Team explores how wear and noise are interlinked in mechanical systems
Integrated wear-noise models are needed
image: Wear and noise are interlinked. Wear reshapes surfaces and dynamics, so it modulates noise, while noise serves as a diagnostic tool for wear progression. However, existing models often isolate these phenomena. This paper advocates for the development of integrated wear-noise models.
Credit: Friction, Tsinghua University Press
In the fields of material science and engineering, understanding the principles of wear and friction is pivotal for the development and optimization of materials and mechanical systems. A research team has synthesized the current research on wear modeling and friction-induced noises, exploring their mechanisms, influencing factors, and predictive challenges. By studying wear modeling and friction-induced noise, engineers are better able to predict the lifespans of components and prevent structural failure across a wide range of industries.
The team’s review is published in the journal Friction on February 10, 2026.
In their review, the team comprehensively explores the current state of research on wear modeling and friction-induced noises. These are two pivotal phenomena in tribology, the study of surfaces moving in relative motion. Wear modeling and friction-induced noises significantly influence the durability, efficiency, and operational smoothness of mechanical systems.
“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,” said Professor Yang Tian from Cranfield University. The team highlights this gap and advocates for the development of integrated wear–noise models, harnessing multiscale simulations, advanced computation, and empirical validation.
In their review the team explains the foundational principles governing these processes, such as Coulomb’s law, which explains how electrically charged objects push or pull on each other, and the Archard model, a formula used to predict how materials wear when they slide against each other.
The team also details a spectrum of wear mechanisms, including adhesion, abrasion, erosion, fretting, impact, and fatigue, along with various factors affecting wear, such as thermal effects, humidity, material properties, and surface roughness. They dissect the mechanisms that drive friction-induced noise, such as stick-slip, sprag-slip, and mode-coupling, identifying the key influencing factors, including surface conditions, damping, and operating parameters.
There are challenges for scientists studying wear modeling. These include the complexity of wear processes, the simultaneous occurrence of multiple wear mechanisms, and the dynamic changes in material and surface properties during sliding interactions.
In looking at friction-induced noise, the team emphasizes the intricate interplay of various mechanisms and system factors, including material properties, surface characteristics, damping, and operating conditions. These collectively govern how noise is generated and propagated.
Complex interrelationships exist between wear and noise. Wear changes surface interactions, characteristics, and system dynamics, thereby influencing noise generation. Conversely, noise often provides as an audible indication of wear progression. “This dynamic interplay is essential for assessing component lifespan, optimizing system performance, and mitigating issues related to wear and noise disturbances,” said Tian.
In recent years, scientists have made significant advances in modeling approaches, ranging from analytical and empirical methods to sophisticated numerical techniques such as finite element analysis, cellular automaton, particle-based simulations, and molecular dynamics. However, challenges remain. Scientists are still working to develop integrated models that simultaneously address wear and friction-induced noise. The current research predominantly addresses these phenomena as separate pieces of the puzzle, often overlooking their connections to each other. “The lack of unified frameworks that capture the dynamic feedback between wear-induced surface changes and noise generation hinders the ability to predict and manage these effects holistically,” said Tian.
Looking ahead, the team recommends that future research focus on the development of comprehensive models that integrate wear and noise. These models should take advantage of the advancements in computational tools, such as multiscale modeling and machine learning, while also using refined experimental techniques. The team suggests that these models should consider both the multifaceted interactions among mechanical, thermal, and chemical factors, as well as the evolving contact dynamics that are influenced by wear. “By addressing these gaps, integrated approaches will facilitate the design of mechanical systems that are more durable, efficient, and quieter. This will advance the field of tribology and enhance its practical applications across industries such as automotive, aerospace, and manufacturing,” said Tian.
The research team includes Yang Tian, Muhammad Khan, and Bohao Zheng from Cranfield University, UK; and Hao Yuan from Nanyang Technological University, Singapore.
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