Adaptive Direct Ink Writing via a Hybrid Physics–Machine Learning Framework with G-Code Optimizer

The ML-AVFP (Machine Learning Adaptive Viscous Filament Printing) G-code Optimizer is a comprehensive MATLAB-based software system designed to optimize printing parameters for Direct Ink Writing (DIW) and other viscous material extrusion processes. The system integrates machine learning algorithms with empirical printing data to dynamically adjust G-code commands, improving print success rates, dimensional accuracy, and material efficiency.

This research software addresses the significant challenge of parameter optimization in viscous material printing, where traditional trial-and-error approaches are time-consuming and material-intensive. The application implements four machine learning models (Neural Network, Random Forest, Gradient Boosting, Ensemble) trained on experimental printing data to predict optimal adjustments for six key parameters: Z-height, print speed, flow rate, layer height, quality prediction, and material saving.

The system features a user-friendly graphical interface with five analysis tabs, supporting researchers and practitioners in material science, biomedical engineering, and additive manufacturing. It includes a comprehensive material database with seven pre-characterized viscous materials (including hydrogels, elastomers, and ceramic composites) and implements intelligent fallback mechanisms for robust operation.