Data-driven parameter calibration in additive manufacturing for construction: an introduction to learning by printing

This paper introduces the Learning by Printing framework, designed to enhance performance and robustness in extrusion-based Additive Manufacturing in Con-struction. Leveraging Fabrication Information Modeling (FIM) as a digital backbone, the framework integrates evaluation, prediction, and calibration stages into a closed fabrication-learning loop. An experimental study on a clay extrusion setup demonstrates the framework’s ability to optimize structural performance through data-driven parameter calibration. The Gaussian Process prediction model achieves over 95% accuracy, while calibration shows to improve system perfor-mance. Future work will scale the framework to larger systems and integrate online learning for real-time control, advancing Learning by Printing toward a predictive and adaptive approach to digital fabrication.