Production Planning

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Boosting Competitiveness in Small Batch Production

Boosting Competitiveness in Small Batch Production

Scalable and flexible body-in-white production line with collaborative mobile robots

Walid Elleuch, Tadele Belay Tuli

Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the pillars of a resilient production system. This article presents a conceptual solution for flexible Body-in-White sheet metal production with autonomous collaborative robotic systems to make product costs affordable for a higher competitive advantage.

Industry 4.0 Science | Volume 41 | Edition 2 | Pages 60-67

Digital Twins Using Semantic Modeling and AI

Digital Twins Using Semantic Modeling and AI

Self-learning development and simulation of industrial production facilities

Wolfram Höpken

, Bernd Michelberger, Markus Till, Timo Schuchter, Alexander Lohr

The AI-driven, self-learning digital twin continuously adapts to real system behavior, ensuring an optimal representation of the production process. A comprehensive semantic model serves as the foundation for advanced artificial intelligence (AI) approaches. Insights derived from AI methods are integrated into this model, enhancing the interpretability and explainability of AI systems. Techniques from the field of eXplainable AI (XAI) facilitate the automated description of AI models and their findings, as well as the development of self-explanatory models.

Industry 4.0 Science | Volume 41 | Edition 2 | Pages 30-36

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

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

Computer Use in Industrial Business Processes

Computer Use in Industrial Business Processes

A systematic literature review of the last 40 years

The most important value-adding industrial business processes are product creation and order processing. For 40 years now, Industry 4.0 Science and its predecessor titles have supported the development and implementation of the software used in these industrial business processes. In honor of the journal’s 40th anniversary, a systematic literature review is carried out to identify trends that played a role in the past but no longer do today. The research provides some surprising insights into the topics that have been important in the last 40 years – as well as those that will remain relevant in the future.

Industry 4.0 Science | Volume 41 | 2025 | Edition 1 | Pages 8-14 | DOI 10.30844/I4SE.25.1.8

Introduction of Machine Learning in Production

Introduction of Machine Learning in Production

An SME-specific, holistic guide

Manuel Savadogo, Malte Stonis

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

, Michael Freitag

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

, Peter Wurster

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

Intelligent Shopfloor Assistants

Intelligent Shopfloor Assistants

Increasing productivity through the use of generative AI

, Christopher Mühlich

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

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

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

AI-Assisted Work Planning

AI-Assisted Work Planning

Extracting expert knowledge from historical data for streamlined efficiency and error mitigation

, Mathias Keil, Nils Killich, Ralph Hensel-Unger

Global competitive pressure is forcing companies to use resources efficiently, especially in high-wage countries. This is further intensified by market and legislative pressure for sustainable products and processes. In the face of digital and ecological change, holistic approaches to optimizing manual work processes are essential. An AI-supported assistance system for work plan creation is intended to remedy this and thus enable more efficient process design.

Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 74-80 | DOI 10.30844/I4SE.24.5.74

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