Understanding coating physics is critical to the optimisation of any coating process in terms of efficiency and quality. In particular, measurement of paint thicknesses of multilayer structures in the automotive sector is now done by contactless coating thickness measurement technologies that have emerged the last decades.

Our work for the #coatML project offers an effective digital twin solution to predict coating-chamber physics and to enable better and faster integration of coating thickness measuring devices into existing industrial coating setups. The feedback gained from ideally integrated measurement devices allows manufacturers to optimise their coating setups reducing resource consumption while increasing coating quality.
By using a novel machine learning (ML) method suitable for learning from fluid dynamics simulations and integrating it within the code MPflow we were able to overcome traditional barriers in the coating industry using Industry 4.0 technologies.
This is a combination of cloud computing and machine learning technology which undoubtedly has the potential to democratise Computer Aided Engineering to non-experts in manufacturing engineering and the general public.

You can learn more about coatML and Mind4Machines and how their Industry4.0 solutions are transforming the manufacturing industry at mind4machines.eu