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The enormous increase in system complexity is one of the greatest challenges facing Industry 4.0. Live analyses that run parallel to the machine runtimes to save time and money, as well as predictive maintenance to prevent outages, are part of the diverse methods and tools that the Fraunhofer Institute for Cognitive Systems IKS is developing.
For more information about the research of Fraunhofer IKS on Industry 4.0 topics, visit our website: Industry 4.0
Related to Industry 4.0
Industrial production is undergoing radical change. With constant technological advancement and societal change, industrial automation must also reinvent itself to meet changing requirements and challenges – with the keyword here being adaptive production.
To investigate the future of automation and possible transition paths towards it, Fraunhofer IKS is working on extending automation systems for novel applications. Read on to understand how these prototypes have taken shape and what insights they provide.
Rather than replacing workers, modern manufacturing approaches aim to combine human expertise and automation, leading to a production setup that increases quality without sacrificing efficiency. Read on to understand how human skills and automation can be combined on the shop floor.
As production technologies continue to evolve, it can feel like companies are permanently playing catch-up. Read on to understand the current key changes in industrial automation and why it is worthwhile to embrace them.
Artificial Intelligence (AI) in Industry 4.0 still faces technological obstacles that make the use of AI difficult. Fraunhofer IKS is therefore developing a framework that supports and optimizes the data and AI lifecycle - and thus significantly increases the added value of AI in production.
Leaving cooperative, heterogeneous robots alone in unstructured environments isn’t such a good idea. After all, only with an AI-powered management solution will they be able to solve complex tasks without causing any damage. The Fraunhofer IKS is working on developing such a system.
Achieving safety with AI systems requires a comprehensive and collaborative approach, including technical, ethical, and regulatory considerations. A central aspect is designing AI systems that comply with safety requirements defined in constraints.
Digital twins are designed with the goal to create precise replicas of our physical world, but how exactly are they being used? For example, in medicine, vehicle construction and industrial production? Read on to find out.
The EAI IC4S international conference has awarded the prize for the best paper to Fraunhofer IKS. The topic of the paper is a concept for safe and efficient collaboration between autonomous mobile robots (AMR) and people in shared areas.
In the field of modern warehouse logistics, speed is crucial to success. This is where autonomous mobile robots (AMR) come in. In order to perform their tasks, they must respond as flexibly as possible to new environmental conditions and warehouse goods. ResilientSOA, the framework developed by Fraunhofer IKS, provides the necessary prerequisites. In this interview, project manager Florian Wörter explains exactly how it works.
Cyber-physical systems (CPS) integrate software algorithms and physical structures. To exceed the simple embedded systems of today, advances in CPS require several properties, particularly adaptability, resilience, and safety. Lean management of CPS with resilient architectures satisfies these requirements by combining data distribution with service-oriented structures. A practical use case: a moving robot in a warehouse.
