SUMMARY

Laser powder bed fusion (LPBF) metal additive manufacturing allows a new paradigm for design creativity and supply chain logistics. There are many process variables that affect part quality in powder bed fusion including powder quality, layer height, laser speed and power, and hatch spacing. Spreading defects can lead to many process defects such as voids, energy density changes, and part topography variations. The overall goal of the present dissertation is to identify the critical size of notches on the recoater blade via investigation of part quality and powder bed topography. The first study is a combined simulation and experimental study investigating recoater damage and spreading defects in LPBF. In the experimental study, notches will be machined into the recoater to investigate the effect of spreading parameters on the powder bed via a laser line scanner. A simulation model via the discrete element method (DEM) will model the spreading conditions, resultant topography, and the distribution of particles. This investigation will aid in understanding the spreading behavior at the particle level. The second study investigates the ability of optical based methods to predict recoater damage from in-process signals. A range of computer vision and deep learning methods will be employed to study this behavior. Key features from the optical images will be used to identify height variations within the powder bed. The third study will investigate the effect of recoater defects on part quality characteristics of LPBF printed parts. Witness specimens will be analyzed via computed tomography and optical profilometry for their roughness and porosity content. This investigation will aid in determining the criticality of the size of the recoater damage and its influence on porosity distribution. Altogether, these studies will inform a comprehensive understanding of power spreading with a damaged recoater and its subsequent effects on powder bed and part level defects.