{"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":"

<\/p>\n

<\/div>\n

<\/p>\n

\n
\n

Access limited<\/center><\/h2>\n

You are currently not logged in \/ not yet registered<\/strong>.<\/p>\n

In order to download the desired file(s), you must be logged in and have an appropriate inclusive subscription. Alternatively, you can also obtain access by paying a one-off fee.<\/p>\n

\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

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
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Adaptive\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Digital Twins for Production<\/h4>\n
RAPIDZ \u2014 Resource analysis and process integration through digital twins<\/div>
Christian Salzig<\/a> \n \"ORCID<\/a>, Julia Burr<\/a> \n \"ORCID<\/a>, Sophie Hertzog<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In today\u2019s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 6-12 | DOI 10.30844\/I4SE.25.3.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Data\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Data Quality in the Engineering of Circular Products<\/h4>\n
Decision support for circular value creation through data ecosystems<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Sven Rarbach<\/a>, Jens Pottebaum<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Decisions affecting the sustainability of products are made during the engineering process. As product engineering progresses, statements on sustainability can also be substantiated. Initially, only estimates based on related products and processes are possible, but later, operational and machine data can be used. When metrics are used for key figures, the traceability of the data should be ensured. For this purpose, relevant data quality criteria and indicators are selected and analyzed for correlations. Data availability can be increased by relying on partners within data ecosystems for product engineering. Data spaces such as Gaia-X, Catena-X and Manufacturing-X form a basis for this ambition. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 12-19 | DOI 10.30844\/I4SE.25.2.12<\/a><\/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":[],"product_cat":[79304],"topic":[79489],"technology":[71297],"knowhow":[],"industry":[],"writer":[83595,83594,80795,83593],"content-type":[],"potential":[71227],"solution":[],"glossary":[],"class_list":["post-94767","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","product_cat-articles","topic-quality","technology-machine-learning","writer-hannes-gelbhardt-en","writer-hoang-viet-hai-luong-en","writer-klaus-dieter-thoben-en","writer-maria-teresa-alvela-nieto-en","potential-energy-efficiency","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\/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}]}}