Surface texturing has been frequently used for tribological purposes in the last three decades due to its great potential to reduce friction and wear. Although biological systems advocate the use of hierarchical, multi-scale surface textures, most of the published experimental and numerical works have mainly addressed effects induced by single-scale surface textures. Therefore, it can be assumed that the potential of multi-scale surface texturing to further optimize friction and wear is underexplored. The aim of this review article is to shed some light on the current knowledge in the field of multi-scale surface textures applied to tribological systems from an experimental and numerical point of view. Initially, fabrication techniques with their respective advantages and disadvantages regarding the ability to create multi-scale surface textures are summarized. Afterwards, the existing state-of-the-art regarding experimental work performed to explore the potential, as well as the underlying effects of multi-scale textures under dry and lubricated conditions, is presented. Subsequently, numerical approaches to predict the behavior of multi-scale surface texturing under lubricated conditions are elucidated. Finally, the existing knowledge and hypotheses about the underlying driven mechanisms responsible for the improved tribological performance of multi-scale textures are summarized, and future trends in this research direction are emphasized.

Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

This paper presents a comparison between the results from numerical modelling and experiments to shed light on the mechanisms by which surface texture can reduce friction when applied to an automotive cylinder liner. In this configuration, textured features move relative to th e piston-liner conjunction and to account for this our approach is to focus on the transient friction response to individual pockets as they pass through, and then leave, the sliding contact. The numerical approach is based on the averaged Reynolds’ equat ion with the Patir & Cheng’s flow factors and the p- θ Elrod- Adams mass-conserving cavitation model. The contact pressures that arises from the asperity interactions are solved simultaneously to the fluid flow solution using the Greenwood and Tripp method. The experimental data is produced using a pin-on -disc set up, in which laser textured pockets have been applied to the disc specimen. Under certain conditions in the mixed and boundary lubrication regime s, both model and experimental results show i ) an increase in friction as the pocket enters the contact, followed by ii ) a sharp decrease as the pocket leaves the contact, and then iii ) a gradual decay back to the pre-entrainment value. From the evidence obtained for the first time from the proposed combined modelling and experimental investigation conducted under carefully controlled conditions, w e suggest that these three stages occur due to the following mechanisms: i ) a reduction in fluid pressure due to the increased inlet gap, ii ) inlet suction as the cavitated fluid within the pocket draws lubricant into the contact, and iii ) film thickness decay as oil is squeezed out of the contact. The interplay of these three mechanisms is shown to control the response of micro-textured surfaces under all lubrication regimes