{"id":94303,"date":"2021-08-15T12:00:00","date_gmt":"2021-08-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=94303"},"modified":"2024-11-27T19:07:58","modified_gmt":"2024-11-27T18:07:58","slug":"industry-4-0-in-remanufacturing","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/industry-4-0-in-remanufacturing\/","title":{"rendered":"Industry 4.0 in Remanufacturing"},"content":{"rendered":"<hr>\n<div class=\"gito-pub-content-bibliography\">\n<h2>Bibliography <\/h2>\n[1] Chakraborty, K. u. a.: A Study on Remanufacturing Possibility of a Product. In: Microsystem Technologies 25 (5), S. 1765\u20131770 (2019).<br \/>\n[2] Steinhilper, R.: Remanufacturing: The Ultimate Form of Recycling. Stuttgart (1998).<br \/>\n[3] Ijomah, W. L. u. a.: Development of Robust Design-for-Remanufacturing Guidelines to Further the Aims of Sustainable Development. 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In: Journal of Cleaner Production 185, S. 562\u2013575 (2018).<br \/>\n[15] Okorie, O. u. a.: Towards a Simulation-Based Understanding of Smart Remanufacturing Operations: A Comparative Analysis. In: Journal of Remanufacturing 14 (2), (2020).<br \/>\n[16] Ondemir, O.; Gupta, S. M.: Quality Management in Product Recovery Using the Internet of Things: An Optimization Approach. In: Computers in Industry 65 (3), S. 491\u2013504 (2014).<br \/>\n[17] Gros, S. u. a.: Agentbased, Hybrid Control Architecture for Optimized and Flexible Production Scheduling and Control in Remanufacturing. In: Journal of Remanufacturing 14 (2), (2020).<br \/>\n[18] French, R. u. a.: Intelligent Sensing for Robotic Re-Manufacturing in Aerospace: An Industry 4.0 Design-Based Prototype. In: 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), S. 272\u2013277 (2017).<br \/>\n[19] Ruggeri, S. u. a.: Micro-Robotic Handling Solutions for PCB (Re-)Manufacturing. 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In: International Journal of Production Research 58 (16), S. 4916\u20134931 (2020).<br \/>\n[30] Chang, M. u. a.: AR-Guided Product Disassembly for Maintenance and Remanufacturing. In: Procedia CIRP 61, S. 299\u2013304 (2017).<br \/>\n[31] Mircheski, I.; Rizov, T.: Improved Nondestructive Disassembly Process Using Augmented Reality and RFID Product\/Part Tracking. In: TEM Journal 6 (4), S. 671\u2013676 (2017).<br \/>\n[32] Bentaha, M. L. u. a.: Profit-Oriented Partial Disassembly Line Design: Dealing with Hazardous Parts and Task Processing Times Uncertainty. In: International Journal of Production Research 56 (24), S. 7220\u20137242 (2018).<br \/>\n[33] Liu, M. u. a.: Robust Disassembly Line Balancing with Ambiguous Task Processing Times. In: International Journal of Production Research 58 (19), S. 5806\u20135835 (2020).<br \/>\n[34] Meng, W.; Zhang, X.: Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards. In: Sustainability 12 (18), S. 7318 (2020).<br \/>\n[35] Tian, G. u. a.: Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost. In: IEEE Transactions on Automation Science and Engineering 15 (2), S. 748\u2013760 (2018).<br \/>\n[36] Li, S. u. a.: Multi-Objective Disassembly Sequence Optimization Aiming at Quality Uncertainty of End-of-Life Product. In: IOP Conference Series Materials Science and Engineering 631 (3), S. 32015 (2019).<br \/>\n[37] Xia, X. u. a.: 3D-Based Multi-Objective Cooperative Disassembly Sequence Planning Method for Remanufacturing. In: The International Journal of Advanced Manufacturing Technology 106 (4), S. 4611\u20134622 (2020).<br \/>\n[38] Li, L. u. a.: An Integrated Approach of Reverse Engineering Aided Remanufacturing Process for Worn Components. In: Robotics and Computer-Integrated Manufacturing 48, S. 39\u201350 (2017).<br \/>\n[39] Zheng, Y. u. a.: A Primitive-Based 3D Reconstruction Method for Remanufacturing. In: The International Journal of Advanced Manufacturing Technology 103 (4), S. 3667\u20133681 (2019).<br \/>\n[40] Khan, A. u. a.: Vision Guided Robotic Inspection for Parts in Manufacturing and Remanufacturing Industry. In: Journal of Remanufacturing 11 (6), S. 49-70 (2020).<br \/>\n[41] Schl\u00fcter, M. u. a.: Vision-Based Identification Service for Remanufacturing Sorting. In: Procedia Manufacturing 21, S. 384\u2013391 (2018).<br \/>\n[42] Siddiqi, M. U. R. u. a.: Low Cost Three-Dimensional Virtual Model Construction for Remanufacturing Industry. In: Journal of Remanufacturing 9 (2), S. 129\u2013139 (2019).<br \/>\n[43] Gibbons, Tom u. a.: A Gaussian Mixture Model for Automated Corrosion Detection in Remanufacturing. In: Advances in Transdisciplinary Engineering 8, S. 63\u201368 (2018).<br \/>\n[44] Nwankpa, C. u. a.: Achieving Remanufacturing Inspection Using Deep Learning. In: Journal of Remanufacturing 11 (2), S. 89-105 (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=\"94303\" data-userid =\"0\" data-filename=\"sprenger_Industrie40-im-Remanufacturing_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\/automation-en\/\">automation<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/industrie-4-0-en\/\">Industrie 4.0<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/industry-4-0-en\/\">Industry 4.0<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/internet-of-things-en\/\">Internet of things<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/literature-review-en\/\">literature review<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/remanufacturing-en\/\">remanufacturing<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Industry%204.0%20in%20Remanufacturing - https:\/\/industry-science.com\/en\/articles\/industry-4-0-in-remanufacturing\/\" 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\/industry-4-0-in-remanufacturing\/\" 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\/serious-games-as-a-training-tool\/\">\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\/04\/Lange_AdobeStock_734724963_alexkich-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/04\/Lange_AdobeStock_734724963_alexkich-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/04\/Lange_AdobeStock_734724963_alexkich-196x180.webp\" alt=\"Serious Games as a Training Tool\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Serious Games as a Training Tool\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Serious Games as a Training Tool<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Game mechanics design to promote resilience<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"\/authors\/annika-lange\/\">Annika Lange<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-4514-9306\" 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=\"\/authors\/thomas-knothe\/\">Thomas Knothe<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-3055-7155\" 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\/serious-games-as-a-training-tool\/\" 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>Unforeseen events are increasingly challenging manufacturing companies. Being resilient during crises is becoming a key competence. Serious games (SG) can help make resilience-building processes more transparent. This article derives specific requirements for SG from different phases of resilience and shows how these can be implemented in game mechanics in order to effectively support the training of resilience.