TY - JOUR
T1 - Effect of MWCNT/ Al2O3/ boron nitride fillers based natural/carbon/ innegra fabrics/ SS-WM/ Iron-WM reinforced UV resistant polyester composites
AU - Mohit, H.
AU - Sanjay, M. R.
AU - Srisuk, Rapeeporn
AU - Siengchin, Suchart
AU - Althomali, Raed H.
AU - Alzahrani, Khalid A.
AU - Asiri, Abdullah M.
AU - Khan, Anish
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - The wide application of natural fiber, inorganic particles, synthetic or metallic fabrics-based composites is the present trend in the field of research. The present investigation focuses on the reinforcement of natural (pineapple leaf, coir, and water hyacinth), inorganic (multi-walled carbon nanotubes, titanium carbide, and boron nitride) fillers, fabrics (jute, flax, carbon, Innegra, and basalt), and metallic wire mesh (stainless steel and iron) in ultra-violet (UV) polyester resin to enhance the physical, mechanical, and thermal characteristics. It was examined from the experimental results that BfWTMPR (Basalt fabric, water hyacinth, titanium carbide, and multi-walled carbon nanotubes reinforced polyester) sample showed higher mechanical properties with tensile, and interlaminar shear strength of 213.91, and 181.5 MPa, respectively. The uniform distribution of fillers, and fiber pull out have been examined from the tensile fracture of the sample using scanning electron microscope. The present investigation also determines the performance and forecasting of the artificial neural network (ANN) to model the material properties of polyester composites. The forecasting of the modeled outcomes was compared with the experiment and examined consistently with the collected values. Statistical analysis was also conducted to examine significance of results under 95% of confidence level. The reliability and importance of such laminates could support replacing the conventional material for automotive applications.
AB - The wide application of natural fiber, inorganic particles, synthetic or metallic fabrics-based composites is the present trend in the field of research. The present investigation focuses on the reinforcement of natural (pineapple leaf, coir, and water hyacinth), inorganic (multi-walled carbon nanotubes, titanium carbide, and boron nitride) fillers, fabrics (jute, flax, carbon, Innegra, and basalt), and metallic wire mesh (stainless steel and iron) in ultra-violet (UV) polyester resin to enhance the physical, mechanical, and thermal characteristics. It was examined from the experimental results that BfWTMPR (Basalt fabric, water hyacinth, titanium carbide, and multi-walled carbon nanotubes reinforced polyester) sample showed higher mechanical properties with tensile, and interlaminar shear strength of 213.91, and 181.5 MPa, respectively. The uniform distribution of fillers, and fiber pull out have been examined from the tensile fracture of the sample using scanning electron microscope. The present investigation also determines the performance and forecasting of the artificial neural network (ANN) to model the material properties of polyester composites. The forecasting of the modeled outcomes was compared with the experiment and examined consistently with the collected values. Statistical analysis was also conducted to examine significance of results under 95% of confidence level. The reliability and importance of such laminates could support replacing the conventional material for automotive applications.
KW - Artificial neural network
KW - Contact angle
KW - Glass transition temperature
KW - Mechanical characteristics
KW - Polyester hybrid composites
UR - http://www.scopus.com/inward/record.url?scp=85169820433&partnerID=8YFLogxK
U2 - 10.1016/j.matchemphys.2023.128383
DO - 10.1016/j.matchemphys.2023.128383
M3 - Article
AN - SCOPUS:85169820433
SN - 0254-0584
VL - 309
JO - Materials Chemistry and Physics
JF - Materials Chemistry and Physics
M1 - 128383
ER -