Model for Surface Finish Prediction and Optimization of Metal Additively Manufactured Parts

Global Engineering and Materials, Inc. (GEM) along with its team members, Northwestern University (NWU) and National Institute for Aviation Research (NIAR) at Wichita State University, propose developing a multiscale multi-physics modeling tool for rapid prediction of the surface finish of additively manufactured (AM) parts as a function of AM process parameters. A high-fidelity multi-physics toolkit will be constructed from our existing integrated module hub framework to simulate powder spreading and thermal-fluid flow, grain growth, voids formation, residual stress progression, and surface roughness. After verification and validation using AM fabricated Ti-6Al-4V coupons of two different geometric configurations under selected combination of AM setting parameters, a reduced order model using a machine learning strategy will be developed to explicitly establish a mapping relation between AM processing parameters and the resulting surface roughness. Capability demonstration of our reduced order model for roughness prediction will be performed by fabrication of AM coupons using optimized AM processing parameters. Optical microscopy and X-ray tomography will be used to fully capture the surface morphology for model validation and demonstration of improved surface finish of printed Ti-6Al-4V coupons.