Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm

Abstract

Fused deposition modelling (FDM) is the most extended additive manufacturing technique up to date. In FDM, a thermoplastic material is extruded through a nozzle to form layers, and the final geometry is the result of consecutive superimposed layers. However, it is difficult to obtain an adequate dimensional accuracy for some applications due to the characteristics of the process. This paper proposes a method for increasing accuracy of the distance between parallel faces on FDM manufactured prismatic parts, consisting in replacing the theoretical values of CAD model dimensions by new optimized values. For this purpose, a model has been developed for predicting the dimensions of the manufactured parts, based on design characteristics. Particularly, this work has used an artificial neural network combined with an optimization algorithm, to determine the optimal dimensional values for the CAD model. Subsequently, CAD model is redesigned according to the dimensions provided by the optimization algorithm, and the part is manufactured. The results show that the application of this methodology allows for a reduction in manufacturing error of approximately 50 % for external dimensions and 30 % for internal dimensions.