Automation Systems

In manufacturing, automation is crucial to ensure the quality and efficiency of production processes. Our vision is for automation techniques to complement human expertise, leveraging the strengths from both perspectives to achieve the best possible outcome. To achieve this, we focus on the following two areas in the context of automation systems.

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We adapt production systems so that they can respond quickly and efficiently to dynamic requirements. This includes the implementation of adaptive automation solutions that enable retooling and adaptation to make the production process more flexible and resilient. At its core, flexilient automation works by applying a feedback loop that continuously monitors and analyzes the current state of the production line. Then, if necessary, the system’s behavior is modified to ensure that its performance remains as good as possible.

We recommend combining strategies at different levels to successfully establish a flexilient system. From an architectural perspective, a design that encourages modularity and a high degree of self-organization is ideal, as it provides a good foundation for recombining the system functionality into new behaviors. For modelling the physical production line, it is advantageous to build digital twins that mirror the current state of machinery, as this improves the availability of monitoring data. Additionally, Artificial Intelligence (AI) techniques can be applied to assist with the modification of the system’s behavior, including automated code generation and other engineering aids.

We ensure that automation systems in the manufacturing industry interact closely with the users’ needs and capabilities to increase efficiency. Advanced AI algorithms and machine learning enable the automated adaptation of processes according to individual user preferences and behaviors. We offer a variety of services to implement these solutions, including consulting on the use of digital twins, support for integrating AI into the smart factory and the development of innovative concepts for digitizing processes at various levels of production.

To accurately respond during human-machine interactions, we recommend the creation of human models to capture the behavior of workers in the production line. This is advantageous because it allows the system to contextually adapt its responses to people, ensuring their safety and supporting their productivity. Likewise, the behavior of robots that interact with workers should be thoroughly mapped to determine the best response for each situation.