About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Machine Learning Based Prediction of Corrosion Behavior in Additively Manufactured Titanium Alloy |
Author(s) |
Nithin Konda, Mythreyi OV, Jayaganthan R |
On-Site Speaker (Planned) |
Nithin Konda |
Abstract Scope |
In the present work, machine learning-based corrosion behavior modeling has been carried out in Laser
powder bed fused titanium alloy. Using optimized process parameters, titanium alloy samples were
produced using the LPBF technique and subjected to heat treatment and shot peening. Potentiodynamic
polarization and electrochemical impedance tests were conducted at room temperature in an alkaline
environment. The collected experimental data was then utilized for predictive model development using
8 different machine learning algorithms. The model performances were compared using the standard
metrics such as RMSE, MSE, and R2. The feature importance analysis was carried out to assess the
magnitude of effect each of the post-processing parameters had on corrosion performance. This work
enhances the understanding between post-processing parameters and the degradation behavior of
titanium alloy using a machine learning approach. |
Proceedings Inclusion? |
Undecided |