Bead geometry prediction using multiple linear regression analysis

Additive manufacturing processes are currently studied in many research areas. Indeed, to qualify additively manufactured metallic parts, it is important to understand how process and postprocessing treatments influence the microstructure and mechanical properties of the parts. However, it is also essential to understand how the process parameters impact bead geometry. In this paper, various first-order parameters were studied, and beads were measured to identify correlations between input parameters (laser power P, scanning speed V and powder feed rate F) and geometrical outputs of Ti-6Al-4V beads (height, dilution, width) manufactured with the laser metal powder deposition process. Several results of linear or multiple linear regressions were analyzed to quantify the quality of the prediction of bead geometry. The results show that the emerged and diluted heights are correlated with the laser power/powder feed rate ratio (LEPF), energy density (ED) and powder density (PD). Additionally, the dilution rate seems to be better predicted when expressed as a function of P, V and F. Bead width regressions gave accurate predictions with low errors. These equations could be used to predict the bead geometry from input parameters or to predict input parameters as a function of the needed geometric properties.