Machine learning-enabled optimization of melt electro-writing three-dimensional printing

Abstract

Melt electrowriting (MEW) is a solvent-free (i.e., no volatile chemicals), a high-resolution three-dimensional (3D) printing method that enables the fabrication of semi-flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error-and-trial method to determine the most suitable parameters, here, we present a machine learning (ML)-enabled image analysis-based method for determining the optimum MEW printing parameters through an easy-to-use graphical user interface (GUI). We trained five different ML algorithms using 168 MEW 3D print samples, among which the Gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with a control feedback loop and MEW can reduce the error-and-trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost-effectiveness of MEW). Moreover, embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML-based optimization techniques in the industrial section (i.e., for users with no ML skills).