High-performance thermoplastic composites (TPCs) combine exceptional toughness, weldability, and recyclability, positioning them as promising materials for future aerospace structures. However, unlike metals or thermosetting composites, TPCs lack a comprehensive predictive framework that links processing conditions to microstructure evolution and structural performance.

This project addresses this gap by developing an integrated computational–materials framework capable of describing processing-induced microstructural changes, interfacial behavior, and failure mechanisms in carbon-fiber–reinforced TPCs. The project unites complementary expertise from Michigan Technological University and UC San Diego. Central to this network is a close, long-standing partnership with Brett A. Bednarcyk and Evan J. Pineda at NASA Glenn Research Center. Together, this unique cooperation integrates multi-scale mechanics, continuum modeling, molecular insights, and data-driven optimization.

Research Focus

• Multi-scale & multi-physics modeling framework: Establishing a predictive description of how processing histories shape microstructure, residual stresses, and effective material behavior.

• Interface and delamination behavior: Developing cohesive zone models to capture bonding, debonding, and mixed-mode fracture at fiber–matrix and interlaminar interfaces.

• Damage and failure modeling: Creating a robust formulation to represent anisotropic damage evolution and softening in TPC laminates across multiple length scales.

• Integration of molecular and experimental insights: Using nano-scale and micro-scale data to inform the physics-based parameters of continuum models.

• Machine-learning surrogate modeling: Building data-driven models to efficiently map processing conditions to structural performance indicators.

• Optimization of processing parameters: Identifying processing windows that maximize interlaminar strength, toughness, and structural reliability.