Webinar 1: AI-enabled Optimization of Production Processes

Industrial production processes are often very complex: they consist of multiple steps and the different parameters of each individual production step can highly influence the end result. Consequently, obtaining good process parameters which guarantee product quality and productivity, can be a cumbersome task, particularly in case measuring (intermediate) product quality is hard and experimentation is expensive. In additions, it is often necessary to make a trade-off between conflicting objectives, e.g. minimizing process time while maximizing product quality.

During this webinar, we introduce new approaches making use of AI-techniques to optimize process parameters. Firstly, we will illustrate by means of an industrial adhesive joining use case how Gaussian Processes can be used to retrieve cost-effective process parameters which guarantee structural strength of the end product. Secondly, we will discuss how AI-techniques can be used to realize anomaly detection systems and active learning methods can provide online quality control for additive manufacturing, able to cope with changing manufacturing and environmental conditions, and demonstrate the effectiveness of the techniques using first experimental results. Finally, Nicolas Deruytter, CEO of ML6, will explain how Machine Learning is already used today to improve efficiency and reduce the carbon footprint of production processes