Industry 4.0

Flexible, efficient, and above all reliable and safe: That’s what deployable person recognition in industrial production should look like. In the PROTECT project, Fraunhofer IKS and Fraunhofer IGCV have taken a big leap toward delivering these capabilities.

Industrial production is undergoing profound change. With rapidly advancing technologies and a changing social environment, industrial automation needs to be rethought in order to respond more flexibly to new requirements and challenges—keyword: adaptive production.

The ability of a production system to adapt autonomously to changing conditions promises efficiency gains and cost benefits. This also applies to subsequent change orders in the production process - a field of application for the new tool set from Fraunhofer IKS, which enables flexible and resilient production.

Lena Heidemann has been part of the Fraunhofer IKS since November 2020. She initially joined the institute as a student assistant. Since November 2021, she has been a research engineer in the Industrial Sensing Systems department. Her curiosity, openness and willingness to simply try out new things led her to research.

Seeing and being seen - this not only counts in the social arena, but also in the efficient and reliable cooperation between humans and robots in industrial production. Fraunhofer IKS will be demonstrating what this is all about in particular at Automatica in Munich at the end of June under the motto: “Simplicity is key”.

Can AI revolutionize production control? This is the question the research project MBO-KISS (Methods for Evaluating and Optimizing AI-generated Control Applications Based on the Physical Simulation of Machines and Their Desired Behavior) aims to address. The project has started at the beginning of the year, with a total duration of three years. The goal is to investigate the possible usage of Large Language Models (LLMs) for generating and securely applying control applications in industrial production.

The previous three parts of our series focused on the technologies “under the hood” of DEEP, the Fraunhofer IKS machine learning toolchain. Now, we take a look at the “big picture” in form of the process steps of the DEEP procedure – how DEEP can be used to get a grip on the problems associated with the use of machine learning (ML) for future flexible quality inspection.

DEEP is a machine learning toolchain by Fraunhofer IKS for the reliable AI-assisted automation of quality inspection systems. For this purpose, DEEP automates various specific Fraunhofer IKS technologies. This part of the series of blog posts focuses on the specific technological contents of modular concept learning.

What are the key ideas shaping the current automation landscape? Read on to learn about three insights that emerged from discussions between participants at Fraunhofer IKS’ Safe Intelligence Day 2025.

DEEP, the machine learning toolchain of Fraunhofer IKS, enables the automation of quality control in production environments, even if the requirements are complex. The technologies FAST and Robuscope, likewise developed by Fraunhofer IKS, are essential components of this solution.

Quality assurance is one of the, if not the most crucial competitive factors for industrial companies. German companies have long since recognized this and generally do not have to contend with quality issues. But that comes at a cost. Artificial intelligence is now believed to become a game changer in quality assurance. Dr. Niels Syassen, Executive Board member with responsibility for SICK AG’s Technology & Digitalization portfolio, explains how this will work.

Machine learning (ML) is considered a promising technology for the automation of quality control in production environments, even if the requirements are complex. However, ML approaches require sufficiently large datasets for training of the system, which are often unavailable. The ML toolchain DEEP (Date Efficient Evaluation Platform) from Fraunhofer IKS addresses this challenge.

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.