Encoded Images

Rossi, Gabriella; Nicholas, Paul

doi:10.52842/conf.acadia.2020.1.218

Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA)

2020

DOI: 10.52842/conf.acadia.2020.1.218

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Encoded Images

Gabriella Rossi,

Paul Nicholas

Abstract: Encoding bending energy of a thin metal shell as a 128 × 128 false-color RGB image. This image is part of a larger training set used to train a cGAN to replace finite element analysis structural simulation.

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Real-Time Precision in 3D Concrete Printing: Controlling Layer Morphology via Machine Vision and Learning Algorithms

Silva,

Wagner,

Silva

et al. 2024

Inventions

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3D concrete printing (3DCP) requires precise adjustments to parameters to ensure accurate and high-quality prints. However, despite technological advancements, manual intervention still plays a prominent role in this process, leading to errors and inconsistencies in the final printed part. To address this issue, machine learning vision models have been developed and utilized to analyze captured images and videos of the printing process, detecting defects and deviations. The data collected enable automatic adjustments to print settings, improving quality without the need for human intervention. This work first examines various techniques for real-time and offline corrections. It then introduces a specialized computer vision setup designed for real-time control in robotic 3DCP. Our main focus is on a specific aspect of machine learning (ML) within this system, called speed control, which regulates layer width by adjusting the robot motion speed or material flow rate. The proposed framework consists of three main elements: (1) a data acquisition and processing pipeline for extracting printing parameters and constructing a synthetic training dataset, (2) a real-time ML model for parameter optimization, and (3) a depth camera installed on a customized 3D-printed rotary mechanism for close-range monitoring of the printed layer.

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Real-Time Precision in 3D Concrete Printing: Controlling Layer Morphology via Machine Vision and Learning Algorithms

Silva,

Wagner,

Silva

et al. 2024

Inventions

View full text Add to dashboard Cite

show abstract

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Encoded Images

Abstract: Encoding bending energy of a thin metal shell as a 128 × 128 false-color RGB image. This image is part of a larger training set used to train a cGAN to replace finite element analysis structural simulation.

Cited by 1 publication

References 10 publications

Real-Time Precision in 3D Concrete Printing: Controlling Layer Morphology via Machine Vision and Learning Algorithms

Real-Time Precision in 3D Concrete Printing: Controlling Layer Morphology via Machine Vision and Learning Algorithms

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