Branche: Automotive

Electric Trucks in Intermodal Terminal Pre- and Post-Carriage

Electric Trucks in Intermodal Terminal Pre- and Post-Carriage

Impact on terminal processes in combined road-rail freight transport
Ralf Elbert, Samira Ghaneian Sebdani ORCID Icon
Electric trucks (e-trucks) play an important role in reducing CO₂ emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply.
Industry 4.0 Science | Volume 41 | Edition 6 | Pages 70-77
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 ORCID Icon, Martin Manns ORCID Icon
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
Data Quality in the Engineering of Circular Products

Data Quality in the Engineering of Circular Products

Decision support for circular value creation through data ecosystems
Iris Gräßler ORCID Icon, Sven Rarbach, Jens Pottebaum ORCID Icon
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.
Industry 4.0 Science | Volume 41 | 2025 | Edition 2 | Pages 12-19 | DOI 10.30844/I4SE.25.2.12
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
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
Transformation in the Automotive Industry

Transformation in the Automotive Industry

Overcoming employee-related challenges with effective leadership
Stefan Süß ORCID Icon, Ingo Klingenberg ORCID Icon, Maximilian Kellerer, Phillip Nguyen
Transformative forces present companies with enormous challenges. At the same time, new forms of collaboration and new roles and responsibilities are emerging. Due to the risks associated with change, many employees hold on to old habits and work processes, which can slow down positive developments. The challenge for managers is to recognize this resistance, prevent it and turn it into acceptance or even proactive support.
Industry 4.0 Science | Volume 40 | Edition 3 | Pages 21-26
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
Digital Platform Frameworks for Manufacturing Companies

Digital Platform Frameworks for Manufacturing Companies

A review
Marcel Rojahn ORCID Icon
In recent years, digital platforms have established themselves as a central concept in the IT field. Due to the wide variety of digital platforms available on the market, there is still a need for clear comparison with criteria to enable interested parties to select, change, operate and further develop these platforms. The following paper aims to contribute to the facilitation of this comparison by undertaking a systematic literature review of digital platform frameworks in the context of the Industrial Internet of Things (IIOT) for manufacturing companies and thus providing a basis for a number of potential ways to effectively compare current digital platforms and ecosystems.
Industry 4.0 Science | Volume 40 | 2024 | Edition 2 | Pages 8-15 | DOI 10.30844/I4SE.24.2.8
Lean Empowerment in the Digital Ecosystem

Lean Empowerment in the Digital Ecosystem

Translating cultural values into technical requirements
Frank Bertagnolli ORCID Icon, Sabrina Karch ORCID Icon, Arndt Lüder ORCID Icon
With the advent of digitalization, prevailing paradigms – such as product centricity, face-to-face collaboration and hierarchical structures – are giving way to the vision of data-driven business models, digital, collaborative ecosystems and an agile, holacratic way of working in flat hierarchies and self-managing teams. Collaboration is made possible through the use of software solutions. In addition to adapted management concepts, the digital space also requires a digital cultural understanding on part of the companies involved. Lean empowerment is a pioneering approach to collaboration based on cultural values. In expert workshops, ideas were developed to explore how these values can be lived in a digital culture and thus in terms of global digital collaboration. This article presents concrete solutions from which requirements for digital collaboration and for implementation within IT structures and software solutions in particular can be derived.
Industry 4.0 Science | Volume 40 | 2024 | Edition 2 | Pages 32-39 | DOI 10.30844/I4SE.24.2.32
Quantum Computing: A Brief History

Quantum Computing: A Brief History

With applications of quantum computing in automotive
David von Dollen, Daniel Weimer, Florian Neukart
In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?
Industrie 4.0 Management | Volume 37 | 2021 | Edition 4 | Pages 34-36
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