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

Peer review status:UNDER REVIEW

17 Nov 2021Submitted to Fatigue & Fracture of Engineering Materials & Structures
17 Nov 2021Assigned to Editor
17 Nov 2021Submission Checks Completed
01 Dec 2021Reviewer(s) Assigned