Using machine learning to predict the maximum erosion rate
and incubation period of alloys used in aerospace like Ti64,12%Cr SS, Al2024, Al7075, TiAl,
etc., underwater droplet impacts from different rigs that are available in works
of literature. The output from different rigs can give different output
values for the same input values because of the difference in rig geometry and experimental
conditions. Need to use machine learning to overcome this problem and also use
techniques to overcome the small data set problems like data augmentation to
improve model accuracy and also after this validation of the final model.
We need to use different algorithms like Linear regression, Decision tree-based
regression, and Neural network-based model and make comparisons of the model
and which one outperforms and validated.
Using machine learning to predict the maximum erosion rate and incubation period of alloys used in aerospace like Ti64,12%Cr SS, Al2024, Al7075, TiAl, etc., underwater droplet impacts from different rigs that are available in works of literature
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