Optimization of Wear Parameters for Glass Fabric-Epoxy Composites using Response Surface Methodology and Flower Pollination Algorithm

Elkhouly, Heba I.; Aly, Mohamad. F.; Abd El-Magied, Ragab K.

doi:10.21608/jessit.2023.304324

Optimization of Wear Parameters for Glass Fabric-Epoxy Composites using Response Surface Methodology and Flower Pollination Algorithm

Authors

¹ Assistant Professor, Department of Mechanical Engineering, Faculty of Engineering, Beni-Suef University, Beni-Suef, Egypt

² Professor, Department of Industrial and Manufacturing Engineering, Faculty of Engineering, Fayoum University, Egypt

³ Professor, Department of Mechatronics Engineering, Faculty of Engineering, October 6 University, Egypt. And Department of Mechanical Engineering, Faculty of Engineering, Beni-Suef University, Egypt

10.21608/jessit.2023.304324

Abstract

The importance of glass fiber-reinforced epoxy lies in its physical and mechanical properties, making it suitable for various manufacturing applications. Additionally, it plays a significant role in enhancing the abrasive wear performance of materials. In this study, a Response Surface Methodology (RSM) was developed to optimize the control variables (including type and weight of filler, normal load, abrasive size, and abrading distance) for glass fabric-epoxy (G-E) composites with different organic (date seed (DS)) and inorganic (silicon carbide (SiC), aluminum oxide (Al2O3) fillers. The samples were prepared using a semi-automatic technique. The analysis of variance (ANOVA) was used to construct an empirical model to demonstrate the relationship between the control factors (filler weight, abrasive) and responses (specific wear rate) in glass fiber-reinforced epoxy. The results of the analysis showed that the filler weight, abrasive size, and their interaction have a significant effect on the specific wear rate. An integrated RSM and Flower Pollination Algorithm (FPA) approach was used to optimize the wear parameters. The results showed that the FPA approach demonstrated good agreement between the predicted and experimental values.

Keywords