| Issue |
MATEC Web Conf.
Volume 257, 2019
2018 International Conference on Progress in Mechanical and Aerospace Engineering (PMAE 2018)
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 4 | |
| Section | Materials Science and Mechanical Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201925702007 | |
| Published online | 16 January 2019 | |
A Novel Process Model of Ship Rust Removal by Premixed Abrasive Jet based on Neural Network
1
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
2
School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, China
In view of the technological requirements of the development of green shipbuilding technology on the effect of ship surface rust removal, the premixed abrasive jet technology is used to remove rust. Because the rust removal of ships with premixed abrasive jet is influenced by multiple parameters and has a high nonlinear relationship between various parameters, the accurate process model of it is difficult to establish. On the basis of artificial neural network modelling technology, the model of ship rust removal with premixed abrasive jet is built. The model takes the system pressure, the target distance, the moving speed of the spray gun and the particle size of the abrasive as input parameters, and the score which can most reflect the effect of the rust removal as output parameter. The test results show that the prediction error of the model is small, and it can better reflect the process rule between the effect of the premixed abrasive jet and the process parameters. We can guide the selection of process parameters according to the model.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.