Using Taguchi method and gray relational analysis to enhance manufacturing performance in photopolymerization

Digital light processing (DLP) is the most widely used 3D molding technology in the medical industry. DLP machine developers aim at the interactive relationship between the forming separation force during layer-by-layer printing and mechanical printing parameters, which affect the dimensional accuracy of printed materials. Experiments were conducted using the Taguchi method to explore the correlation between four machine-printing parameters: slice thickness, geometric sharpness, exposure time, distance between the printed object and fixed end of the resin tank, and printing accuracy. The results indicated that the exposure time, the slice thickness, and the geometric shapes had the most significant impact on the dimensional accuracies along the X, Y, and Z axes, respectively, and the distance between the printed object and the fixed end of the resin tank had the most significant impact on the separation force generated during the printing process. Then, gray correlation analysis was used to propose the optimal printing parameters for the machine that balance the size accuracy of the printed object and the forming separation force. Machine-printing parameters are offered that can optimize the target characteristics. The Taguchi and the gray relational analysis strategy we adopted in this study can be extended to different DLP manufacturing applications to improve production efficiency.