Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Abstract Quality control and repeatability of 3D printing must be enhanced to fully unlock its utility beyond prototyping and noncritical applications. Machine learning is a potential solution to improving 3D printing performance and is explored for areas including flaw identification and property prediction. However, critical problems must be resolved before machine learning can truly enable 3D printing to reach its potential, including the very large data sets required for training and the inherently local nature of 3D printing where the optimum parameter settings vary throughout the part. This work outlines an end‐to‐end tool for integrating machine learning into the 3D printing process. The tool selects the ideal parameter settings at each location, taking into consideration factors such as geometry, hardware and material response times, and operator priorities. The tool demonstrates its usefulness by correcting for visual flaws common in fused filament fabrication parts. An image recognition neural network classifies local flaws in parts to create training data. A gradient boosting classifier then predicts the local flaws in future parts, based on location, geometry, and parameter settings. The tool selects optimum parameter settings based on the aforementioned factors. The resulting prints show increased quality over prints that use global parameters only.