Prediction of Geometry-Induced Porosity in Cold Spray Additive Manufacturing of Leading Edges

Abstract

In this study, a method is demonstrated for predicting defect-laden areas within deposits produced by cold spray additive manufacturing that are caused by substrate geometry and robot tool path. Leading edge shapes were used as the test bed for this method, and porosity was selected as the metric by which defects are quantified. A porosity model was developed based on the observation that porosity is significantly influenced by particle impact velocity normal to the surface and is therefore highly correlated to particle impact angle. The model outputs deposit shape and a probability map of porosity based on initial substrate geometry and robot tool path as inputs. The model was calibrated by experimental deposits formed at varying impact angles. To validate the method, the model was applied to three airfoil leading edge geometries and was shown to qualitatively estimate the density of pores in the deposit. The study also revealed that robotics alignment is an important factor in causing defects within highly curved geometries. The mathematical construct of the model is applicable to other applications outside of leading edges and deposit properties beyond porosity, but other applications and properties are not evaluated in this study.