label=\"Share on WhatsApp\"><i class=\"icon-whatsapp\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.facebook.com\/sharer.php?u=https:\/\/industry-science.com\/en\/articles\/project-lotus-energetic-optimization-of-parts-drying\/\" data-label=\"Facebook\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); 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return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip linkedin\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" rel=\"noopener nofollow\"><i class=\"icon-linkedin\" aria-hidden=\"true\"><\/i><\/a><\/div><\/div><\/div><hr style=\"margin-top:0px;\">\n<h2 class=\"gito-pub-frontend-post-headline\">You might also be interested in<\/h2>\n<!-- GITO_PUB_POST start flex-container -->\n<div class=\"gito-pub-flex-container\">\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/experiential-knowledge-powered-ai\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schmauder_AdobeStock_2036790511_DC-Studio-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schmauder_AdobeStock_2036790511_DC-Studio-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schmauder_AdobeStock_2036790511_DC-Studio-196x180.webp\" alt=\"Experiential Knowledge Powered by AI\u00a0\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Experiential Knowledge Powered by AI\u00a0\">                  <table class=\"gito-pub-frontend-post-card-header\">\n                     <tr>\n                        <td>                           <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Experiential Knowledge Powered by AI\u00a0<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Practical insights from industry<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/autoren\/martin-schmauder\/\">Martin Schmauder<\/a> <a href=\"https:\/\/orcid.org\/0009-0002-8796-5093\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/autoren\/gritt-ott\/\">Gritt Ott<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-6208-6546\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/autoren\/bianca-windisch\/\">Bianca Windisch<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     The use of experiential knowledge is a key success factor for companies. Based on four corporate case studies, this article analyzes the technical, organizational, and personnel challenges associated with the use of retrieval-augmented generation (RAG) systems. The results show that the success of such systems depends on the strategic development and maintenance of the knowledge base, as well as on employee engagement.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 128-135 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.15\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.15<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/inclusive-work-system-design\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-196x180.webp\" alt=\"Inclusive Work System Design\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Inclusive Work System Design\">                  <table class=\"gito-pub-frontend-post-card-header\">\n                     <tr>\n                        <td>                           <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Inclusive Work System Design<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Automation, standardization, and adaptability<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/sebastian-schlund-en\/\">Sebastian Schlund<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-8142-0255\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     The design of inclusive work systems is gaining importance due to demographic change and the increasing digital penetration of value creation processes. While traditional ergonomic approaches are based primarily on percentile logic and thus address only a portion of the user population, the integration of digital technologies opens up new possibilities for the dynamic and individualized adaptation of work systems. This article presents a conceptual framework for inclusive work system design that integrates standardization, automation, and adaptability. Methodologically, the article is based on a conceptual analysis of existing approaches from ergonomics and human-centered design. The article makes a theoretical contribution to the systematization of inclusive work system design and identifies areas of focus for further research and industrial practice.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 70-76 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.8\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.8<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/mtm-analyses-ai-rule-algorithms\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/eckart_AdobeStock_1923313286_noppadon-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/eckart_AdobeStock_1923313286_noppadon-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/eckart_AdobeStock_1923313286_noppadon-196x180.webp\" alt=\"MTM Analyses with AI and Rule-Based Algorithms\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"MTM Analyses with AI and Rule-Based Algorithms\">                  <table class=\"gito-pub-frontend-post-card-header\">\n                     <tr>\n                        <td>                           <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">MTM Analyses with AI and Rule-Based Algorithms<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">An approach to interpreting textual process descriptions<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/constantin-eckart\/\">Constantin Eckart<\/a> <a href=\"https:\/\/orcid.org\/0009-0002-9922-0603\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/martin-benter-en\/\">Martin Benter<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-9336-0739\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/peter-kuhlang-en\/\">Peter Kuhlang<\/a> <a href=\"https:\/\/orcid.org\/0009-0005-3706-7588\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     MTM methods are a proven standard for analyzing and designing human work processes. However, work planners continue to face challenges in applying these methods correctly and efficiently. Artificial intelligence \u2014 particularly in the case of large language models \u2014 holds significant potential for combatting these challenges. However, the use of AI raises legitimate questions regarding the reliability and traceability of the results. The approach presented here combines LLMs with a rule-based algorithm to extract the information required for MTM analyses from textual process descriptions such as work instructions. This information is then translated into MTM analyses in accordance with the MTM methodology. This approach ensures that the resulting analyses can be transparently traced back to the original input data by the user.