Prediction of fatigue damage growth in notched composite laminates using an artificial neural network
Source abstract
Abstract
Models to predict the split growth in notched AS4/3501-6 graphite/epoxy quasi-isotropic laminates under tension-dominated fatigue are presented. First, a power law model and an artificial neural network (ANN) model are developed to describe the split growth under constant-amplitude fatigue. They are then applied in conjunction with a linear damage growth model to predict the split growth under spectrum fatigue. The ANN model is found to work better than the power law model as a predictive tool for split growth.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Mechanical Behavior of Composites · Fatigue and fracture mechanics · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (23)
Show source-provided references
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