Quantum mechanics

Composite number

Engineering

Experimental data

Statistics

Materials science

Displacement (psychology)

Damage mechanics

Ultimate tensile strength

Computer science

Physics

Finite element method

Nanotechnology

Psychology

Mathematics

Characterization (materials science)

Psychotherapist

Isotropy

Artificial intelligence

Composite material

Structural engineering

Tension (geology)

Artificial neural network

Composite laminates

Composite Structures

A novel approach for damage characterization through machine learning is presented where theoretical knowledge of failure and strain-softening is linked to the macroscopic response of quasi-isotropic composite laminates in over-height compact tension tests. A highly efficient continuum damage finite element model enables the training of a system of interconnected Neural Networks (NNs) in series solely based on the macroscopic load-displacement data. Using experimental results, the trained NNs predict suitable damage parameters for progressive damage modeling of IM7/8552 composite laminates. The predicted damage properties are validated successfully using experimental measurements obtained through cumbersome non-destructive data analysis. The proposed strategy demonstrates the effectiveness of machine learning to reduce experimental efforts for damage characterization in composites.

Theory-guided machine learning for damage characterization of composites