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Dataset in support of the journal article 'Acoustic and plasma sensing of laser ablation via deep learning'

Dataset in support of the journal article 'Acoustic and plasma sensing of laser ablation via deep learning'
Dataset in support of the journal article 'Acoustic and plasma sensing of laser ablation via deep learning'
This dataset contains: Figure_1.png Figure_2.png Figure_3.png Figure_4.png 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.
University of Southampton
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michael
1840a474-dd50-4a55-ab74-6f086aa3f701

Grant-Jacob, James, Mills, Benjamin and Zervas, Michael (2023) Dataset in support of the journal article 'Acoustic and plasma sensing of laser ablation via deep learning'. University of Southampton doi:10.5258/SOTON/D2609 [Dataset]

Record type: Dataset

Abstract

This dataset contains: Figure_1.png Figure_2.png Figure_3.png Figure_4.png 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.

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Published date: 2023

Identifiers

Local EPrints ID: 480958
URI: http://eprints.soton.ac.uk/id/eprint/480958
PURE UUID: 15570c6e-1247-4fe2-8f5f-9aa8a5a89e5d
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for Michael Zervas: ORCID iD orcid.org/0000-0002-0651-4059

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Date deposited: 11 Aug 2023 17:08
Last modified: 12 Aug 2023 01:41

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Contributors

Creator: James Grant-Jacob ORCID iD
Creator: Benjamin Mills ORCID iD
Creator: Michael Zervas ORCID iD

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