The surface oxide between function and failure

Important results from the project

The project aim was to accelerate the development of corrosion-resistant materials through advanced predictive models. We have developed molecular dynamic models to simulate surface oxide and corrosion, improving understanding of the materials´ behavior. Electrochemical accelerated tests in an industrial-like environment have been carried out, increasing predictive certainty. By integrating AI-powered image recognition, we have been able to analyze corroded samples with high precision. The project has come a long way towards the goal of optimizing material performance.

Expected long term effects

The implementation of the project has led to advancements in corrosion research. The developed computer models and methods for accelerated testing are research that leads to materials performing better in corrosive environments. AI-driven analyses of corroded samples have great potential to enhance our understanding of corrosion mechanisms. Expected effects include longer product lifespans and reduced maintenance costs, benefiting both industry and the environment. Thus, the project has achieved many of its goals and created valuable insights for future applications.

Approach and implementation

The project´s analysis strategy focuses on advanced experiments and data modeling to understand pitting corrosion in stainless steel. Through electrochemical testing, synchrotron characterization, and molecular dynamics modeling, we map corrosion processes. AI-driven image analysis of corrosion samples complements these studies, enabling deeper insights and more accurate predictions of material durability and behavior under extreme conditions. This multifaceted methodology aims to accelerate research and deliver practical solutions for the industry.

External links

Forskningen har blivit en viktig del av avdelningen för Industriell Produktion på LTH och beskrivs på dess hemsida.