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

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

Description

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.

Files

Institutions

Texas A&M University Central Texas

Categories

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

Funding

U.S. National Science Foundation

ECCS 1953694

U.S. National Science Foundation

IIS 1849085

Licence