loading page

A Machine Learning Model for Predicting Progressive Crack Extension based on Direct Current Potential Drop Fatigue Data
  • +1
  • Jacob Keesler-Evans,
  • Ansan Pokharel,
  • Robert Tempke,
  • Terence Musho
Jacob Keesler-Evans
West Virginia University

Corresponding Author:[email protected]

Author Profile
Ansan Pokharel
West Virginia University
Author Profile
Robert Tempke
West Virginia University
Author Profile
Terence Musho
West Virginia University
Author Profile

Abstract

Time history data collected from a Direct Current Potential Drop (DCPD) fatigue experiment at a range of temperatures was used to train a Bidirectional Long-Short Term Memory Neural Network (BiLSTM) model. The model was trained on high sampling rate experimental data from crack initiation up through the Paris regime. The BiLSTM model was able to predict the progressive crack extension at intermediate temperatures and stress intensities. The model was able to reproduce crack jumps and overall crack progression. The BiLSTM model demonstrated the potential to be used as a tool for future investigation into fundamental mechanisms such as high-temperature oxidation and new damage models.