READ ME File For 'Deep learning for the monitoring and process control of femtosecond laser machining'

Dataset DOI: 10.5258/SOTON/D0754

ReadMe Author: Yunhui Xie [0000-0002-8841-7235], University of Southampton

This dataset supports the publication:
XIE, Y., HEATH, D. J., GRANT-JACOB, J. A., MACKAY, B. S., MCDONNELL, M. D. T., PRAEGER, M., EASON, R. W. AND MILLS, B.
Deep learning for the monitoring and process control of femtosecond laser machining
Journal of Physics: Photonics, 1(3), p.035002.
https://doi.org/10.1088/2515-7647/ab281a


This dataset contains:

Table_1.xlsx spports data for Fig. 4, Fig. 5 and Fig. 6.

The figures are as follows:

Fig. 1  the schematic of setup for real-time closed loop feedback.
Fig. 2  the schematic of the structure of the convolutional neural network used.
Fig. 3  the schematic of how data augmentation operators.
Fig. 4  CNN's performance under different levels of data augmentation on single axis.
Fig. 5  CNN's performance on beam translations and rotation.
Fig. 6  Comparison between CNN's predicted pulses left and a naive gauss based on mean of pulse used.

Date of data collection: 07/08/2018 ~ 16/11/2018

Information about geographic location of data collection: University of Southampton, U.K.

Licence: CC BY

Related projects:
Mills, B., Heath, D., Grant-Jacob, J., Xie, Y. and Eason, R. (2018). Image-based monitoring of femtosecond laser machining via a neural network. Journal of Physics: Photonics, 1(1), p.015008.


Date that the file was created: 07/2019