Estimation of surface roughness for digital light processing based additively manufactured parts

Additive manufacturing (AM), commonly known as 3D printing, fabricates objects by sequentially layering material and fusing each layer until the object is complete. Among the diverse AM technologies, digital light processing (DLP) stands out for its high precision and rapid printing speed. Nevertheless, considerable research has scrutinized the surface roughness of DLP-printed components, akin to stereolithography (SLA) printing, with primary efforts directed towards optimizing process parameters and perimeter geometry to mitigate the "staircase effect." To address the inherent variability in surface roughness values, both theoretical and empirical approaches have been employed to assess and predict the roughness profiles of DLP-printed parts. This study's primary objective is to enhance the understanding of surface roughness in DLP 3D printing, reduce unpredictability, and minimize estimation errors through the development of accurate mathematical models. These models aim to refine the optimization of process parameters and perimeter geometry, thereby improving the overall quality of printed components. This research contributes novel mathematical models for predicting surface roughness in DLP 3D printing, filling a significant gap in the existing literature. By addressing the inherent unpredictability in roughness values, the study offers practical solutions for process optimization and quality enhancement. The systematic approach combines theoretical analysis with empirical studies, leveraging experimental data and literature review to develop comprehensive mathematical models. These models cover various build orientations and material properties, providing improved accuracy in predicting surface roughness profiles compared to current methods. Testing across different build orientations validates the proposed models' efficacy. Through the proposed model, surface roughness value is successfully estimated with an accuracy of 92.87% for surface angle 0°–5°, with an accuracy of 91.6% for surface angle 10°–60° and with an accuracy of 90.16% for the surface angle of 60°–90°. The findings offer valuable insights for optimizing DLP 3D printing processes, and enhancing the overall quality of printed parts, thereby advancing the field of additive manufacturing.