The neural networks used in FDM printing study

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.

Abstract licence: CC BY

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