Typeset

Hybrid Decision Support in Product Creation

Hybrid Decision Support in Product Creation

Improving performance with data science and artificial intelligence
Iris Gräßler ORCID Icon, Jens Pottebaum ORCID Icon, Peter Nyhuis ORCID Icon, Rainer Stark ORCID Icon, Klaus-Dieter Thoben ORCID Icon, Petra Wiederkehr ORCID Icon
Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.
Industry 4.0 Science | Volume 41 | Edition 1 | Pages 18-25 | DOI 10.30844/I4SE.25.1.18
Real-time Reactions for Automated Guided Vehicles (AGV)

Real-time Reactions for Automated Guided Vehicles (AGV)

Monitoring and controlling with long latencies
Dominik Augenstein, Lea Basler
The constant advance of digitalization confronts companies with new challenges and opportunities. Immediate data processing is now ubiquitous and the advantages are obvious. However, broadband coverage in Germany is insufficient, which makes it difficult to improve processes. Mathematical approaches and machine learning enable timely optimization and smooth production.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 56-62
Improving Social Media Moderation with Generative Language Models

Improving Social Media Moderation with Generative Language Models

Study on the detection and correction of disinformation
Anton Schegolev, Maximilian Ambros ORCID Icon
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.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 72-79 | DOI 10.30844/I4SE.24.6.72
Digitization of Raster Drawings with Deep Learning

Digitization of Raster Drawings with Deep Learning

Framework outperforms OCR software in extracting data from mechanical drawings
Xiao Zhao, Marko Weber, Jan Schöffmann, Daniela Oelke ORCID Icon
A new look into the depths of technical drawings: A deep learning framework reads CAD drawings more accurately than ever before, recognizing geometrical dimensioning and tolerancing, dimensions, and every other detail. What used to be tedious manual labor is now carried out by an AI that understands the special features of every line and label. This promising technology not only increases accuracy but also speeds up the processing of drawings considerably. The system thus opens up new avenues for precision in production.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 10-17
Introduction of Machine Learning in Production

Introduction of Machine Learning in Production

An SME-specific, holistic guide
Manuel Savadogo, Malte Stonis ORCID Icon, Peter Nyhuis ORCID Icon
Machine learning offers a wide range of potential, especially in production, and is therefore becoming increasingly important. However, small and medium-sized businesses are lacking guidelines that are specifically tailored to their individual challenges to guide them step-by-step through the process. In conjunction with a potential analysis, the determination of relevant prerequisites and a maturity assessment, this article can serve as a guide for SMEs.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 88-95
Can Artificial Intelligence (AI) Act as an Enabler for Industry 4.0 (4IR)?

Can Artificial Intelligence (AI) Act as an Enabler for Industry 4.0 (4IR)?

Impacts on the maturity level of Industry 4.0 technologies
Dennis Richter, Mildred Doe, Steffen Kinkel ORCID Icon
Artificial intelligence is often mentioned often mentioned in the same context as Industry 4.0, but the exact role of AI is unclear. Is AI just another 4IR technology or an essential "enabler" for other 4IR technologies? Six experts assess the impact of AI on 41 4IR technologies. AI could indeed be a decisive factor in unleashing the full potential of Industry 4.0.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 80-87 | DOI 10.30844/I4SE.24.6.80
Setting Up Assembly Assistance Systems

Setting Up Assembly Assistance Systems

System for the efficient configuration of assembly instructions and assistance functions
Dennis Keiser, Dario Niermann ORCID Icon, Michael Freitag ORCID Icon
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.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 32-39
Aiming to Create Green AI

Aiming to Create Green AI

Putting a focus on AI energy efficiency and minimizing the CO2 footprint of AI-based systems
Marcus Grum ORCID Icon, Maximilian Ambros ORCID Icon, Marcel Rojahn ORCID Icon
Reducing CO2 emissions is one of the most urgent tasks of our time. Simultaneously, artificial intelligence is developing rapidly. However, AI often brings about its own significant CO2 impact. Experimental testing of Green AI strategies is therefore crucial for their long-term success. A management tool can support this process so that both users and managers can make optimal use of AI as a tool.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 18-30 | DOI 10.30844/I4SE.24.6.18
Process Reference Model (PRM) for AI Development in Vehicles

Process Reference Model (PRM) for AI Development in Vehicles

Practical guide to the development of AI functionalities in the automotive industry
Sebastian Grundstein ORCID Icon, Bernhard Burger, Andreas Aichele ORCID Icon
Artificial intelligence is increasingly being integrated into vehicles, but conventional product development processes often do not fully capture the specific requirements of AI projects. In order to meet these requirements, a process reference model (PRM) has been developed specifically for the development of AI functionalities in the automotive industry. This model is intended to support companies in adapting their conventional software development processes more easily to the special features of AI projects.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 96-101
Large Language Models (LLM) in Production

Large Language Models (LLM) in Production

An analysis of the potential for transforming production processes in modern factories
Pius Finkel ORCID Icon, Peter Wurster ORCID Icon, Robin Radler
The rapid development of generative artificial intelligence is opening up new avenues for the manufacturing industry amid a shortage of skilled workers. Large language models can potentially make production processes in medium- sized businesses more efficient. But how exactly is this potential measured? Key areas of application such as communication, training and knowledge management show why a lot depends on employee acceptance.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 48-55 | DOI 10.30844/I4SE.24.6.48
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