@article{Samuel_Sumaila_Dan-Asabe_2022, title={CELLULOSIC FIBER REINFORCED HYBRID COMPOSITE (PxGyEz) OPTIMIZATION FOR LOW WATER ABSORPTION USING THE ROBUST TAGUCHI OPTIMIZATION TECHNIQUE}, volume={45}, url={https://jurnalmekanikal.utm.my/index.php/jurnalmekanikal/article/view/432}, DOI={10.11113/jm.v45.432}, abstractNote={<p>The manufacturing process of a material is a strong determinant of its performance in service. Different applications like ships, wind turbine blades, oil rigs, etc demand materials with low water absorption due to their operational environment. Previous studies have reported the water absorption behavior of cellulosic fiber-reinforced composites but the optimization of the water absorption properties of pineapple leaf/glass fiber hybrid reinforced epoxy composites by optimizing its manufacturing parameters have not been studied even with its possible wide range of application. This paper presents the optimization of the water absorption properties of a material P<sub>x</sub>G<sub>y</sub>E<sup>z</sup> (with x, y, and z representing the volume fraction of pineapple leaf fiber (PALF) (P), the volume fraction of glass fiber (G), and fiber length respectively in an epoxy matrix) using the Taguchi robust optimization technique and statistical analysis. The material at x=15%, y=15%, and z=20mm which is, P<sub>15</sub>G<sub>15</sub>E<sup>20</sup> was the optimum having the lowest water absorption of 0.2667%. A notable observation was that fiber length had a significant contribution to the water absorption properties of the material. The interaction effect of fiber length with the cellulosic fiber and the glass fiber at mean values was found to be 49.37% and 14.24% respectively. SEM and FTIR analysis showed microstructural and chemical formations that explained the water absorption behavior of the optimized hybrid composite. The water absorption property of the material was modeled and the equations proved to be 95.6% accurate in predicting the water absorption of the material at different combinations.</p>}, number={01}, journal={Jurnal Mekanikal}, author={Samuel, Bassey and Sumaila, Malachy and Dan-Asabe, Bashar}, year={2022}, month={Jun.}, pages={1–20} }