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Late News Poster Session: On-Demand Artificial Intelligence Poster Session
Program Organizers: MS&T Administration, MS&T PCC

Friday 8:00 AM
October 22, 2021
Room: On-Demand Poster Hall
Location: MS&T On Demand

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Poster
LSTM Model to Predict Low-cycle Fatigue in IN718: Jacob Keesler-Evans¹; Ansan Pokharel¹; Terence Musho¹; ¹West Virginia University
    Understanding low-cycle fatigue is critical to designing new alloys that can operate in extreme environments. The following study uses time history data collected from a creep-fatigue experiment at a range of temperatures to train a Long-Short Term Memory Neural Network (LSTM) to predict crack dynamics. This study focuses on a polycrystalline nickel-based superalloy, known as Inconel 718. The LSTM model is trained on data taken from a DCPD measurement at a high sampling rate. The model is trained on data from crack initiation up through stage two crack propagation. The LSTM model was able to reproduce creep-fatigue data for a continuous range of temperatures and stress intensities. The LSTM model provides a tool for bettering understanding crack initiation and propagation in the absence of experimental data. Moreover, the model can also provide better damage prediction provided loading and thermal history of parts.

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