Abstract
Remarkable advancements in high-performance computing and artificial intelligence, featuring state-of-the-art techniques such as Computer Vision and Reinforcement Learning, are revolutionizing numerous industries. The lubricants sector is an industry that benefits greatly from these advancements especially due to its extensive yet unstructured historical data. Employing Computer Vision can significantly enhance the analysis of surface effects due to the lubricant and wear leading to superior accuracy, reliability, and resolution in understanding these characteristics. Reinforcement Learning plays a pivotal role in devising lubricant digital twins that effectively simulate behaviors under established conditions while facilitating the detection of anomolous behaviour. The seamless integration of these computational methods with traditional chemical and physical research accelerates product development and fosters a deeper understanding of the complex mechanisms driving product performance. This paper delves into the successful implementation of these innovative techniques at Fuchs Lubricants, highlighting their indispensable contribution to the company’s research and development endeavors.