Prediction of Tribological Properties of UHMWPE/SiC Polymer Composites Using Machine Learning Techniques.

Polymer composites are a class of material that are gaining a lot of attention in demanding tribological applications due to the ability of manipulating their performance by changing various factors, such as processing parameters, types of fillers, and operational parameters. Hence, a number of samples under different conditions need to be repeatedly produced and tested in order to satisfy the requirements of an application. However, with the advent of a new field of triboinformatics, which is a scientific discipline involving computer technology to collect, store, analyze, and evaluate tribological properties, we presently have access to a variety of high-end tools, such as various machine learning (ML) techniques, which can significantly aid in efficiently gauging the polymer's characteristics without the need to invest time and money in a physical experimentation. The development of an accurate model specifically for predicting the properties of the composite would not only cheapen the process of product testing, but also bolster the production rates of a very strong polymer combination. Hence, in the current study, the performance of five different machine learning (ML) techniques is evaluated for accurately predicting the tribological properties of ultrahigh molecular-weight polyethylene (UHMWPE) polymer composites reinforced with silicon carbide (SiC) nanoparticles. Three input parameters, namely, the applied pressure, holding time, and the concentration of SiCs, are considered with the specific wear rate (SWR) and coefficient of friction (COF) as the two output parameters. The five techniques used are support vector machines (SVMs), decision trees (DTs), random forests (RFs), k-nearest neighbors (KNNs), and artificial neural networks (ANNs). Three evaluation statistical metrics, namely, the coefficient of determination (R2-value), mean absolute error (MAE), and root mean square error (RMSE), are used to evaluate and compare the performances of the different ML techniques. Based upon the experimental dataset, the SVM technique was observed to yield the lowest error rates-with the RMSE being 2.09 × 10-4 and MAE being 2 × 10-4 for COF and for SWR, an RMSE of 2 × 10-4 and MAE of 1.6 × 10-4 were obtained-and highest R2-values of 0.9999 for COF and 0.9998 for SWR. The observed performance metrics shows the SVM as the most reliable technique in predicting the tribological properties-with an accuracy of 99.99% for COF and 99.98% for SWR-of the polymer composites.

Sliding and Rolling Motion of a Ferro-Liquid Droplet on the Hydrophobic Surface under Magnetic Influence.

The ferro-liquid droplet manipulation on hydrophobic surfaces remains vital for various applications in biomedicine, sensors and actuators, and oil-water separation. The magnetic influence of ferro-liquid droplets on the hydrophobic surface is elucidated. The mechanisms of a newborn droplet formation under the magnetic force are explored. The sliding and rolling dynamics of the ferro-liquid droplets are assessed for the various concentrations wt % of ferro-particles. High-speed recording and a tracker program are used to evaluate the droplet sliding and translational velocities. It is demonstrated that the mode of droplet motion changes from sliding to rolling as the magnetic Bond number increases, in which case, the droplet position becomes close to the magnet surface. The translational velocity of the droplet under rolling mode increases as the ferro-particle concentration in the droplet fluid increases. A further increase of the magnetic Bond number results in the creation of a newborn droplet attached to the magnet surface.

Hydrophobized metallic meshes can ease water droplet rolling.

Rolling liquid droplets are of great interest for various applications including self-cleaning of surfaces. Interfacial resistance, in terms of pinning and shear rate, has a critical role in droplet rolling dynamics on hydrophobic surfaces. Lowering the interfacial resistance requires reducing the droplet wetting length and droplet fluid contact area on hydrophobic surfaces. The present study examines droplet rolling behavior on inclined hydrophobized metallic meshes, which facilitate reduced wetting length and contact area of droplets. Experiments are carried out using a high-speed recording facility to evaluate droplet translational and rolling velocities over various sizes of hydrophobized meshes. The flow field inside the droplet fluid is simulated in 3-dimensional space mimicking the conditions of experiments. The findings reveal that droplet translational velocity attains significantly higher values for hydrophobized meshes than plain hydrophobized metallic surfaces. Increasing the mesh size enhances the droplet velocity and reduces the droplet kinetic energy dissipation created by interfacial surface tension and shear forces. Increasing the droplet volume enhances the droplet velocity despite the fact that pinning and frictional forces increase at the liquid-mesh interface. Hence, for rolling droplets on the mesh surface, the increase in the gravitational force component becomes larger than the increase in interfacial pinning and frictional forces.

Droplet Rolling Dynamics over a Hydrophobic Surface with a Minute Width Channel.

Unidirectional and stabilize droplet rolling over hydrophobic surfaces is critical for self-cleaning applications of large areas. Introducing minute size channels on hydrophobic surfaces in the droplet rolling direction can minimize droplet wobbling and enables unidirectional rolling. The droplet rolling behavior over an inclined hydrophobic surface having a minute size channel is investigated. The flow field developed inside the droplet fluid is numerically simulated in a three-dimensional domain pertinent to experimental conditions. Experiments are carried out using a high-speed facility to monitor and evaluate droplet motion over channeled and flat hydrophobic surfaces. The findings revealed that predictions of the droplet translational velocity and those obtained from the experiments are in good agreement. The presence of a minute channel on the hydrophobic surface gives rise to droplet fluid inflection into the minute channel, which in turn modifies the center of mass of the droplet during rolling. This lowers the droplet wobbling height and enables the droplet to roll unidirectionally along the channel length. Enlarging the channel width on the hydrophobic surface increases droplet kinetic energy dissipation while reducing the droplet rolling speed. The complex flow structures formed in the droplet fluid modifies the pressure along the droplet centerline; however, the droplet fluid pressure remains almost the same order as the Laplace pressure in the upper region of a rolling droplet over the channeled hydrophobic surface.

Sliding Water Droplet on Oil Impregnated Surface and Dust Particle Mitigation.

Self-cleaning of surfaces becomes challenging for energy harvesting devices because of the requirements of high optical transmittance of device surfaces. Surface texturing towards hydrophobizing can improve the self-cleaning ability of surfaces, yet lowers the optical transmittance. Introducing optical matching fluid, such as silicon oil, over the hydrophobized surface improves the optical transmittance. However, self-cleaning ability, such as dust mitigation, of the oil-impregnated hydrophobic surfaces needs to be investigated. Hence, solution crystallization of the polycarbonate surface towards creating hydrophobic texture is considered and silicon oil impregnation of the crystallized surface is explored for improved optical transmittance and self-cleaning ability. The condition for silicon oil spreading over the solution treated surface is assessed and silicon oil and water infusions on the dust particles are evaluated. The movement of the water droplet over the silicon oil-impregnated sample is examined utilizing the high-speed facility and the tracker program. The effect of oil film thickness and the tilting angle of the surface on the sliding droplet velocity is estimated for two droplet volumes. The mechanism for the dust particle mitigation from the oil film surface by the sliding water droplet is analyzed. The findings reveal that silicon oil impregnation of the crystallized sample surface improves the optical transmittance significantly. The sliding velocity of the water droplet over the thick film (~700 µm) remains higher than that of the small thickness oil film (~50 µm), which is attributed to the large interfacial resistance created between the moving droplet and the oil on the crystallized surface. The environmental dust particles can be mitigated from the oil film surface by the sliding water droplet. The droplet fluid infusion over the dust particle enables to reorient the particle inside the droplet fluid. As the dust particle settles at the trailing edge of the droplet, the sliding velocity decays on the oil-impregnated sample.