Application of Artificial Neural Network to Control Accuracy Dimension of 3D Resin DLP Products

Current devices using resin 3D printing technology are divided into three main lines, including: industrial, desktop and opensource. The use of desktop devices for producing end products is garnering significant attention thanks to several advantages. These include the capacity to utilize a variety of popular materials, affordable costs for both materials and components, and stable operation. However, some product quality categories such as surface properties, dimension and geometry accuracy, etc., of these devices depend greatly on the selection and adjustment of technological parameters. This article presents the process of building an Artificial Neural Network (ANN) model to control the dimension of test samples manufactured using Digital Light Processing (DLP) technology in three directions X, Y, Z. The testing process is designed according to the Face-centered central composite design (FCCCD) method with five input parameters. Test samples are chosen based on ASTM standards, produced using a desktop DLP resin 3D printer, and assessed with a Mitutoyo PJ-H3000F profile measuring instrument. Training results show that the ANN build model fits with the database and produces good predictions with the Mean Average Error (MAE) compared to actual data in the X direction being 0.038342, in the Y direction being 0.010258012, and in the Z direction being 0.012501036. The success of this research helps engineers and technicians reduce the time spent operating and calibrating related devices.