Using image data for Quality Assurance in Additive Manufacturing

Additive Manufacturing (AM) processes currently suffer from high failure rates, which can amount to immense danger when the AM parts are incorporated in complex systems used by the public, i.e. cars, airplanes, or construction. Most current AM Quality Assurance (QA) methods are conducted post production, though ultrasonic testing may also be conducted while a part is being built. The focus of the project is to build a non-destructive post-production verification process that uses image data to render 3D models of the actual build, which will aid users in determining the quality of a part. The visualization process presented here will automate some labor intensive tasks and ultimately help users easily determine how print parameters affect the quality of a build by 1) collating and cleaning image data from actual builds, 2) storing data efficiently, and 3) creating visualizations of the images. A crucial outcome of this research is the ability to determine the quality of a final printed object without destroying it for QA inspection.