A generalizable artificial intelligence tool for identification and correction of self-supporting structures in additive manufacturing processes

Abstract

High mix, low volume processes such as additive manufacturing (AM) offer tremendous promise for increasing the customization in manufacturing but are hindered by the lack of efficient methods for identifying process parameters for complex new geometries exhibiting the desired performance. The search over the process space can be automated with analysis tools that can be applied in a time and resource efficient manner such that ambitious print designs are not dissuaded by the cost of process parameter discovery. In this work, we propose an image analysis tool that can classify spanning prints as one of five process-relevant archetypes, invariant of the span dimensions. We describe a modular design of the tool such that simple adjustments to image processing parameters allow for compatibility with different print processes and environments. Furthermore, we demonstrate how this tool may be incorporated into a fully automated workflow on multiple AM systems to facilitate rapid autonomous process parameter discovery and/or deeper scientific understanding.