Abstract: A machine learning (ML) approach enables real-time direct ink write (DIW) print-parameter optimization through in-situ monitoring of printed line geometry. The method can use an invertible neural network (INN) to solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and INNs, DIW printing parameters can be autonomously optimized to print a target line width in a matter of seconds. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The method eliminates user-intensive, time-consuming, iterative parameter discovery approaches that currently limit accelerated implementation of DIW and other extrusion-based additive manufacturing processes.

Abstract: A novel method can determine the mechanical properties of additively manufactured structures using artificial neural network and computer vision models. Using this methodology, simulation times can be dramatically reduced, allowing for the implementation of a genetic algorithm which can determine the optimal AM parameters to achieve a targeted mechanical response.