Natural Frequency prediction of FDM manufactured parts using ANN approach

This research study demonstrates the use of machine learning tools for the prediction of dynamic mechanical characteristics of parts produced by the Fused Deposition Modeling (FDM) process. In this regard, I-optimal design of experiments was followed with raster angle, air gap, build orientation and number of contours as independent variables together with natural frequency as the mechanical part characteristic for investigation. Accordingly, a Artificial Neural Network (ANN) model was trained using the Bayesian regularization function. Finally, the trained ANN model was validated by performing multiple confirmation runs which provided predictions generally within 5% accuracy.