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

Published: 13 October 2023| Version 1 | DOI: 10.17632/bfnnn86hhn.1
Adithyaa Karthikeyan


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. - 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.



Texas A&M University Central Texas


Sensor Fusion, Porosity, Acoustic Emission, Directed Energy Deposition, Advanced Manufacturing, Explainable Artificial Intelligence


National Science Foundation

ECCS 1953694

National Science Foundation

IIS 1849085