Friction Behavior of Aluminum Bronze Reinforced by Boron Carbide Particles

Abstract

A promising composite material for tribotechnical applications based on aluminum bronze with reinforcing boron carbide particles fabricated by a special electron beam additive deposition technique was studied experimentally and numerically. Tribological experiments showed that reinforcing by carbide particles allowed reducing the coefficient of friction from 0.26 to 0.19 and improving the wear resistance by 2.2 times. Computer modeling reveals two main factors playing a significant role in the friction behavior of the studied metal matrix composite: the mechanical effect of reinforcing ceramic inclusions and effective hardening of the metal matrix due to the peculiarities of the 3D electron beam printing. The mechanical effect of hardening inclusions determines a more rounded shape of wear particles, preventing wedging, and thereby increasing the stability of friction. Strengthening the metal matrix leads to reducing the number of wear particles.

Numerical study of the influence of hardening inclusions in the surface layer of bronze on the features of sliding friction

“…Трёхмерная численная модель композита триботехнического назначения была построена на основе метода подвижных клеточных автоматов, который уже на протяжении двадцати лет успешно используется для решения подобных задач [2]. В отличие от ранее использованного двумерного приближения [3], в настоящей работе использована трёхмерная модель, которая обеспечивает более корректное описание напряжённого состояния в условиях трибологического контакта.…”

Peakforce Quantitative Nanomechanical Mapping for Surface Energy Characterization on the Nanoscale: A Mini-Review

“…Despite the importance of surface characterization of materials, the conventionally adopted method to determine the surface energy involves measuring the contact angle between the liquid droplet and the solid surface interface [17][18][19][20][21][22]. This liquid droplet contact angle measurement can only measure at bulk scale or microscale owing to the inert size of the liquid droplet, thereby limiting nanoscale measurements.…”

On the determination of the friction-caused energy losses and its potential for monitoring industrial tribomechanical systems

“…Primarily, factors are materials [2,3], load [4], surface roughness [5], sliding velocity [6,7], sliding mode [8], coating [9], lubricant [10,11], temperature [12], or their combinations [13,14]. For all these studies many researchers used analytical [15,16] and numerical approaches [17,18], mostly focusing on the experimental, measurement and modeling methods [19,20], or arti cial neural network (ANN) model to predict the friction coe cient based on experimental investigation [21].…”