Machine learning based prediction on mechanical and wear characterization of sisal fiber reinforced dolomite dust-epoxy hybrid composites

This publication has 28 references indexed in Scilit:

• Epoxy‐hemp and epoxy‐flax composites filled with glass dust for enhanced thermal insulation: An analytical and experimental study
  Polymer Composites, 2024
• Steady state analysis on sliding wear behavior of waste marble dust filled kenaf‐polyester composites
  Materialwissenschaft und Werkstofftechnik, 2023
• Waste marble dust-filled sustainable polymer composite selection using a multi-criteria decision-making technique
  Arabian Journal of Chemistry, 2023
• Dolomite dust filled glass fiber reinforced epoxy composite: Influence of fabrication techniques on physicomechanical and erosion wear properties
  Polymer Composites, 2021
• An experimental investigation of epoxy‐based hybrid composites with hexagonal boron nitride and short sisal fiber as reinforcement for high performance microelectronic applications
  Polymer Engineering & Science, 2021
• Prediction of mechanical properties of Al-Si-Mg alloy using artificial neural network
  Sādhanā, 2021
• Influence of particulate surface treatment on physical, mechanical, thermal, and dielectric behavior of epoxy/hexagonal boron nitride composites
  Polymer Composites, 2019
• Prediction of mechanical properties of carbon fiber based on cross-scale FEM and machine learning
  Composite Structures, 2019
• Effect of alkali‐resistant glass fiber on polypropylene/polystyrene blends: Modeling and characterization
  Polymer Composites, 2014
• Properties of fillers and reinforcing fibers
  Mechanics of Composite Materials, 1998