A Short Review: Tribology in Machining to Understand Conventional and Latest Modeling Methods with Machine Learning

Abstract

Tribology plays a critical role in machining technologies. Friction is an essential factor in processes such as composite material machining and bonding. This short review highlights the recent advancements in controlling and leveraging tribological phenomena in machining. For instance, high-precision machining is increasingly relying on the in situ observation and real-time measurement of tools, test specimens, and machining equipment for effective process control. Modern engineering materials often incorporate functional materials in metastable states, such as composites of dissimilar materials, rather than conventional stable-phase materials. In these cases, tribological effects during machining can impede precision. On the other hand, the friction in additive manufacturing demonstrates a constructive application of tribology. Traditionally, understanding and mitigating these tribological phenomena have involved developing physical and chemical models for individual factors and using simulations to inform decisions. However, accurately predicting system behavior has remained challenging due to the complex interactions between machine components and the variations between initial and operational (or deteriorated) states. Recent innovations have introduced data-driven approaches that predict system behavior without the need for detailed models. By integrating advanced monitoring technologies and machine learning, these methods enable real-time predictions within controllable parameters using live data. This shift opens new possibilities for achieving more precise and adaptive machining control.