Data-Driven Parameter Calibration in Additive Manufacturing for Construction: An Introduction to Learning by Printing

This paper presents the Learning by Printing framework, a data-driven, closed-loop system using Fabrication Information Modeling to optimize extrusion-based additive manufacturing in construction, achieving over 95% prediction accuracy and improved performance through parameter calibration.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-04
DOI
https://doi.org/10.5281/zenodo.17052168
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

Data-Driven Parameter Calibration in Additive Manufacturing for Construction: An Introduction to Learning by Printing

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Data-Driven Parameter Calibration in Additive Manufacturing for Construction: An Introduction to Learning by Printing

article en

Abstract

This paper presents the Learning by Printing framework, a data-driven, closed-loop system using Fabrication Information Modeling to optimize extrusion-based additive manufacturing in construction, achieving over 95% prediction accuracy and improved performance through parameter calibration.

Zenodo (CERN European Organization for Nuclear Research)
Technical University of Munich (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 53%
Geochemistry and Geologic Mapping
DOIOpen Access PDFOpenAlex
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Data-Driven Parameter Calibration in Additive Manufacturing for Construction: An Introduction to Learning by Printing · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS