3D Geometric Measurements of 3D Printed Concrete via Structured Light Machine Vision (2026-01)¶
, Martin Michael, ,
Contribution - Computing in Civil Engineering, pp. 876-884
Abstract
Automated inspection and quality control of the robotic 3D printing process remains a key challenge for the broader adoption of this innovative construction method. To address this, a novel methodology for in-process geometrical measurement of 3D printed layers using laser light machine vision is proposed. This system utilizes a vision algorithm to process data from a 2D camera, enhanced by a structured laser light (SLL) pattern, to measure both the width and height of the extrudates. A systematic validation program, including single-layer and multi-layer deformed specimens, was conducted to evaluate the proposed methodology. The results demonstrate that the designed SLL vision system delivers continuous geometrical measurements with a maximum error of less than 1 mm. The SLL algorithm’s processing time of 0.25 s, combined with its high measurement accuracy, indicates its great potential for real-time geometrical measurements during robotic 3D printing.
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13 References
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0 Citations
BibTeX
@inproceedings{bani_mart_masc_kaze.2025.3GMo3PCvSLMV,
author = "Kasra Banijamali and Michael Martin and David Mascareñas and Ali Kazemian",
title = "3D Geometric Measurements of 3D Printed Concrete via Structured Light Machine Vision",
doi = "10.1061/9780784486436.093",
year = "2025",
pages = "876--884",
booktitle = "Computing in Civil Engineering: Computational and Intelligent Technologies",
editor = "Amirhosein Jafari and Yimin Zhu",
}
Formatted Citation
K. Banijamali, M. Martin, D. Mascareñas and A. Kazemian, “3D Geometric Measurements of 3D Printed Concrete via Structured Light Machine Vision”, in Computing in Civil Engineering: Computational and Intelligent Technologies, 2025, pp. 876–884. doi: 10.1061/9780784486436.093.
Banijamali, Kasra, Michael Martin, David Mascareñas, and Ali Kazemian. “3D Geometric Measurements of 3D Printed Concrete via Structured Light Machine Vision”. In Computing in Civil Engineering: Computational and Intelligent Technologies, edited by Amirhosein Jafari and Yimin Zhu, 876–84, 2025. https://doi.org/10.1061/9780784486436.093.