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

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 pro-cess is sensitive to changes in printing parameters(e.g.,voltage,printing pressure,and temperature),which can causefluid column breakage,jet lag,and/orfiber 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 graph-ical user interface(GUI).We trainedfive 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).