assembly

I4S 5/2025: Artificial Intelligence and Digital Assistance

I4S 5/2025: Artificial Intelligence and Digital Assistance

How we can better support work
Demographic change, skills shortages, and stagnating productivity are threatening the competitiveness of German industry. At the same time, AI and digital assistance systems are opening up new opportunities: they make work more efficient and support skilled workers. But while they have long been part of everyday life, their potential in industry remains largely untapped—this is where this issue comes in with innovative concepts.
Empathic Assembly Assistance

Empathic Assembly Assistance

Combining AI-based data analysis and empathic human digital twins
Matthias Lück ORCID Icon, Katharina Hölzle ORCID Icon, Christian Saba-Gayoso, Joachim Lentes
Industrial companies in Germany face demographic change and stagnating productivity in an increasingly complex world. Manual assembly remains essential for complex, low-volume products, yet productivity and quality lag due to human variability. This paper introduces a concept and demonstrator for an empathic assembly assistance system that merges a human digital twin and AI-based screwdriver data analytics within a modular architecture. Tightening anomalies are classified, linked to inferred worker states and translated into information and recommendations.
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 6-13 | DOI 10.30844/I4SE.25.5.6
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
Robot-Based Assembly Automation in Mid-Sized Companies

Robot-Based Assembly Automation in Mid-Sized Companies

Obstacles, drivers and implications
Aaron Zinßer, Fabian Diefenbach ORCID Icon, Arik Lämmle ORCID Icon
Production automation is well established in large companies for high volume products. But robot-based assembly automation in mid-sized companies is still in its infancy. This study uses results from 19 expert interviews and a survey to identify obstacles to and drivers of automation in this field. Among the obstacles is the low flexibility of the robotic systems. One driver for automation is the increasing shortage of skilled workers. Based on the empirical findings, the study proposes options to increase the use of automation.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 4 | Pages 21-24 | DOI 10.30844/IM_23-4_21-24
Concept for a Modular, Reconfigurable Assembly System

Concept for a Modular, Reconfigurable Assembly System

Increased flexibility through reconfiguration at various production levels
Jasper Wilhelm, Nils Hoppe, Michael Freitag ORCID Icon
Companies must increase their flexibility and enable high product customization and variety to meet market demands. In assembly, this requires a large number of special machines, which leads to high investments and space requirements. This paper presents a concept for a modular, reconfigurable assembly system that allows unrestricted connection of individual modules. It is shown how such a system can be located in the RAMI4.0 framework and fulfills changeability requirements on different production levels. (Only in German)
Industrie 4.0 Management | Volume 38 | 2022 | Edition 4 | Pages 33-37
A Self-Learning Assistance System for Industrial Robots

A Self-Learning Assistance System for Industrial Robots

Gestenbasierte Programmierung von skillbasierten Robotersystemen in der Montage
Ulrich Berger, Marlon Lehmann, Ronny Porsch
In the project ARAS (Advanced Robot Assistance Solution) a robot programming assistant was developed, which allows for automated generation of robot programs for assembly processes. By using a multimodal approach for human-machine-interaction, assembly steps are recognized with machine learning algorithms while a worker is showing the robot how an assembly process is performed. Afterwards, a robot program is generated automatically. This way, new robot programs are created within minutes without the user having any knowledge about programming or robotics.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 6 | Pages 23-26
Human-Centered Assistance Systems

Human-Centered Assistance Systems

Systematic evaluation of assembly assistance systems
Dennis Keiser, Christoph Petzoldt, Thies Beinke, Michael Freitag ORCID Icon, Henning Vogler
The employee remains a key productivity element in industrial assembly. Assembly assistance systems have therefore become an integral part of employee support. This paper presents a novel assistance system that complements process-related assistance with human-centered functionalities. In addition, an approach for the systematic evaluation of assembly assistance systems is presented in this paper. The research is based on an evaluation of the current state of the art through systematic market analysis of available assembly assistance systems.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 3 | Pages 11-15
Digital Solutions for the Control of Dynamically interconnected Assembly Systems

Digital Solutions for the Control of Dynamically interconnected Assembly Systems

Realisierung von flexiblen Routen im Kontext Industrie 4.0
Jonas Rachner, Simon Hort, Robert Schmitt ORCID Icon
Due to an increased product variety, the need for flexibly configured assembly systems is steadily growing. In contrast to classic assembly lines with predominantly rigid conveyor technology, efficient, individual assembly routes with cycle-independent processing times are implemented in a dynamically interconnected assembly system using intelligent control and AGVs. This article presents the most important factors for the IT-related implementation of a dynamically interconnected assembly system and shows which existing standards from the field of Industry 4.0 can be used for this purpose.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 6 | Pages 43-47
Design of Collaborative HRC Workplaces

Design of Collaborative HRC Workplaces

Hinweise für die Planung von kollaborativen Arbeitsplätzen an einem Beispiel der Metabowerke GmbH
Wilhelm Bauer, Peter Rally, Oliver Scholtz, Marc Wenzelburger
In human-robot collaboration (HRC), in which the employee works next to the robot - as is often the case in the previously purely manual assembly - the cost effectiveness of HRC application is often difficult to represent. Therefore, in the design of HRC applications, the focus in the first planning phase is on ensuring economic efficiency. In the ROKOKO research project, the involved partners developed a simple method for estimating the required total investment. The planning of a HRC application case at the company Metabowerke GmbH using the new method is the subject of this article.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 47-51
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