New Research Project “X|CausePro” started

One of the biggest challenges in modern electronics production is the increasing complexity of manufacturing processes, driven in particular by ever smaller dimensions and increasing functional integration. Numerous influencing parameters and their interactions determine product quality and are becoming increasingly difficult to control using conventional methods. Traditional machine learning approaches based purely on pattern recognition and correlation are reaching their limits when it comes to identifying actual cause-and-effect relationships. Causal Machine Learning (Causal ML), on the other hand, represents a holistic approach that makes it possible to precisely quantify and specifically influence causal relationships.

The joint project X|CausePro aims to reduce the entry barriers for causal analyses in production by combining large language models (LLMs), causal machine learning and an event-based data infrastructure, while at the same time increasing production efficiency and quality.

Further information can be found on the FAPS website.

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