Deep learning–based stress prediction for bottom-up SLA 3D printing process

Abstract

Additive manufacturing (AM) allows fabrication of complex geometric parts that are difficult to fabricate using a traditional subtractive manufacturing process. Stereolithography (SLA) printing is an AM technique that prints the 3D part from liquid resin based on the principle of photopolymerization. Part deformation and failure during the separation process are the key bottlenecks in printing high-quality parts using bottom-up SLA printing. Cohesive zone models have been successfully used to model the separation process in the bottom-up SLA printing process. However, the finite element (FE) simulation of the separation process is prohibitively computationally expensive and thus cannot be used for online monitoring of the SLA printing process. This paper outlines a deep learning (DL)–based framework to predict the stress distribution on the cured layer of the bottom-up SLA process–based printed part in real time. The framework consists of (1) a new 3D model database that captures a variety of geometric features that can be found in real 3D parts and (2) FE simulation on the 3D models present in the database that is used to create inputs and corresponding labels (outputs) to train the DL network. Two different types of DL networks were trained to predict the stress on the test dataset. Results further show that this framework drastically reduces computational time in comparison with FE simulations.