Current Trends and Applications of Machine Learning in Tribology-A Review

Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

Automated summary

Machine learning and artificial intelligence are being increasingly used in tribology to sort through patterns and identify trends. Research covers various fields in tribology, from composite materials to lubricants, utilizing diverse applications and algorithms. This review aims to introduce and discuss the current trends and applications of ML and AI in the field, providing inspiration and support for researchers and engineers.