Research Overview

CoCoMEng Lab focuses on advanced materials engineering and material failure modeling across multiple length scales. Our research aims to understand how materials deform, degrade, and fail by connecting processing, microstructure, defects, interfaces, and loading conditions with material performance.

Although the lab is rooted in composites and computational materials engineering, our work adopts a broader materials engineering perspective. We combine experiments, material characterization, manufacturing-process understanding, physics-based modeling, data-driven analysis, and engineering interpretation to address fundamental and applied problems in advanced materials.

Research Pillars

1. Material Failure Modeling

We develop models to understand and predict material failure mechanisms under mechanical, thermal, environmental, and manufacturing-induced conditions. This includes damage initiation, damage evolution, fracture, fatigue, degradation, and progressive failure of engineering materials.

Keywords:
failure modeling, damage mechanics, fracture, fatigue, degradation, durability, progressive failure

2. Multiscale Materials Engineering

We study material behavior across electronic, atomistic, nano, micro, meso, and continuum scales. The goal is to connect lower-scale mechanisms with engineering-scale material response and failure prediction.

Methods:
DFT, ReaxFF, molecular dynamics, micromechanics, homogenization, FE², continuum modeling, finite element analysis

3. Composites and Advanced Materials

We investigate composite materials and other advanced engineering materials by studying the relationship between constituents, interfaces, microstructure, defects, processing conditions, and mechanical performance.

Topics:
fiber-reinforced composites, polymer composites, thermoplastic composites, interface behavior, transverse cracking, fiber bridging, delamination, and composite degradation

4. Additive Manufacturing and Process-Induced Defects

We explore how manufacturing processes influence material behavior, especially in additively manufactured and architected materials. Research includes the effect of printing parameters, defects, voids, anisotropy, interlayer bonding, and geometry deviation on mechanical response and failure.

Topics:
3D-printed materials, sandwich composites, auxetic metastructures, crashbox structures, cellular materials, and process–property relationships

5. Physics-Based and Data-Driven Modeling

We combine physics-driven models with data-driven approaches to accelerate material analysis and failure prediction. Machine learning is used not as a replacement for mechanics, but as a tool to support faster prediction, surrogate modeling, automated data generation, and interpretation of complex material behavior.

Topics:
machine learning, surrogate models, stress-field prediction, automated simulation data generation, FEM–ML integration, physics-informed modeling