With the increasing demand for precise control in 3D concrete printing, coordinating material rheological properties and printing parameters has become a critical challenge. This paper addresses how to intelligently optimize printing parameters to adapt to varying concrete material attributes and improve printing quality. A dual-path framework co-driven by physical information equations (PIE) and machine learning (ML) is proposed. PIE is embedded into convolutional neural networks (CNN) to enhance rheological properties prediction, while also coupled with the random forest (RF) model to predict printing parameters. Results show this approach efficiently matches yield stress (YS), plastic viscosity (PV), extrusion speed (ES), and printing speed (PS), significantly enhancing printing performance. This research provides construction engineers and 3D printing operators with a physics-guided, interpretable intelligent tool that reduces trial-and-error and improves construction reliability. The integration strategy also opens promising directions for future studies on large-scale printing involving multi-scale material-process-structure optimization and time-dependent rheological modeling.

中文翻译：

---

协同驱动物理和机器学习，用于高精度 3D 混凝土打印的智能控制

随着 3D 混凝土打印对精确控制的需求不断增加，协调材料流变特性和打印参数已成为一项关键挑战。本白皮书介绍了如何智能地优化打印参数以适应不同的混凝土材料属性并提高打印质量。提出了一种由物理信息方程 （PIE） 和机器学习 （ML） 共同驱动的双路径框架。PIE 嵌入到卷积神经网络 （CNN） 中以增强流变特性预测，同时还与随机森林 （RF） 模型耦合以预测打印参数。结果表明，这种方法有效地匹配了屈服应力 （YS）、塑料粘度 （PV）、挤出速度 （ES） 和打印速度 （PS），从而显著提高了打印性能。这项研究为施工工程师和 3D 打印作员提供了一种物理引导、可解释的智能工具，可减少试错并提高施工可靠性。该集成策略还为大规模打印的未来研究开辟了有希望的方向，包括多尺度材料-过程-结构优化和时间依赖性流变建模。