About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Prediction of Stress-Strain Curve of High-Entropy Alloys and 3D Printed Steels Using Machine Learning |
Author(s) |
Shalini Priya, Nitish Bibhanshu |
On-Site Speaker (Planned) |
Shalini Priya |
Abstract Scope |
Predicting stress-strain curves is a challenging problem in material science due to non-linear properties of materials. Recently, artificial intelligence based models are found efficient for modelling such relationships and represent important material characteristics. In this work, we present a deep neural network (DNN) approach for strain-strain curve prediction. The stress-strain curves of high entropy alloys as well as of the steel were selected for the different range of temperatures. However, it’s been observed that any specific model is not able to sufficiently capture the properties across the materials hence we present an ensemble based technique of different DNN’s with its variations in parameters for fine-tuning and get the best prediction curve with minimal error. We compare our proposed model performance with standard machine learning and neural network predicted curves as well as with standard state-of-the-art techniques. We further compare our ensemble model with experimental data generated by the machine. |
Proceedings Inclusion? |
Undecided |