Friction stir welding of IS:65032 aluminum alloy and predicting tensile strength using Ensemble Learning
Developed by @UpenderKaveti
Joining two metals play a significant role in the automobile and aerospace industries and it is a challenging job. To overcome the challenges, Friction Stir Welding (FSW) is considered as the solid-state joining technique in the industries as it is a new and unique way. As the field of machine learning is extending its applicability to different fields, it can even be applied in the field of welding. The objective of this repository is to create machine learning regression models to predict the tensile strength of IS:65032 aluminum alloy by taking rotational speed, welding speed, tool tilt angle, and tool pin shoulder diameter as the input parameters. In this work, the ensemble learning approach is adopted to develop models as it uses the wisdom of many learning algorithms to achieve better performance by filling in the gaps of learning ability.
- Colab
- Numpy
- Pandas
- Seaborn
- Matplotlib
- Scikit-learn
- SHAP (SHapley Additive exPlanations)
- Gradio
Data was generated by performing FSW experimentation in DRML.
$ !pip install shap
$ !pip install gradio
