Wear modeling and friction-induced noise: a review

Abstract

Wear and friction-induced noise are pivotal tribological phenomena that significantly influence the longevity and efficiency of mechanical systems. This review synthesizes current research on wear modeling and friction-induced noise, exploring their mechanisms, influencing factors, and predictive challenges. Wear modeling encompasses a range of approaches, from traditional methods such as the Archard equation to more advanced numerical and machine learning techniques. These models address diverse mechanisms—adhesive, abrasive, and fatigue wear—which are shaped by material properties, surface roughness, and environmental conditions. Friction-induced noise, arising from stick-slip, sprag-slip, and mode-coupling, is influenced by surface states, damping, and operational parameters. Crucially, wear and noise are interlinked. Wear reshapes surfaces and dynamics, thereby modulating noise, while noise can serve as a diagnostic tool for wear progression. Yet, existing models often isolate these phenomena, neglecting their synergy and impeding accurate system-life predictions. This review highlights this gap and advocates for the development of integrated wear-noise models, harnessing multiscale simulations, advanced computation, and empirical validation. The development of such models has the potential to significantly enhance the accuracy of durability and acoustic performance predictions. They offer a holistic framework that captures the dynamic interplay between surface degradation and noise generation. This framework is essential for advancing non-invasive detection technologies in industries such as automotive, aerospace, and manufacturing. In these sectors, addressing these dual challenges is crucial for enhancing performance, safety, and efficiency.wear