MATEC Web Conf.
Volume 178, 201822nd International Conference on Innovative Manufacturing Engineering and Energy - IManE&E 2018
|Number of page(s)||6|
|Section||Assembling Technologies, Forming Technologies, Additive Manufacturing|
|Published online||24 July 2018|
The neural networks used in FDM printing study
Technical University Gheorghe Asachi of Iasi, Department of Machining Tool, Romania
* Corresponding author: email@example.com
Neural networks have aroused a lively interest since 1943 when Warren McCulloch and Walter Pitts proposed a neural network model (a single layer model), that has remained fundamentally structural even today for most neural networks. Problem solving and implicit the study of a system's operating model such as 3D printing involves the association between input data, hypotheses and output data, and neural networks provide the ability to form their own model of solving. The main difference between neural networks and other information processing systems is the ability to learn from interacting with the environment and so improving performance. A correct representation of information, allowing interpretation, prediction, and response to an external stimulus, can allow the network to build a model of the considered process, in the paper case fused deposition modelling (FDM) process.
© 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/).
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