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 98-104<\/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\/from-brownfield-to-industry-4-0\/\">\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\/04\/voelker-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/04\/voelker-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/04\/voelker-196x180.jpg\" alt=\"From Brownfield to Industry 4.0\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"From Brownfield to Industry 4.0\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">From Brownfield to Industry 4.0<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Learning factories as training and testing environment for digital transformation<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"\/authors\/jakob-weber\/\">Jakob Weber<\/a>, <a href=\"\/authors\/sven-voelker\/\">Sven V\u00f6lker<\/a> <a href=\"https:\/\/orcid.org\/0009-0000-9707-1478\" 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\/from-brownfield-to-industry-4-0\/\" 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>To succeed in their digital transformation, manufacturing companies need engineers with in-depth knowledge of key technologies and concepts, and a profound understanding of the transition from Industry 3.0 to Industry 4.0. This article describes the concept of a learning factory that is continuously subjected to a digital transformation, thereby creating an environment for the development of transformation competencies. The concept of digital transformation is based on digital worker assistance systems and multi-agent systems for production control. These enable the incremental integration of existing resources into the digitalized factory. The learning factory is not presented to students as a completed solution. Instead, it is continuously developed further as part of student projects. This way, it contributes directly to the qualification of personnel for the implementation of Industry 4.0.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 88-96<\/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-colleagues\/\">\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\/04\/Franken_titel-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/04\/Franken_titel-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/04\/Franken_titel-196x180.jpg\" alt=\"AI Colleagues?\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI Colleagues?\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI Colleagues?<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Competence requirements and training for AI use in industry<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"\/authors\/swetlana-franken-en\/\">Swetlana Franken<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-9991-3015\" 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-colleagues\/\" 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>Artificial intelligence is fundamentally changing tasks, roles, and skills in (industrial) companies. Increasingly, it acts as a colleague, preparing decisions, supporting processes, and interacting with people. This article highlights key competence requirements for AI use in industry, presents an integrated competence model, and outlines practical strategies for the transfer of skills. The aim is to prepare companies and employees for humane, competence-oriented AI implementation that combines technological efficiency with human creativity and judgment.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 78-86<\/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\/tachaid-ethical-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\/02\/Rath_AdobeStock_629687249_everythingpossible-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Rath_AdobeStock_629687249_everythingpossible-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Rath_AdobeStock_629687249_everythingpossible-196x180.jpg\" alt=\"Operationalizing Ethical AI with tachAId\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Operationalizing Ethical AI with tachAId\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Operationalizing Ethical AI with tachAId<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Validating an interactive advisory tool in two manufacturing use cases<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"\/authors\/pavlos-rath-manakidis\/\">Pavlos Rath-Manakidis<\/a>, <a href=\"\/authors\/henry-huick\/\">Henry Huick<\/a>, <a href=\"\/authors\/bjoern-kraemer\/\">Bj\u00f6rn Kr\u00e4mer<\/a> <a href=\"https:\/\/orcid.org\/0009-0004-4659-012X\" 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=\"\/authors\/laurenz-wiskott\/\">Laurenz Wiskott<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-6237-740X\" 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                     Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of\u2014from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool\u2019s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ...                