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 62-69 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.7\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.7<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-lubrication-thread-forming\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-196x180.webp\" alt=\"AI-Powered Lubrication Strategies for Thread Forming\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI-Powered Lubrication Strategies for Thread Forming\">                  <table class=\"gito-pub-frontend-post-card-header\">\n                     <tr>\n                        <td>                           <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI-Powered Lubrication Strategies for Thread Forming<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Adaptive spray jet control to increase process reliability and tool life<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/autoren\/reinhard-schmied\/\">Reinhard Schmied<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/marco-susic\/\">Marco Susic<\/a>, <a href=\"https:\/\/industry-science.com\/en\/autoren\/christian-donhauser\/\">Christian Donhauser<\/a> <a href=\"https:\/\/orcid.org\/0009-0009-0366-1828\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/ai-lubrication-thread-forming\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2027 | Edition 3 | Pages 76-83<\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/human-models-optimized-assembly\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-196x180.webp\" alt=\"Optimized Manual Processes in Automotive Production\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Optimized Manual Processes in Automotive Production\">                  <table class=\"gito-pub-frontend-post-card-header\">\n                     <tr>\n                        <td>                           <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Optimized Manual Processes in Automotive Production<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">A module-based approach for the efficient creation of work system simulations<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/autoren\/barbara-brockmann\/\">Barbara Brockmann<\/a>, <a href=\"https:\/\/industry-science.com\/en\/autoren\/tobias-jurk\/\">Tobias Jurk<\/a>, <a href=\"https:\/\/industry-science.com\/en\/autoren\/beate-stoffels\/\">Beate Stoffels<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/jochen-deuse-en\/\">Jochen Deuse<\/a> <a href=\"https:\/\/orcid.org\/0000-0003-4066-4357\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/human-models-optimized-assembly\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 48-55<\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/smartbending-inline-measurement-for-process-correction\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/susic-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/susic-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/susic-196x180.jpg\" alt=\"SmartBending\u2014Inline Measurement for Process Correction\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"SmartBending\u2014Inline Measurement for Process Correction\">                  <table class=\"gito-pub-frontend-post-card-header\">\n                     <tr>\n                        <td>                           <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">SmartBending\u2014Inline Measurement for Process Correction<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Inline process optimization for error compensation in swivel bending<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/autoren\/christian-donhauser\/\">Christian Donhauser<\/a> <a href=\"https:\/\/orcid.org\/0009-0009-0366-1828\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/autoren\/reinhard-schmied\/\">Reinhard Schmied<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/marco-susic\/\">Marco Susic<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/smartbending-inline-measurement-for-process-correction\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 134-141<\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n<\/div>\n<!-- GITO_PUB_POST end flex-container -->\n","protected":false},"excerpt":{"rendered":"<p>Due to rising quality requirements in the metalworking industry, parts drying has been gaining significance, leading to the increasing importance of reducing the energy consumption of drying processes. Therefore, the LoTuS project investigates different approaches to increase drying efficiency. Along with alternative drying technologies, process digitization is employed to provide sufficient transparency for part-specific drying. Using sensor data, artificial intelligence is utilized for process monitoring. Peak demand is further reduced by implementing load management techniques.<\/p>\n","protected":false},"featured_media":96081,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80214],"product_cat":[],"topic":[79333],"technology":[67717],"knowhow":[],"industry":[],"writer":[83382,83381,83383],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-94311","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-simulation-en","topic-process-optimization","technology-simulation-en","writer-adrian-von-hayn-en","writer-ghada-elserafi-en","writer-matthias-weigold-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\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/elserafi_Leistungsoptimierte-Trocknung-und-Sauberkeit_IM-2021_4-1400x788-64x36.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":"Due to rising quality requirements in the metalworking industry, parts drying has been gaining significance, leading to the increasing importance of reducing the energy consumption of drying processes. Therefore, the LoTuS project investigates different approaches to increase drying efficiency. Along with alternative drying technologies, process digitization is employed to provide sufficient transparency for part-specific drying.&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94311","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\/96081"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94311"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94311"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94311"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94311"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94311"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94311"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94311"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94311"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94311"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94311"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}