Equivalent Morphology Concept in Composite Materials Using Machine Learning and Genetic Algorithm Coupling

Abstract

The objective of this study is to investigate the synergistic integration of machine learning and evolutionary algorithms for the discovery of equivalent morphologies exhibiting analogous behavior within the domain of composite materials. To pursue this objective, two comprehensive databases are meticulously constructed. The first database encompasses randomly positioned inclusions characterized by varying volume fractions and contrast levels. Conversely, the second database comprises microstructures of diverse shapes, such as elliptical, square, and triangular, while maintaining consistent volume fraction and contrast values across samples. Label assignment for both databases is conducted using a finite-element-method-based computational tool, ensuring a standardized approach. Machine learning techniques are then applied, employing distinct methodologies tailored to the complexity of each database. Specifically, an artificial neural network ANN model is deployed for the first database due to its intricate parameter configurations, while an eXtreme Gradient Boosting XGBoost model is employed for the second database. Subsequently, these developed models are seamlessly integrated with a genetic algorithm, which operates to identify equivalent morphologies with nuanced variations in geometry, volume fraction, and contrast. In summation, the findings of this investigation exhibit notable levels of adaptation within the discovered equivalent morphologies, underscoring the efficacy of the integrated machine learning and evolutionary algorithm framework in facilitating the optimization of composite material design for desired behavioral outcomes.