OPTIMISATION OF INJECTION MOULDED POLYPROPYLENE SAWDUST COMPOSITE USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS

Authors : Cyril ALİYEGBENOMA, Mercy OZAKPOLOR

Pages : 169-177

Doi:10.47933/ijeir.744495

View : 20 | Download : 14

Publication Date : 2020-11-30

Article Type : Research Paper

Abstract :This study focuses on the optimisation of the injection moulded Polypropylene -Sawdust insert ignore into journalissuearticles values(PP-sawdust); composite. The PP material and sawdust were mixed together to form a homogenous mixture with various percentage composition by volume as recommended by the design of experiments using the central composite design insert ignore into journalissuearticles values(CCD);. The two screw plunger injection moulding machine was used to produce Polypropylene-Sawdust insert ignore into journalissuearticles values(PP-Sawdust); composite at various temperature. The produced composites were evaluated for their mechanical properties which included tensile strength, proof stress, percentage elongation and flexural strength. The response surface methodology insert ignore into journalissuearticles values(RSM); and artificial neural networks insert ignore into journalissuearticles values(ANN); were used to determine the effect of the interaction of temperature, material type and percentage by volume of material on the mechanical properties of the produced PP-sawdust composite. The models were validated using coefficient of determination insert ignore into journalissuearticles values(R2);. The models were validated using coefficient of determination insert ignore into journalissuearticles values(R2);. The coefficient of determination insert ignore into journalissuearticles values(R2); obtained ranged from 0.9435 insert ignore into journalissuearticles values(94.357%); to 0.9988 insert ignore into journalissuearticles values(99.88%); which indicates that a substantial good fit was achieved by the model developed. A desirability of 0.952 was obtained which shows the adequacy of the model terms The optimization results for PP-Sawdust composites shows that the tensile strength, proof stress, flexural strength and flexural modulus were maximized with values of 31.90 MPa, 41.94 MPa, 88.22 MPa and 2.72 GPa respectively obtained at barrel temperature of 224.65 oC and polymer level of 45.56% while percentage elongation and average deflection were minimized with values of 74.12% and 6.46 cm respectively
Keywords : Central composite design, Composite, Mahogany, Modeling, Polypropylene, Sawdust, tensile strength