Identifying the Most Effective Data Processing for Fatigue Delamination Growth in FRPs: Insights on Artificial Data Simulation

This work investigates the fatigue induced delamination growth in CFRP considering different fibre orientation combinations. The study explores the application of Artificial Neural Networks (ANN) in the estimation of fatigue delamination behaviour in order to reduce the number of experimental tests required for certification. The research evaluates the effectiveness of ANN at different stages of data processing, including raw data simulation and final curve estimation. The results show that applying ANN at the raw data stage provides flexibility in modelling with an error < 10%.

This publication provides the data set for quasi-static delamination (pre-cracking) and under Mode I fatigue delamination, including raw data, crack length images and post-processed data, and the scripts for data processing and ANN modelling. For each data we provide the CHADA to document the material characterisation.

The research was funded by the European Union under GA No. 101091409 (D-STANDART). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.