{"id":94767,"date":"2023-04-15T12:00:00","date_gmt":"2023-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=94767"},"modified":"2024-09-26T17:36:03","modified_gmt":"2024-09-26T15:36:03","slug":"product-quality-energy-production","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/product-quality-energy-production\/","title":{"rendered":"The Role of Product Quality in Energy-Efficient Production Processes"},"content":{"rendered":"

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n−<\/td>\n26,10 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Data Quality and Domain Expertise for Resilient AI Deployment<\/h4>\n
Integrating anomaly and label error detection in industry<\/div>
Pavlos Rath-Manakidis<\/a>, Henry Huick<\/a>, Erdi \u00dcnal<\/a>, Bj\u00f6rn Kr\u00e4mer<\/a> \n \"ORCID<\/a>, Laurenz Wiskott<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI implementation transforms work and worker-technology relationships in industrial quality control. This paper explores how approaches to data quality and model transparency support ethical AI deployment, fostering worker agency, trust, and sustainable work design in automatic surface inspection systems (ASIS). Recurring problems like data inefficiency, variable model confidence, and limited AI expertise point to key challenges of human-centered AI: user trust, agency and responsible data management. A solution co-developed with an ASIS supplier demonstrates that the challenges extend beyond the purely technical, underscoring the value of AI design that augments human capabilities. Technical solutions such as anomaly, label error, and domain drift detection are proposed to enhance data quality and model reliability. The insights emphasize the following generalizable strategies for resilient AI integration: understanding user-reported problems through a human-AI interaction lens, ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 128-135 | DOI 10.30844\/I4SE.26.1.120<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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AI Implementation in Industrial Quality Control<\/h4>\n
A design science approach bridging technical and human factors<\/div>
Erdi \u00dcnal<\/a> \n \"ORCID<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a>, Pavlos Rath-Manakidis<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, Felix Hoenig<\/a>, Christian Meske<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation\/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844\/I4SE.26.1.112<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Multi-Stakeholder\n <\/picture>\n <\/div>\n
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Multi-Stakeholder AI Ethics in Radiology<\/h4>\n
Implications for integrated technology and workplace design<\/div>
Valentin Langholf<\/a> \n \"ORCID<\/a>, Alexander Ranft<\/a>, Lena Will<\/a>, Robin Denz<\/a> \n \"ORCID<\/a>, Johannes Schwarz<\/a> \n \"ORCID<\/a>, Majd Syoufi<\/a>, Pavlos Rath-Manakidis<\/a>, Marc K\u00e4mmerer<\/a>, Marcus Kremers<\/a>, Axel Mosig<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a>, J\u00f6rg Wellmer<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI assistance can be seen as a welcome aid in radiology, a highly complex environment characterized by round-the-clock time pressure and quality expectations. However, it must meet high ethical standards from the perspective of both users and patients. It is a challenge to incorporate this human-centered approach into the development and introduction of AI applications. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 136-143 | DOI 10.30844\/I4SE.26.1.128<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Adaptive\n <\/picture>\n <\/div>\n
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Adaptive In-Orbit Servicing of Altered Satellite Components<\/h4>\n
Adaptive gripper placement on altered components for servicing in-orbit satellites<\/div>
Justus Rein<\/a> \n \"ORCID<\/a>, Christian Plesker<\/a> \n \"ORCID<\/a>, Adrian Reuther<\/a> \n \"ORCID<\/a>, Hanyu Liu<\/a> \n \"ORCID<\/a>, Benjamin Schleich<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In-orbit servicing of satellites presents several challenges as the satellite hardware is exposed to external influences throughout its life cycle. These factors wear down the components and cause changes to their physical structure. In such cases, the limits of simple dis- and reassembly steps may be reached, as the gripping surfaces are no longer present or suitable. This paper proposes an approach of an adaptive grip position estimation in a CubeSat disassembly process. The relevant components are identified using CAD models and a 3D camera. The gripping positions are determined based on the geometry of the gripper and the point cloud of the component. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 10-21 | DOI 10.30844\/I4SE.25.6.10<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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AI Smart Workstation for Industrial Quality Control<\/h4>\n
Enhancing productivity through vision systems, real-time assistance, and Axiomatic Design<\/div>
Leonardo Venturoso<\/a> \n \"ORCID<\/a>, Simone Garbin<\/a> \n \"ORCID<\/a>, Dieter Steiner<\/a> \n \"ORCID<\/a>, Dominik T. Matt<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Traditional quality control often falls short in high-mix, low-volume production environments due to variability and complexity. This project introduces an advanced workstation to boost industrial productivity and quality, developed with Axiomatic Design to ensure a clear link between customer needs, functional requirements, and design solutions. Combining polarization cameras, high-resolution imaging, adaptive lighting, and deep learning-based computer vision, the system performs high-accuracy inspection on quantity, quality, and compliance. A digital assistance system offers real-time feedback via an intuitive interface. Validation in a controlled environment confirmed both the system\u2019s practical benefits and its scalability. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 128-134 | DOI 10.30844\/I4SE.25.5.124<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Real-Time\n <\/picture>\n <\/div>\n
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Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO\u2082 footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Energy efficiency is becoming increasingly important in all sectors of the manufacturing industry. Companies are currently feeling the pressure of exorbitant energy prices very clearly, as well as the additional challenge of becoming CO2-neutral by 2045. With technologies from the field of machine learning (ML), innovative solutions can be developed that enable energy-efficient product manufacturing. In this way, ML-supported process control can make a decisive contribution to increasing the sustainability and competitiveness of a company. Decisive for ML-supported process control are the process- and raw material- dependent parameters, which are significantly responsible for the quality of the final product. The subject of this paper is a procedure for analyzing the complex relationships between the relevant influencing parameters for increasing energy efficiency in the manufacturing industry. (Only in German)<\/p>\n","protected":false},"featured_media":95677,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80024,79574,80239,80240],"product_cat":[3300],"topic":[79489],"technology":[71297],"knowhow":[],"industry":[],"writer":[83595,83594,80795,83593],"content-type":[],"potential":[71227],"solution":[],"glossary":[],"class_list":{"0":"post-94767","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-energy-efficiency-en","10":"tag-machine-learning-en","11":"tag-product-quality-en","12":"tag-production-processes-en","13":"product_cat-article","14":"topic-quality","15":"technology-machine-learning","16":"writer-hannes-gelbhardt-en","17":"writer-hoang-viet-hai-luong-en","18":"writer-klaus-dieter-thoben-en","19":"writer-maria-teresa-alvela-nieto-en","20":"potential-energy-efficiency","21":"product","22":"first","23":"instock","24":"downloadable","25":"virtual","26":"sold-individually","27":"taxable","28":"purchasable","29":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/IM-02-2023_Nieto-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":"Energy efficiency is becoming increasingly important in all sectors of the manufacturing industry. Companies are currently feeling the pressure of exorbitant energy prices very clearly, as well as the additional challenge of becoming CO2-neutral by 2045. With technologies from the field of machine learning (ML), innovative solutions can be developed that enable energy-efficient product manufacturing.…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94767","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\/95677"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94767"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94767"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94767"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94767"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94767"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94767"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94767"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94767"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94767"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94767"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94767"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}