MATEC Web Conf.
Volume 178, 201822nd International Conference on Innovative Manufacturing Engineering and Energy - IManE&E 2018
|Number of page(s)||6|
|Section||Mechanical and Manufacturing Equipment Devices and Instrumentation|
|Published online||24 July 2018|
Prediction of thermal field dynamics of mould in casting using artificial neural networks
Dunărea de Jos University of Galaţi, Department of Manufacturing Engineering, 111 Domnească Street, Galaţi, Romania
* Corresponding author: firstname.lastname@example.org
Manufacturing a large number of cast parts made of aluminium alloy led to an increased interest in developing and applying new control techniques of the casting process. Anyway, the difficulty in estimating some important process parameters only allowed the use of some approaches which are limited to a few geometric models. Many researchers made great efforts to find the best method for monitoring and measuring thermal field dynamics of the cast and mould during solidification and cooling of the melt alloy. Acquiring very accurate data leads to best approach for solving the heat transfer problem in casting. The paper presents the prediction of thermal field dynamics of mould in permanent mould casting using artificial neural networks and based on thermal history of the cast part and the way this thermal history influences the thermal changes of the mould. It is very important to identify the relation between the thermal fields' dynamics of both cast and mould in order to create and use a control technique of the cast solidification and cooling. The necessity of controlling the cast solidification is due to the large demand of cast parts with improved mechanical properties.
© The Authors, published by EDP Sciences, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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.