Glass Fiber-Epoxy Composites with Carbon Nanotube Fillers for Enhancing Properties in Structure Modeling and Analysis Using Artificial Intelligence Technique

Abstract

Hybrid composite materials are a form of material that incorporates more than one type of reinforcement into a matrix to attain enhanced qualities. This usually includes the use of nanoparticle fillers in classic advanced composites with fiber reinforcements such as carbon or glass. In the current investigation, the impact of carbon nanopowder filler on the wear and thermal performance of the chopped strand mat E-glass fiber-reinforced epoxy composite (GFREC) were analyzed. Multiwall carbon nanotube (MWCNT) fillers were used; they react with the resin system to contribute a significant improvement of properties in the polymer cross-linking web. The experiments were carried out employing the central composite method of design of experiment (DOE). A polynomial mathematical model was created using response surface methodology (RSM). To forecast the wear rate of composites, four machine learning (ML) regression models were built. The study’s findings indicate that the addition of carbon nanopowder has a substantial impact on the wear behavior of composites. This is mostly owing to the homogeneity created by the carbon nanofillers in uniformly dispersing the reinforcements in the matrix phase. Results revealed that a load of 1.005 kg, a sliding velocity of 1.499 m/s, a sliding distance of 150 m, and 15 wt % of filler were found to be the optimal parameters for the efficient reduction of specific wear rate. Composites with 10 and 20% carbon contents exhibit lower thermal expansion coefficients than plain composites. These composites’ coefficients of thermal expansion fell by 45 and 9%, respectively. If the carbon proportion increases beyond 20%, so will the thermal coefficient of expansion.

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

“…Machine learning (ML) is vital in investigating and optimizing fiber-reinforced epoxy composites (FRECs). ML approaches are useful for evaluating large data sets, predicting material behavior, improving composite qualities, and speeding up the creation of novel materials. , By identifying the optimal mix of fibers, epoxy resin, and other additives, ML algorithms can aid in the design and optimizing FRECs. These algorithms may examine material attributes, processing circumstances, and cost limitations to recommend formulations that satisfy specific needs .…”

Effect of TiO2 Nanofillers on the Mechanical and Abrasive Wear Properties of Epoxy Reinforced with Carbon Fabric Hybrid Composites

“…Al-Zubaydi et al studied the effect of nano TiO 2 particles on the properties of carbon fiber-epoxy composites and showed improved mechanical and wear resistance properties with the addition of 4% TiO 2 . Vaddar et al investigated glass fiber-epoxy composites with carbon nanotube fillers for enhancing properties in structure modeling and analysis using artificial intelligence technique, and the results revealed that a load of 1.005 kg, a sliding velocity of 1.499 m/s, a sliding distance of 150 m, and 15 wt % of filler were found to be the optimal parameters for the efficient reduction of specific wear rate. Navaneeth et al investigated “Damped Free Vibration Analysis of Woven Glass Fiber-Reinforced Epoxy Composite Laminates” which showed a good agreement.…”