Murat Oduncuoglu

Wear Behavior of Aluminum Matrix Hybrid Composites Fabricated through Friction Stir Welding Process

Effects of friction stir processing (FSP) parameters and reinforcements on the wear behavior of 6061-T6 based hybrid composites were investigated. A mathematical formulation was derived to calculate the wear volume loss of the composites. The experimental results were contrasted with the results of the proposed model. The influences of sliding distance, tool traverse and rotational speeds, as well as graphite (Gr) and titanium carbide (TiC) volume fractions on the wear volume loss of the composites were also investigated using the prepared formulation. The results demonstrated that the wear volume loss of the composites significantly increased with increasing sliding distance, tool traverse speed, and rotational speed; while the wear volume loss decreased with increasing volume fraction of the reinforcements. A minimum wear volume loss for the hybrid composites with complex reinforcements was specified at the inclusion ratio of 50% TiG+50% Al2O3 because of improved lubricant ability, as well as resistance to brittleness and wear. New possibilities to develop wear-resistant aluminum-based composites for different industrial applications were proposed.

Effects of Temperature, Time, Magnesium, and Copper on the Wettability of Al/TiC System

The effects of temperature, time, and the additions of magnesium and copper on the wetting behavior of Al/TiC are studied theoretically. Mathematical formula is presented in explicit form. The effect of each variable is investigated by using the obtained equation. It is observed that the time and temperature have a stronger effect on the wetting of TiC in comparison to other input parameters. The proposed model shows good agreement with test results and can be used to find the wetting behavior of Al/TiC. The findings led to a new insight of the wetting process of TiC.

Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites

In the current study, the effect of applied load, sliding speed, and type and weight percentages of reinforcements on the wear properties of ultrahigh molecular weight polyethylene (UHMWPE) was theoretically studied. The extensive experimental results were taken from literature and modeled with artificial neural network (ANN). The feed forward (FF) back-propagation (BP) neural network (NN) was used to predict the dry sliding wear behavior of UHMWPE composites. Eleven input vectors were used in the construction of the proposed NN. The carbon nanotube (CNT), carbon fiber (CF), graphene oxide (GO), and wollastonite additives are the main input parameters and the volume loss is the output parameter for the developed NN. It was observed that the sliding speed and applied load have a stronger effect on the volume loss of UHMWPE composites in comparison to other input parameters. The proper condition for achieving the desired wear behaviors of UHMWPE by tailoring the weight percentage and reinforcement particle size and composition was presented. The proposed NN model and the derived explicit form of mathematical formulation show good agreement with test results and can be used to predict the volume loss of UHMWPE composites.