├── FCGR_Test.xlsx ├── FCGR_Train.xlsx └── README.md /FCGR_Test.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nithink27/FCGR-Predictions-using-Machine-Learning/HEAD/FCGR_Test.xlsx -------------------------------------------------------------------------------- /FCGR_Train.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nithink27/FCGR-Predictions-using-Machine-Learning/HEAD/FCGR_Train.xlsx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FCGR-Predictions-using-Machine-Learning 2 | Fatigue Crack Growth Rate Predictions of Ti6Al4V which is processed using LPBF and post Processed. 3 | The data is collected from the Literature (Cain et al) where variables are Post Processing Technique and Built Orientation. 4 | Data is taken from the FCGR graphs, which are plotted after conducting the experiments. 5 | Making this data into Regression Problem, trying to predict the FCGR with 3 variables which are Stress Intensity factor, Post Processing technique, Built Orientation. 6 | Label Encoded the Categorical columns and Normalized the Numerical columns for faster convergence. 7 | Used 4 ML Algorithms and compared R2 scores 8 | Used the better performing algorithm for Hyper Parameter Tuning 9 | Used the best Hyperparameters on the better performing algorithm for Feature Importance. 10 | --------------------------------------------------------------------------------