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

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Bettermann, Luca and Slepicka, Martin and Esser, Sebastian and Borrmann, André; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Data-driven parameter calibration in additive manufacturing for construction : an introduction to learning by printing. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 153-163. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093249)

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Abstract

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.

  • Item type:Book Section
    ID code:93249
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 10:35
    Last modified:29 Aug 2026 12:29
    URI:https://strathprints.strath.ac.uk/id/eprint/93249
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