Design of grayscale digital light processing 3D printing block by machine learning and evolutionary algorithm

Abstract

Grayscale digital light processing (DLP) 3D printing method, using grayscale light patterns to adjust the microscale material properties, is a revolutionary technology for future advanced manufacturing. However, the design and optimization method of the grayscale distribution remains elusive. In this paper, we propose an optimization method for grayscale DLP printed rectangular blocks based on a machine learning and evolutionary algorithm. We use an automated finite element model-based evaluation to predict the deformation shapes with arbitrary grayscale distribution, considering the nonlinear mechanical performances. A machine learning model based on recurrent neural networks is trained using the database formed by the finite element method model to enable fast and precise prediction of the deformed shapes. Optimal designs of the deformed shapes are efficiently realized via integrating the machine learning model and evolution algorithm method. Grayscale distributions are optimized to form desired deformations and validated by experiments. This work paves the way for designing and optimizing grayscale digital light processing 3D printed structures.