Tribological Performance of Random Sinter Pores vs. Deterministic Laser Surface Textures: An Experimental and Machine Learning Approach

*Guido Boidi, Philipp G. Grützmacher, Markus Varga, Márcio Rodrigues da Silva, Carsten Gachot, Daniele Dini, Francisco J. Profito and Izabel F. Machado*

## **Abstract**

This work critically scrutinizes and compares the tribological performance of randomly distributed surface pores in sintered materials and precisely tailored laser textures produced by different laser surface texturing techniques. The pore distributions and dimensions were modified by changing the sintering parameters, while the topological features of the laser textures were varied by changing the laser sources and structuring parameters. Ball-on-disc tribological experiments were carried out under lubricated combined sliding-rolling conditions. Film thickness was measured *in-situ* through a specific interferometry technique developed for the study of rough surfaces. Furthermore, a machine learning approach based on the radial basis function method was proposed to predict the frictional behavior of contact interfaces with surface irregularities. The main results show that both sintered and laser textured materials can reduce friction compared to the untextured material under certain operating conditions. Moreover, the machine learning model was shown to predict results with satisfactory accuracy. It was also found that the performance of sintered materials could lead to similar improvements as achieved by textured surfaces, even if surface pores are randomly distributed and not precisely controlled.

**Keywords:** lubrication, friction reduction, laser surface texturing, sintered material, machine learning

#### **1. Introduction**

The surface topography of mechanical components is often modified to improve their tribological performance, such as cylinder liner honing in internal combustion engines [1] and laser surface texturing in piston-rings [2, 3], rolling element bearings [4], and journal- and thrust bearing systems [5].

Surface textures can promote several tribological improvements. Surface cavities can reduce abrasive wear in harsh contact conditions by retaining wear and

contaminant particles (debris trapping effect), as well as work as micro-reservoirs and secondary oil suppliers (oil reservoir effect) [6]. Under mixed and (elasto) hydrodynamic lubrication, friction can be reduced by the interplay of different mechanisms, such as the (i) micro-hydrodynamic bearing (boosting in the fluid pressures due to the texture-induced cavitation at the divergent regions of the textures) [7], (ii) inlet suction (lubricant sucking into the interface due to the difference between the supply pressure and the cavitation pressure) [3, 8, 9], and (iii) shear-area variation (decrease of the fluid and contact shear stresses over the contact area) [10] mechanisms. Furthermore, under boundary and mixed lubrication regimes, textures can influence the sealing performance and percolation behavior. The reader is referred to the comprehensive reviews [4, 11–14] for a more in-depth evaluation of several aspects of surface texturing for tribological improvements.

The tribological effects of surface texturing were first studied in the 1960s by Hamilton's and Anno's research groups [15, 16]. Afterwards, this topic covered a marginal role in the tribological community until the 1990s, when Etsion and coworkers re-discovered its potential impact [17, 18], also in the context of improved manufacturing techniques. In recent years, the interest in research on surface texturing, mainly laser texturing, has significantly increased [11] as some texture configurations have shown significant tribological improvements in various machine elements [2, 19–21].

The design of effective surface textures requires a thorough understanding of the tribosystem's characteristics and the capabilities and limitations of the texturing techniques available. General guidelines for texture design could be found in [4, 12]. Nevertheless, further in-depth research is still required to reveal the precise mechanisms (and their interplay) responsible for the improvements in tribological performance due to the presence of optimally designed micro-textured surfaces; this is still not fully understood. This lack of understanding is mainly related to the design and manufacturing limitations of optimal texture geometries for different components, the influence of varying operating conditions in transient applications, and the evolution of the texture geometry over the components' lifetime. Additionally, there is a particular debate on the effect of surface textures on the behavior of lubricated non-conformal contacts under different sliding-rolling conditions. Finally, to successfully and economically implement surface texturing in practical applications and industrial scale, the gain in tribological performance must compensate for the additional manufacturing steps leading to longer processing times and costs.

Surface porosity in sintered materials could be potentially used as inherent surface texture and saving extra manufacturing. Pore characteristics can be controlled, to certain limits, by the sintering parameters and powder properties [22]. However, in contrast to deterministic laser textures – with a precisely tailored topography – surface pores are statistically distributed and have irregular shapes. The effect of pores on mechanical proprieties of sintered materials was extensively studied [23–26]; however, the influence of surface porosity on the tribological performance is sparsely investigated [27–29].

Advanced statistics, artificial intelligence (AI) and machine learning (ML) methods have gained increasing importance in describing and interpreting scientific findings. In tribology research, ML & AI approaches have already been used for online condition monitoring of bearings, design of material composition, lubricant formulations, lubrication and fluid film formation analysis, among other applications [30–32]. An ML method was also recently proposed to predict the frictional performance of textured and porous interfaces [33], whose methodology could be extended to support the optimum design of surface texturing.

*Tribological Performance of Random Sinter Pores vs. Deterministic Laser Surface Textures… DOI: http://dx.doi.org/10.5772/intechopen.100245*

This study aims to quantitatively compare random surface features of porous sintered materials with distinctly manufactured laser surface textures in the particular case of lubricated non-conformal contacts. Especially the sliding-rolling conditions as occurring in roller-bearings are tackled. Finally, the radial basis ML method was used to enhance results interpretation and build a general predictive model to support the design of new surface features for obtaining superior tribological performances.
