Advancing Nitrocarburizing through Hybrid AI (NitroHAI)

Purpose and goal

With the rapid development of AI and machine learning, there is a huge potential to leverage these technologies for increased resource efficiency and process optimization in industry. In this project we will focus on process optimization of nitrocarburizing which is a heat treatment process used for steel components and optimize for lower resource consumption, reduced carbon footprint and increased product quality.

Expected effects and result

Expected results of the project is a hybrid AI model that can be used to optimize furnace recipes to achieve desired end product properties, and to reduce resource consumption and carbon emissions

Planned approach and implementation

The project will develop an Hybrid AI-solution based on physics-informed machine learning, causal modeling and Bayesian experimental design, to model the nitrocarburization process from thermochemical reactions to final surface properties, which will enable simulation, prediction, optimization of process results, dynamic process control and study of necessary actions when disturbances occur.