Process Parameters Optimization to Improve Dimensional Accuracy of Stereolithography Parts

Stereolithography process limits wider applications due to low dimensional accuracy comparing with cnc process. hence, to improve accuracy and reduce part distortion, understanding the physics involved in the relationship between the setup input parameters and the part dimensional accuracy is prerequisite. in this paper, a model is proposed to find and optimize important parameters to achieve higher accuracy and also predict dimensional accuracy using various parameters values. for this purpose, the result of a previous study is used, where it is found that in stereolithography process these factors in order of importance with respect to dimensional accuracy are: layer thickness, hatch style, hatch spacing, hatch fill cure depth and hatch overcure. moreover, in this research the proposed neural network model is able to predict dimensional accuracy with 6% error.