Optimization of 3D Printing Parameters on Deformation by BP Neural Network Algorithm

Abstract

Traditional processing technology is not suitable for the requirements of advanced manufacturing due to the disadvantages of large repeated experiments, high cost, and low economic effect. As the latest additive technology, 3D printing technology has to deal with many issues such as process parameters and nonlinear mathematical models. A three-layer backpropagation (BP) artificial neural network with a Lavenberg–Marquardt algorithm was established to train the network and predict orthogonal experimental data. Additionally, the best combination of parameters of material deformations were predicted and verified by experiments. The results show that the predicted value obtained by the BP model is in good agreement with the experimental value curve, with a small relative error and a correlation coefficient of 0.99985. Moreover, the deformation errors of the printed model are not more than 3%. The incorporation of the BP neural network algorithm into the 3D printing process can, therefore, help cope with related problems, which is a future trend.

Let’s Print an Ecology in 3D (and 4D)

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Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm

“…where x n is the standardized data, x is the experimental data or the data after de-standardization; and x min and x max are the minimum and maximum values of the experiment, respectively [27]. The parameters of this BP neural network model are set as follows: trainlm of the Levenberg-Marquardt (L-M) optimization algorithm is chosen as the training function, the output layer neuron function uses purelin, the maximum training times is 1,000, the training target is 0.0001, and the learning rate is 0.01.…”

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

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