READ ME File For 'Dataset for Live imaging of laser machining via plasma deep learning'

Dataset DOI: https://doi.org/10.5258/SOTON/D2764

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: Live imaging of laser machining via plasma deep learning 
JOURNAL: Optics Express
PAPER DOI IF KNOWN:  https://doi.org/10.1364/OE.507708


This dataset contains:

Picture1.jpg 
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Data2.txt
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Picture6.png
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The figures are as follows:


Picture1.jpg Fig. 1. Schematic of the experimental setup along with an example set of experimental plasma images and associated experimental images of the laser machined sample before and after the laser pulse. For this work, the two neural networks were run in real-time, hence providing a live image of the sample during machining.

Picture2.jpg Fig. 2. (a) Schematic of the U-net architecture used for the generator for both neural network models used in this work. Loss for the generator for predicting the (b) before and (c) after images during the training process. There were 2000 iterations per epoch. An example of (d) plasma and corresponding experimental and predicted images before and after ablation for 100, 150, 200 and 250 epochs, with the average of all test data L1 losses labelled on the images.

Data_2.txt Fig. 2. L1 data for average test before and after ablation.


Picture3.png Fig. 3. A single example of a real-time prediction, with a comparison to the associated experimental result, shown as a process flowchart. Neural network 1 predicts the appearance of the sample before the laser pulse, and neural network 2 predicts the appearance of the sample after the laser pulse.

Picture4.jpg Fig. 4. Ten sequential laser pulses and the associated experimental and generated before and after images of the sample, with and without masking of the region corresponding the spatial extent of the laser pulse. Pulse 10 in this figure was used for the Fig. 2 schematic.

Picture5.jpg Fig. 5. One hundred examples of experimental plasma images, taken from sequential laser pulses, with the pulse number and scale bar included in each image.

Picture6.png Fig. 6. Average absolute difference between (a) E1 and P1, (b) E2 and P2, (c) E1 and E2, and (d) P1 and P2 (where E1 = experimental before, E2 = experimental after, P1 = predicted before, P2 = predicted after). The figure therefore shows the prediction error for (a) before and (b) after the laser pulse, and (c) the real change and (d) the predicted change in the sample appearance due to the laser pulse. Also show for reference are the average images for (e) E1, (f) E2, (g) P1, and (h) P2. The sets of figures are shown using the same colour scale to assist in comparison.

Picture7.png Fig 7. Comparison of neural network capability in predicting the after image via a direct and indirect route. Showing (a) a flowchart describing the direct and indirect prediction route, the average images for (b) plasma, (c) direct after prediction, (d) before prediction and (e) indirect after prediction, and prediction errors for the (f) direct and (g) indirect routes.



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