Simulation

In the manufacturing industry, the integration of digital human models into the product development and manufacturing process is becoming increasingly important. Particularly in assembly, which is characterized by a high proportion of manual tasks, motion simulations enable a realistic representation of human work and thus make a significant contribution to the evaluation of motion economy, process validation, and efficiency improvement. However, widespread application in production planning faces various challenges, such as the high initial effort required to create human simulations as well as volatile planning conditions. This article presents a practice-oriented solution from the automotive assembly sector that enables the creation of simulations with reduced effort as well as their early and consistent use in the planning process.

In recent years, learning factories have evolved into key experimental environments in the context of the Fourth Industrial Revolution. In addition to their role as training centers for skilled workers, they also serve as real-world research laboratories. This issue of Industry 4.0 Science examines learning factories as venues for exploring new approaches and technologies—whether digital assistants, cobots, serious games, or digital twins.

Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case.

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.

The supply of production processes and buildings with thermal energy represents a significant share of the total energy demand of an industrial site. The use of industrial waste heat offers a way to reduce the external purchase of final energy. Due to the lack of transparency and the complexity of such measures, their potential often remains untapped. In the research project ETA im Bestand a user-oriented software solution was prototypically implemented. The software solution enables the development and evaluation of industrial energy concepts. Approaches from the research area of operations research and dynamic simulation are applied.

Professor Bernd Scholz-Reiter studied industrial engineering at the Technical University of Berlin. After several positions in Germany and abroad, he accepted the call to the University of Bremen in 2000, where he initially held the professorship of Planning and Control of Production Systems in the Department of Production Engineering. From 2002 to 2012, he also headed the Bremen Institute for Production and Logistics (BIBA). From 2012 to 2022, Bernd Scholz-Reiter was rector of the University of Bremen.