In-situ surface porosity prediction in DED process using explainable multimodal sensor fusion

This repository maintains data associated with the models used in "In-situ surface porosity prediction in DED (directed energy deposition) printed SS316L parts using multimodal sensor fusion".

The repository contains 4 documents.

1. Surfels.xlsx - Contains the ground truth data (area percentage of porosity, as obtained from ImageJ software) for all surface elements ("surfels"). Surfels with percentage area of pores < 1% are classified as non-porous and those with area percentages > 2% are classified as porous.

2. Tensor_data_72_surfels.zip - Contains the spectrogram data from Accelerometer and Acoustic Emission signals pertaining to printing and milling tracks for all 72 surface elements in a zip file.

3. CNN Model Architecture and Predictions.ipynb - The CNN architectures for the various models developed including k-fold validation.

4. LIME_Explanations_Porosity_Predictions.ipynb - Python code for explaining the CNN model predictions using LIME.