SUMMARY

Material jetting-based (MJ) processes are promising additive manufacturing (AM) approaches often cited for their high resolution, speed, material flexibility and scalability. Such advantages make it uniquely suited to enable new innovation for a number of applications, such as paper machine press fabrics. To date, much of the related characterization has focused on material properties like strength, or macroscale geometric accuracy, with little investigation on mesoscale features of interest for press fabrics. Additionally, little process modeling exists due to the inherent complexity and multiscale nature of the process. Robust physics based modeling from individual drop through to final part is virtually impossible and has resulted in a gap between droplet scale high fidelity modeling and simplistic process planning models that cannot predict local feature errors found in mesoscale parts. To address these gaps, this research proposes to investigate mesoscale MJ fabrication through both characterization and predictive modeling, and in parallel, explore new press fabric designs. The intellectual merit of this research lies in improving our understanding of material jetting-based AM processes, then developing computational methods and tools which predict the behavior and performance of these processes. Additionally, contributions to the field of paper science are expected through the development of novel press fabric designs with improved dewatering capacity. https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTkyZjU1ZTMtYjE4MS00YTBjLWE3YjItZWVhNmQxZTVlYWNk%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%229df132f7-ae33-4882-99c2-9c104c63969f%22%7d