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Multi-Fidelity ML Based Approach to Predict Local Material Response
High strain dynamic response of materials is of key importance for material development. An
attempt was made to study the local material behavior of PMMA (polymethyl methacrylate) using
a flat nail impact. The developed experiment aims to study the local material behavior by
measuring the local strain and deformation response, and bulk load response. The material
behavior is studied, and the prediction is made about the local material response with machine
learning and predictive modeling techniques using training data set from the developed microscale nail impact experiment. PMMA samples were impacted with flat nails at high velocities
using an in-house built gas gun. Sensors (force sensitivity resistors) are used to measure the
material load response of examined samples. Digital image correlation (DIC) was used with a
high-speed camera to record the local surface deformation and strains.
The aim of this work holds to understand the wave propagation in the material and to predict the
material behavior of the sample under the point of impact using the sensor data. Experiments and
its data processing here were expensive in terms of time and complexity and hence concurrently,
a FEM simulation was developed to mimic the experiment. A large amount of FEM data was
generated to create the dataset for a low-fidelity model. The experiment (and simulation)
parameters and the sensor data (force profile on the back surface of the sample in case of the
simulation) are used to build the training data. The model predicts the strain profile (DIC data) of
the sample at the point just below the impact. Composite neural-net architecture is implemented
to model to multi-fidelity approach and comparisons were made between high-fidelity and multi-fidelity prediction.
History
Degree Type
- Master of Science in Aeronautics and Astronautics
Department
- Aeronautics and Astronautics
Campus location
- West Lafayette
