Digitization of Process-Structure Optimization of Direct Ink Writing Additive Manufacturing System

Additive manufacturing overcomes limitations of conventional manufacturing ranging from geometric complexity to multi-material integration. This newer technology requires studies into how the process affects the structure and material properties of the finished part. Traditionally, this is done through experimental changing of process parameters and destructive testing of the manufactured material. This traditional process is time consuming and cost ineffective. One solution for this issue is a data driven approach for digitizing the manufacturing exploration process. This thesis introduces a framework for investigating the relationships between additive manufacturing process parameters and observed structures. A characterization method is presented followed by separate microstructure and mesostructure modeling methods. A traditional feature statistics approach is applied to the microstructure while a machine learning computer vision approach is applied to the mesostructure. The combination of these two approaches creates a workflow for fully understanding how the manufacturing process parameters affect the varying levels of material structure.