Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes

Abstract

Additive manufacturing (AM) processes involve the fabrication of parts in a layer-wise manner. The layers of material are deposited using a variety of established methodologies, the most popular of which involve either the use of a powerful laser to sinter/melt successive layers of metal/alloy/polymer powders or, the deposition of layers of polymers through a heated extrusion head at a controlled rate. The thermal nature of these processes causes the development of temperature gradients throughout the part and as a result, the part undergoes irregular deformations which ultimately leads to dimensional inaccuracies in the manufactured part. An Artificial Neural Network (ANN) based methodology is presented in this paper to directly compensate the part geometric design which will help to counter the thermal deformations in the manufactured part. A feed-forward ANN model is trained using backpropagation algorithm to study part deformations resulting from the AM process. The trained network is subsequently employed on the part Stereolithography (STL) file to effect the required geometrical corrections. Two examples are presented to evaluate the performance of the proposed compensation methodology. A novel approach to evaluate the conformity of the final part profile to the original part CAD profile has also been developed to quantify the performance of the proposed methodology. The results of the examples show substantial improvement in the part accuracy and thus validate the ANN based geometric compensation approach.