READ ME File For 'Dataset for Acoustic and plasma sensing of laser ablation via deep learning'

Dataset DOI: https://doi.org/10.5258/SOTON/D2609

ReadMe Author: James A Grant-Jacob  University of Southampton https://orcid.org/0000-0002-4270-4247

This dataset supports the publication:
AUTHORS: James A. Grant-Jacob, Michalis N. Zervas and Ben Mills
TITLE: Acoustic and plasma sensing of laser ablation via deep learning 
JOURNAL: Optics Express
PAPER DOI IF KNOWN: 


This dataset contains:

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Figure_4.txt
Figure_5.png
Figure_6.png

The figures are as follows:

Figure_1.png  Schematic of the experimental setup and corresponding examples of experimentally collected images and acoustic spectra occurring during the time period when a single laser pulse is incident on the target sample.

Figure_2.png Concept diagram of the application of the four neural networks used in this work, showing the use of plasma images and acoustic spectra for predicting the laser pulse energy and for predictive visualization of the appearance of the laser ablated samples.

Figure_3.png Schematic of the architectures for the (a) CNN and (b) cGAN used for this work.

Figure_4.png Actual and predicted pulse energy for test images associated with (a) plasma images and (b) acoustic spectra. 

Figure_4.txt Data for Actual and predicted pulse energy for test images associated with (a) plasma images and (b) acoustic spectra. 

Figure_5.png Examples of activation intensity for (a) plasma images and (b) acoustic spectra sent through their respective neural network dropout layer. 

Figure_6.png Generated images and actual images of laser ablated surface for (a) plasma images and (b) acoustic spectra from different laser pulse energies. 



Licence: CC-BY

Related projects:
EPSRC grant EP/P027644/1
EPSRC grant EP/T026197/1
EPSRC grant EP/W028786/1

Date that the file was created: 07, 2023