A Machine Learning Approach for Real-Time Control of Extrusion Additive Manufacturing

Abstract

Material extrusion 3D printing has enabled an elegant fabrication pathway for a vast material library.  Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing.  To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization.  In this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry.  To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network.  By combining in-situ computer vision and deep INNs, the 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 methods developed and presented in this paper eliminate time-consuming, iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based additive manufacturing processes.

Keywords: Machine learning, inverse neural network, Predictive modelling, Computer vision, Additive Manufacturing