Manufacturing Systems

Work-Integrated Learning in Industry 4.0

Work-Integrated Learning in Industry 4.0

A qualitative analysis of various assistance systems in assembly
Kathleen Warnhoff ORCID Icon
In the era of Industry 4.0, many industrial companies are facing major transformations. In the process of digitalization, factory management is adopting new technologies such as cognitive assistance systems, which has led to changes in work processes. Regarding assembly in the metal and electrical industries, it is unclear to what extent this development has promoted work-integrated learning. Therefore, the topic of this paper is a qualitative analysis that explores employees' perceptions of the learning opportunities and risks presented by cognitive assistance systems. Results: Not all assembly employees benefit equally from these new developments.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 20-29 | DOI 10.30844/I4SE.25.2.20
Assembly in Transition

Assembly in Transition

Empirical results of digitalization
Mathias König ORCID Icon, Herwig Winkler ORCID Icon
Assembly is an important part of industrial production and is also characterized by a high proportion of manual work. Manufacturing companies have an intrinsic interest in increasing personnel productivity and preventing unit labor costs from rising. Many thus hope to gain economic benefits by implementing digitalization projects. The potential of digitalization in assembly must be exploited to achieve these goals.
Industry 4.0 Science | Volume 41 | 2025 | Edition 1 | Pages 42-49
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
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
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
Parameter Optimization for a Brine Injector

Parameter Optimization for a Brine Injector

Development of an AI pipeline using an example from the meat industry
Tim Zeiser ORCID Icon, Theo Lutz ORCID Icon, Corinna Köters ORCID Icon, Maik Schürmeyer, Alexander Prange ORCID Icon
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.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 40-46
Intelligent Shopfloor Assistants

Intelligent Shopfloor Assistants

Increasing productivity through the use of generative AI
Eckart Uhlmann ORCID Icon, Julian Polte ORCID Icon, Christopher Mühlich ORCID Icon, Yassin Elsir
In modern production companies, a heterogeneous IT landscape often complicates day-to-day work. A promising antidote is the use of intelligent agents, which use generative AI for routine tasks and can therefore increase efficiency. Whether these intelligent systems can be successfully integrated into existing networks determines whether the flow of information can be improved and manual effort reduced.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 64-71
Double Transformation in Mechanical and Plant Engineering

Double Transformation in Mechanical and Plant Engineering

Digitalization and sustainability for one-of-a-kind and small-batch manufacturers
Christoph Laroque ORCID Icon, Deike Gliem ORCID Icon, Sigrid Wenzel ORCID Icon
A decisive competitive factor for smaller and medium-sized manufacturers of one-of-a-kind and small batches is their products’ timely completion, delivery and commissioning. Precise logistics planning is just as important as production control. However, the processes are often characterized by uncertainties, e.g. due to local conditions at the customer or cooperation with suppliers. Digital shadows for data evaluation in real time offer a convincing solution.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 10-17 | DOI 10.30844/I4SE.24.5.10
From Lean Production to the Sustainable Production System of the Future

From Lean Production to the Sustainable Production System of the Future

An innovation factory as a multi-stage learning factory
Markus Schneider, Christoph Müller
The typical problems of a medium-sized company, coupled with the new requirements for sustainability, harbor the potential for economic tension. Learning factories can counteract this: they simulate production processes and offer an environment where participants can develop knowledge and skills in a realistic production setting. Establishing an innovation factory not only increases productivity, but also significantly reduces land consumption.
Industry 4.0 Science | Volume 40 | 2024 | Edition 4 | Pages 78-84
Spare Part Production of Vehicle Gearbox Bearings

Spare Part Production of Vehicle Gearbox Bearings

A method using additive manufacturing
Norbert Babel, Tobias Empl, Raimund Kreis ORCID Icon, Peter Roider
Spare parts for older products are often difficult to obtain or cannot be produced in an economically viable way using conventional manufacturing techniques. This article examines whether damping elements for gearbox bearings (in/for the automotive sector) can be manufactured from thermoplastic polyurethanes (TPU) with the same or compatible properties as the original part alternatively using additive manufacturing.
Industry 4.0 Science | Volume 40 | 2024 | Edition 2 | Pages 16-22
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