1. AutoML in the Factory: How to Empower Engineers to Adopt AI by Weidmüller (Markus Köster)

Developing industrial analytics solutions usually requires specific know-how in the data science domain. In the engineering domain, experience in data science is sparse, which prevents unleashing the power of artificial intelligence and machine learning on the factory shop floor. This talk highlights challenges and our experience in implementing AI analytics models for real-world machinery applications, by addressing why a pure data driven approach does not lead to satisfying models. To summarize, Markus names five factors for successfully adopting AI and ML in the engineering domain.

2. Controlling Manufacturing Processes with Causal Models (Maksim Greiner)

Modelling manufacturing processes often suffers from limited training data and spurious correlations. With Bayesian networks we can combine data and process knowledge to amplify the data and reduce spurious correlations, the most important part being the causal structure of a process. This session will present several examples for the manufacturing industry (e.g., from Festo and BMW), explain how causal structure relate to Bayesian networks and how they can be used to predict the outcomes and calculate optimal parameters.