@article{article_1444907, title={Investigation of The Abrasive Wear Behavior of GFRC And CFRC with Different Parameters Using Taguchi And Artificial Neural Networks Method}, journal={Politeknik Dergisi}, volume={28}, pages={215–228}, year={2025}, DOI={10.2339/politeknik.1444907}, author={Demir, Mehmet Emin}, keywords={GFRC, CFRC, Abrasive wear, Taguchi, ANN}, abstract={Fiber-reinforced composites are increasingly being utilized in various sectors, including aerospace, maritime, electronic components, and in elements exposed to wear such as bolts, nuts, cams, and gaskets. This study aims to determine the optimal processing parameters in abrasive wear tests conducted under varying wear conditions on glass and carbon fiber-reinforced composites. Employing a mixed-level L36 Taguchi orthogonal experimental design, tests were conducted on a pin on disk apparatus under different loads, sliding distances, and speeds. The results indicated that the most significant parameters affecting the coefficient of friction (COF) and mass loss were the type of fiber and the load. It was observed that an increase in load, sliding distance, and speed augmented the COF and mass loss. Predictions of the coefficient of friction and mass loss were made using a model developed in Artificial Neural Networks (ANN), and these predictions were compared with experimental results. The R2 overall regression values for COF and mass loss in ANN were calculated as 0.98939 and 0.98349, respectively. ANN was found to provide more consistent results in predicting COF and mass loss compared to the Taguchi method.}, number={1}, publisher={Gazi University}