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 50-59 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.1.48\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.1.48<\/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-industrial-quality-control\/\">\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\/01\/Uenal_AdobeStock_1653851064_Stock-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/01\/Uenal_AdobeStock_1653851064_Stock-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/01\/Uenal_AdobeStock_1653851064_Stock-196x180.webp\" alt=\"AI Implementation in Industrial Quality Control\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI Implementation in Industrial Quality Control\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI Implementation in Industrial Quality Control<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">A design science approach bridging technical and human factors<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"\/authors\/erdi-unal\/\">Erdi \u00dcnal<\/a> <a href=\"https:\/\/orcid.org\/0009-0007-2809-030X\" 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=\"\/authors\/kathrin-nauth\/\">Kathrin Nauth<\/a> <a href=\"https:\/\/orcid.org\/0009-0007-3457-102X\" 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=\"\/authors\/pavlos-rath-manakidis\/\">Pavlos Rath-Manakidis<\/a>, <a href=\"\/authors\/jens-poeppelbuss\/\">Jens P\u00f6ppelbu\u00df<\/a> <a href=\"https:\/\/orcid.org\/0000-0003-4960-7818\" 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=\"\/authors\/felix-hoenig\/\">Felix Hoenig<\/a>, <a href=\"\/authors\/christian-meske\/\">Christian Meske<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-5637-9433\" 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                     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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.1.112\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.1.112<\/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\/guidelines-fair-use-generative-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\/01\/Gerlmaier_AdobeStock_1847585259_master1305-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/01\/Gerlmaier_AdobeStock_1847585259_master1305-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/01\/Gerlmaier_AdobeStock_1847585259_master1305-196x180.jpg\" alt=\"Guidelines for the Fair Use of Generative AI\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Guidelines for the Fair Use of Generative AI\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Guidelines for the Fair Use of Generative AI<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Practical examples from production management and social welfare<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"\/authors\/anja-gerlmaier\/\">Anja Gerlmaier<\/a>, <a href=\"\/authors\/paul-fiete-kramer\/\">Paul-Fiete Kramer<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-9602-4952\" 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=\"\/authors\/dirk-marrenbach\/\">Dirk Marrenbach<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-6890-6966\" 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=\"\/authors\/rene-wenzel\/\">Ren\u00e9 Wenzel<\/a> <a href=\"https:\/\/orcid.org\/0009-0002-8315-2407\" 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                     With the rapid spread of assistive AI tools such as ChatGPT, Gemini, and Copilot, companies are being challenged to address the opportunities and challenges of artificial intelligence. Based on two practical examples, this article provides insight into how companies can use company-specific risk and potential analyses to develop guidelines for the fair and responsible use of AI.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 22-28 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.1.22\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.1.22<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n<\/div>\n<!-- GITO_PUB_POST end flex-container -->\n","protected":false},"excerpt":{"rendered":"<p>Remanufacturing, previously characterized by manual and cost-intensive processes, is a critical step on the way to a resource-efficient circular economy. Industry and research agree that the introduction of Industry 4.0 technologies is the key to the development of automated and economical remanufacturing systems. Based on a systematic literature review, this paper is dedicated to the analysis of promising Industry 4.0 approaches with a focus on the overall process as well as the sub-processes of disassembly and inspection. The results suggest that there is a need for additional knowledge, experience and research in the development and real demonstration of the approaches and their transferability to broader application fields.<\/p>\n","protected":false},"featured_media":96077,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80287,79627,80127,80264,80368,80369],"product_cat":[],"topic":[68005,68206,69611],"technology":[],"knowhow":[],"industry":[],"writer":[80570,83377,83379,83376,83378,82384],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":{"0":"post-94303","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"category-design-en","7":"category-translate-en","8":"category-typeset","9":"tag-automation-en","10":"tag-industrie-4-0-en","11":"tag-industry-4-0-en","12":"tag-internet-of-things-en","13":"tag-literature-review-en","14":"tag-remanufacturing-en","15":"topic-automation","16":"topic-industry-4-0","17":"topic-internet-of-things-en","18":"writer-gisela-lanza-en","19":"writer-jan-felix-klein-en","20":"writer-kai-furmans-en","21":"writer-kim-sprenger-en","22":"writer-marco-wurster-en","23":"writer-nicole-stricker-en","24":"product","25":"first","26":"instock","27":"downloadable","28":"virtual","29":"sold-individually","30":"taxable","31":"purchasable","32":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_IM-2021_4-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/sprenger_Industrie40-im-Remanufacturing_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":"Remanufacturing, previously characterized by manual and cost-intensive processes, is a critical step on the way to a resource-efficient circular economy. Industry and research agree that the introduction of Industry 4.0 technologies is the key to the development of automated and economical remanufacturing systems. Based on a systematic literature review, this paper is dedicated to the&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94303","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\/96077"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94303"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94303"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94303"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94303"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94303"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94303"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94303"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94303"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94303"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94303"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94303"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94303"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}