Industrial Automation - Smart Factory to increase efficiency

In the course of industrial automation, the manufacturing industry is developing digital strategies to optimize communication between people, machines and systems. Automated production enables the smart factory to improve efficiency through its self-adaptive adaptability, for example, by processing smaller batch sizes.

Innovative concepts such as digital twin space, resilient software architectures, smart sensors and predictive maintenance are used as part of the digitalization of processes. This allows for more efficient maintenance and increased efficiency, which enables the smart factory to maintain operations even in the event of component failures or changing environmental conditions such as material quality, tool wear or unexpected logistics goods.

Great expectations are also placed on artificial intelligence to increase efficiency and ensure sustainable, resource-conserving production. In particular, the vision is for the automated smart factory to function in a resilient and sustainable manner. The implementation of this new, holistic digital strategy requires new approaches to flexible and self-organized automation for production systems.

In addition to social challenges such as the increasing shortage of skilled workers, industrial production is currently experiencing a paradigm shift - also driven by European Union measures to promote the digitalization of processes and digital strategies under the umbrella term "Industry 5.0": people are seen as an integral part of the system and the production chain and are being placed at the center of automation. This is because when experts are relieved of manual or repetitive tasks, expertise can be deployed where it delivers the greatest added value.

Although technology-driven change is often said to make people obsolete, the need for a close connection between man and machine is particularly evident in approaches to reliable, human-centered automation. This innovative human-machine interaction, such as with safe and adaptive cobots in an intelligent factory, requires reliable perception of objects and the environment and self-optimizing cyber-physical systems - while at the same time ensuring safe and robust integration of the individual components.

To this end, Fraunhofer IKS develops solutions in which system automation interactively adapts to individual people, taking into account their capabilities and measurable performance levels. Our aim is to offer efficient, reliable and safe solutions for the manufacturing industry of the future.

In reliable, human-oriented automation, artificial intelligence plays a decisive role in the digitalization of processes. In order to increase the level of autonomy in production processes and prevent risks for people, Fraunhofer IKS is working on AI-based solutions to reliably and resource-efficiently detect people and objects. This allows skilled workers to easily stay in control of autonomous production processes and, thanks to the flexibility this brings to their day-to-day work, apply their skills where they are most needed.

Advanced algorithms and machine learning can be used to recognize human behavior and patterns in the production process to implement and adapt automated, digital processes accordingly. This enables adaptive and reliable automation that integrates seamlessly into the workflows of the manufacturing industry, increasing efficiency and ensuring safety at the same time.

Our services include:

• Development of a system for reliable product quality testing that offers flexibility and adaptability for different production requirements.
• Integration of novel techniques, including machine learning, into production processes.
• Advice on the use of digital twins and SW architectures.
• Advice on the use of cognitive systems in the automation framework, including engineering, SW architectures, digital twins, ML use, new programming, cooperation and operating concepts.
• Support in the development of adaptive human-centered interaction concepts.
• Use of a low-code engineering system for reliable person and object recognition.
• Help with the development of human models for virtual validation and dynamic adaptation to human users.
• Consulting and support in the design of human-centered machines / robot behaviors, including subject studies.
• Advice on solutions with AI-based self-organization and optimization of automation.