Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Abstract

To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

Advances in application of machine learning to life cycle assessment: a literature review

“…Also, in LCA at the miscellaneous level of production, ML algorithms can have an impact in reducing GHG emissions in LCA for geographically differentiated and contextualized design measures; however, they are still underutilized for such applications (Milojevic-Dupont and Creutzig 2021). Kurdi et al reviewed methods for simulation in tribology to model tribo-contact scenarios and investigated LCA with simulation combined with ML (Kurdi et al 2020). Wu and Wang reviewed ML methods applied to toxicity prediction and discussed the ML algorithm's input parameter to enhance prediction accuracy (Wu and Wang 2018).…”

A review of recent advances and applications of machine learning in tribology

“…[9][10][11][12] For example, high energy consumption, failure of industrial processes, mechanical equipment, and increased transportation cost caused by unavoidable friction and wear have motivated researchers to understand the tribological phenomenon at different time and length scales. [13][14][15][16] In recent years, a majority of these studies have developed and employed stateof-the-art experimental methods with the intention of reducing friction and wear, which account for one-fifth of all annual energy consumption. 17 Specifically, advanced experimental techniques such as nanoindentation, atomic force microscopy and imaging, 18 digital image correlation, 19 and in situ spectroscopy, have been developed with the aim of studying tribological systems.…”

Effect of Silicon Carbide Nanoparticles on the Friction-Wear Properties of Copper-Based Friction Discs

“…Thus, the study of additives to improve the friction-wear properties of CBFM is necessary and urgent. The silicon carbide particles (SCN), which have the advantages of stable chemical properties, high thermal conductivity, low thermal expansion coefficient, and excellent wear resistance, have been widely used as an abrasive in composite materials [28][29][30][31]. It was employed to improve tribological properties such as anti-wear, reducing friction, and high load capacity [32].…”