Accelerating Metal Extrusion Additive Manufacturing Process Development with Citrine Machine Learning Tools
Abstract/Summary
Material Extrusion (MEX) is a low-cost approach to metal additive manufacturing that involves extruding a metal-polymer composite filament to create a part comprised of polymer-bound metal powder, which is subsequently debound and sintered to produce a fully metal component. A major barrier to broader implementation of metal MEX is the time-intensive process for determining printing and sintering process parameters. To consistently produce sintered parts that satisfy geometric and materials properties requirements, trial-and-error experimentation is required to develop process parameters, correct shrinkage variability, and mitigate defects. This project aims to leverage Citrine Informatics’ machine learning (ML) tools to accelerate development of key aspects of the metal MEX process, including defect mitigation, shrinkage behavior, and sintering conditions, while still achieving materials properties that meet metal injection molding (MIM) standards. Successfully integrating ML with metallographic characterization and mechanical testing will enhance industrial viability of metal MEX by using predictive modeling to accelerate process parameter optimization, thereby reducing the number of printing and sintering hours required to produce a component that meets design requirements.
URL: https://digitalcommons.calpoly.edu/ceng_surp/97