Statistical analysis of dimensional accuracy in additive manufacturing considering STL model properties

Abstract

Additive manufacturing (AM) is a technology that produces a part layer by layer based on the computer-aided designed (CAD) model. Each AM process is defined by a set of parameters and materials. The laser power, scan spacing and speed, preheating and bed temperatures, hatch length, pulse frequency, and part placement (coordinates of a part placed in the build) are among the most studied process parameters reported in the literature. Recent attention to improving part quality is caused by the possibility of using AM for manufacturing, but the inconsistency of results’ repeatability is the main challenge that is not solved yet. This work attempts to improve the dimensional accuracy by predicting dimensional features of the part, namely length, width, and thickness. Data is collected from two identical runs done on EOS P395 polymer laser sintering system. By identical runs is meant that build layout, material and process parameters were kept constant in both runs. Pearson correlation test is used to identify whether the new parameters (the number of mesh triangles, surface, and volume of CAD model) are significantly correlated to dimensional features. Based on the correlation results, linear regression models are developed to predict dimensional features (compensate shrinkage effect). The obtained results are the following: models for thickness (in XZY orientation), length (in ZYX orientation), and length and thickness (in Angle orientation) can already be used to predict dimensional features (to minimize shrinkage effect by proposing scaling ratio for each specimen in the build separately).