Optimization of DLP 3D printing parameters via the Taguchi method for improvement in dimensional and geometric accuracy

Purpose The quality of the final polymer part fabricated via digital light processing (DLP)-three-dimensional printing technology strongly depends on the process parameters selected. The purpose of this study is to optimize the process parameters (namely, layer thickness, cure depth and build orientation) that affect the dimensional and geometric accuracy of DLP-printed parts. Design/methodology/approach The experimental design of the Taguchi method was used to examine the effects of process parameters on the dimensional accuracy, such as length, width, height and diameter, and geometric accuracy, such as roundness and flatness of printed parts. Analysis of variance (ANOVA) and factor response tables were generated to determine the most significant parameters, and the optimal parameter combination was obtained via the CRITIC-TOPSIS method. Findings First, on the basis of the findings of the ANOVA and factor response table, layer thickness and build orientation were the most significant factors affecting accuracy, whereas cure depth was the least significant factor. Second, via the relative closeness values calculated using the CRITIC-TOPSIS method, the overall accuracy of the printed parts first increases and then decreases with increasing layer thickness and build orientation. Finally, the results of the confirmation test show that the optimized process parameters can improve the overall accuracy of DLP-printed parts. Originality/value This study focuses on both the dimensional and geometrical accuracy of DLP-printed parts and proposes the use of the CRITIC-TOPSIS method to optimize the process parameters to improve the quality of these parts. Unlike prior studies that rely on subjective weighting (e.g. grey relational analysis) or single-response optimization, the CRITIC-TOPSIS method eliminates subjectivity by deriving weights from data variability and intercriteria conflict and balances trade-offs between different accuracy metrics, thereby ensuring a comprehensive improvement in part quality.