Machine learning-enabled multiscale modeling platform for damage sensing digital twin in piezoelectric composite structures.

ABSTRACT: Nondestructive evaluation (NDE) of aerospace structures plays a crucial role in their successful operation under harsh environments. Most NDE methods, however, lack real-time in-situ predictive capabilities of evolving damage and are conducted in a post-mortem manner. This paper proposes a damage-sensing digital twin (DT) for piezoelectric composite structures incorporating microscale morphology and mechanisms into structural damage to address this shortcoming. The DT framework consists of a two-step computational process integrating multiscale-multiphysics modeling with machine learning (ML) tools to detect damage progression in the piezoelectric composite structure using electrical signal measurements at a few surface points. A parametrically upscaled coupled constitutive damage mechanic

SUBMITTER: Ghosh S 

PROVIDER: S-EPMC11850843 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature