About this Research Topic
The primary objective of this Research Topic is to explore and document promising recent and novel research trends in the utilization of fillers and polymers, enhanced by Machine Learning (ML) predictions, to create advanced composites. The focus will be on their application in various aerospace and automotive components. Machine Learning can significantly optimize the development and performance prediction of these composites, enabling faster, more accurate material design and testing. By integrating ML techniques, we can anticipate material behaviors, streamline the experimental process, and ultimately produce superior composite materials with tailored properties.
This Research Topic invites contributions that delve into the development, characterization, and application of advanced composite materials using fillers, polymers, and Machine Learning. Specific themes of interest include, but are not limited to:
- Development of Filler-Based Advanced Composites
- Application of Machine Learning in Predicting Composite Properties
- Mechanical Performance Studies (e.g., Tensile Strength, Flexural Strength, Impact Strength, Hardness)
- Tribo-Performance Analysis of Filler-Based Composites (e.g., Wear, Erosion, Corrosion Resistance)
- Advanced Composite Materials for Aerospace Applications and Their Characterization
- Characterization Techniques for Fillers and Polymers
- Structural Analysis and Predictive Modeling
- Sustainable and Recycled Fillers for Environmentally Friendly Composites
We welcome review articles and original research manuscripts addressing these and related topics.
Keywords: Advanced Composites, Fillers, Polymers, Machine Learning, Structural Applications, Aerospace, Automotive, Predictive Modeling, Sustainable Materials
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.