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.ML-AVFP（机器学习自适应粘性丝材打印，Machine Learning Adaptive Viscous Filament Printing）G代码优化器是一款基于MATLAB开发的综合性软件系统，专为直接墨水书写（Direct Ink Writing，DIW）及其他粘性材料挤出成型工艺优化打印参数而设计。该系统将机器学习算法与实测打印数据相结合，可动态调整G代码指令，从而提升打印成功率、尺寸精度与材料利用效率。 这款科研软件针对粘性材料打印中参数优化的重大难题展开研发——传统试错法不仅耗时冗长，且材料消耗巨大。该应用基于实验打印数据训练了四种机器学习模型，分别为神经网络（Neural Network）、随机森林（Random Forest）、梯度提升（Gradient Boosting）与集成模型（Ensemble），用于预测六项核心参数的最优调整方案，包括Z轴高度、打印速度、挤出流量、层厚、打印质量预测以及材料节约量。 该系统配备了界面友好的图形化交互界面，包含五个分析标签页，可服务于材料科学、生物医学工程以及增材制造领域的科研人员与行业从业者。系统内置了涵盖七种预表征粘性材料的综合性材料数据库（包括水凝胶、弹性体以及陶瓷基复合材料），并搭载了智能回退机制以保障运行的鲁棒性。