{"id":103428,"date":"2024-02-20T13:38:34","date_gmt":"2024-02-20T12:38:34","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103428"},"modified":"2025-02-10T17:38:21","modified_gmt":"2025-02-10T16:38:21","slug":"quality-management","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/quality-management\/","title":{"rendered":"Quality Management"},"content":{"rendered":"\n\n\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>
Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, Kathrin Nauth<\/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 \"Potentials,\n <\/picture>\n <\/div>\n
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Potentials, Premises, Perspectives<\/h4>\n
Using LLMs to reinterpret corporate knowledge management<\/div>
Vanessa Kuks<\/a> \n \"ORCID<\/a>, Pius Finkel<\/a> \n \"ORCID<\/a>, Peter Wurster<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Demographic change is exacerbating the shortage of labor and skilled workers in the manufacturing industry, making knowledge management an increasingly important issue in many companies. Collecting and preserving tacit knowledge poses a particular challenge. This study examines the extent to which large language models (LLMs) can provide meaningful support in knowledge gathering through expert interviews. Three experts test and evaluate a personalized chatbot that has been developed using ChatGPT-5. The results of the interview are promising, but the summary shows room for improvement. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 48-56 | DOI 10.30844\/I4SE.25.6.48<\/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 \"Data\n <\/picture>\n <\/div>\n
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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
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\n \n \n \n \"Error\n <\/picture>\n <\/div>\n
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Error Management in Production<\/h4>\n
Current situation and challenges in the industry<\/div>
Johannes Prior<\/a> \n \"ORCID<\/a>, Milan Brisse<\/a> \n \"ORCID<\/a>, Nikita Govorov<\/a>, Robert Egel<\/a> \n \"ORCID<\/a>, Bernd Kuhlenk\u00f6tter<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n This study explores experience-based error management on the basis of 23 participating companies. This study aims to identify essential criteria for effective error management in production. For this purpose, a comprehensive questionnaire was created, featuring 77 questions across eight key topics, including error culture, documentation, root cause analysis and software-supported knowledge management. The following analysis highlights both positive and negative measures, providing specific recommendations to optimize experience-based error management. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 38-45<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Improving\n <\/picture>\n <\/div>\n
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Improving Social Media Moderation with Generative Language Models<\/h4>\n
Study on the detection and correction of disinformation<\/div>
Anton Schegolev<\/a>, Maximilian Ambros<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Fake news are increasingly dominating the digital world. The question arises: Can modern technologies reverse this trend? The article highlights the potential of the GPT-4o language model for identifying fake news in online comments and news articles and for correcting false information. With impressive accuracy, the model shows how language technology can combat misinformation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 72-79 | DOI 10.30844\/I4SE.24.6.72<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Setting\n <\/picture>\n <\/div>\n
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Setting Up Assembly Assistance Systems<\/h4>\n
System for the efficient configuration of assembly instructions and assistance functions<\/div>
Dennis Keiser<\/a>, Dario Niermann<\/a> \n \"ORCID<\/a>, Michael Freitag<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>In industrial assembly, humans are working more closely with machines due to assembly assistance. However, despite their great potential, the implementation of digital systems is time-consuming, which entails high training requirements. Small and medium-sized businesses, in particular, are reaching their limits. A newly developed setup system is designed to facilitate the introduction and use of such assembly assistance systems and increase their acceptance. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 32-39<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Parameter\n <\/picture>\n <\/div>\n
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Parameter Optimization for a Brine Injector<\/h4>\n
Development of an AI pipeline using an example from the meat industry<\/div>
Tim Zeiser<\/a> \n \"ORCID<\/a>, Theo Lutz<\/a> \n \"ORCID<\/a>, Corinna K\u00f6ters<\/a> \n \"ORCID<\/a>, Maik Sch\u00fcrmeyer<\/a>, Alexander Prange<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The production of cooked ham involves a number of challenges. In production, cuts of meat are put through in a multi-stage curing and cooking process involving brine. This can lead to fluctuations in quality due to structural defects in the meat. The result: the brine is not optimally absorbed. An AI model trained on historical data intends to solve the problem. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 40-46<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Bakery\n <\/picture>\n <\/div>\n
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Bakery 4.0<\/h4>\n
Development of an IoT framework for the automatic collection of machine, process data in medium-sized bakery operations<\/div>
Abderrahim Ait Alla<\/a> \n \"ORCID<\/a>, Frida K\u00f6ning<\/a>, Heiner Alsen<\/a>, Joshua Coordes<\/a>, Michael Freitag<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>While the digital world has already made its way into many other production areas, the bakery industry has so far benefited little from these technological developments. This is because many baking processes are manually controlled and rely on expert knowledge. In addition, the process data from the machines is still not automatically captured via sensors. This paper describes a procedure for digitizing baking processes by developing an IoT framework consisting of an IoT device including measurement methods, an edge gateway, and a simulation-based solution for process optimization. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 39 | 2023 | Edition 3 | Pages 36-40<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Interactive\n <\/picture>\n <\/div>\n
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Interactive 8D as Application for Sustainable Problem Solving<\/h4>\n
A Knowledge-based IT Assistance for Structured 8D Problem Solving in the Automotive Industry<\/div>
Martin Kempel<\/a>, Ralph Richter<\/a>, Jochen Deuse<\/a> \n \"ORCID<\/a>, Lukas Schulte<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>n the automotive industry, preventive quality actions are applied to ensure the quality of the end products. During production ramp-up the occurrence of nonconformities can be a critical issue. Nonconformities with new and innovative products can be especially challenging due to limited experience of previously unknown processes. To address this challenge, an IT application has been developed to capture the organization's existing knowledge and use this to support the problem- solving team in applying an enhanced 8D method. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 38 | 2022 | Edition 5 | Pages 35-39<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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