3D Printing Deformation Estimation Using Artificial Vision Strategies for Smart-Construction

Villacrés, Juan; Guamán, Robert; Menéndez, Oswaldo; Cheein, Fernando Auat

doi:10.1109/iecon48115.2021.9589770

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The layer-thickness deformation analysis was performed using CV techniques with input devices, including monocular camera, LiDAR, and LiDAR camera. It was found that low-cost sensors, such as cameras, are faster in acquiring and processing the data in detecting deformations in concrete 3D-printed structures [5,7]. Further, CV-based techniques were used to quantify the layer deformations in concrete 3D printing under different printing variables, such as layer thickness, printing orientation, and printing direction [31].…”

Section: Previous Studiesmentioning

confidence: 99%

“…Optimal design of structures in terms of material and topology is another significant advantage of concrete 3D printing [3,4]. Despite a lot of research in this area, the practical application of this technology relies on trial and error and heuristicsbased calibration of material parameters [5]. As a result, the output quality of 3D-printed elements is highly variable, resulting in a waste of material, effort, and time.…”

Section: Introductionmentioning

confidence: 99%

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Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures

Senthilnathan

Raphael

2022

Sustainability

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Concrete 3D printing has the potential to reduce material and process waste in construction. Thus, it contributes to making the construction industry more sustainable through the use of digital-fabrication technologies. While concrete 3D printing is attractive due to its potential to realize complex designs, practical challenges include an increased chance of defects and deformities. Quality assessment of 3D-printed elements is essential for large-scale implementation. Workability of concrete is known to decrease with printing time and it impacts extrudability. It is usually visible in 3D-printed elements, with the lower layers having a smooth finish, while the top layers have cracks and discontinuities. A computer-vision-based quality assessment method is proposed in this paper using a two-bin Linear Binary Pattern textural analysis. Information entropy is used as the metric for measuring the texture variation within each layer and its changes over the layers are studied. A higher entropy value is found for layers having deformities. Finally, through the error-minimization technique, a threshold entropy value is calculated and, using this, the printed layers can be assessed and corrective actions taken. This paper contributes to developing a non-intrusive quality assessment technique for concrete 3D-printed elements.

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Section: Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures

Senthilnathan

Raphael

2022

Sustainability

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“…El reemplazo de unidades robóticas antiguas por modernas no es una opción viable debido a que no han cumplido un ciclo de trabajo o sus costos todavía no han sido amortizados [5]. Por lo tanto, existe la necesidad de actualizar un gran número de unidades para garantizar las demandas operativas.…”

Section: Introductionunclassified

Posicionamiento de ayuda visual utilizando compresión de nube de puntos y cámaras RGB-D para manipuladores robóticos

Cisternas Velásquez,

del Río Caldumbide,

Prado Romo

et al. 2023

Ingeniare. Rev. chil. ing.

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Durante la última década, se ha logrado la optimización de un conjunto masivo de tareas industriales aprovechando la precisión de los manipuladores robóticos. Si bien la nueva era de los manipuladores robóticos presenta herramientas de alta tecnología para solucionar problemas de posicionamiento y seguimiento, la actualización de las unidades más antiguas es un desafío importante debido a la incompatibilidad de hardware, mecanismos obsoletos y restricciones de operación. Este trabajo introduce un nuevo sistema de ayuda visual para determinar la postura y orientación, es decir, pose de un manipulador robótico utilizando un arreglo de dos cámaras estéreo. El sistema de posicionamiento visual calcula la posición del efector final a partir de la nube de puntos completa adquirida por el arreglo de cámaras y el modelo inverso del robot. Las nubes de puntos son fusionadas utilizando el algoritmo de Iterative Closest Point. Un detector de color amarillo detecta los puntos relacionados con el efector final. Finalmente, la posición del efector final se calcula con un estimador Media. Los resultados experimentales muestran que el dispositivo propuesto puede estimar la posición relativa del efector final respecto de la base del brazo robótico con errores aproximados de posicionamiento longitudinal, lateral y vertical de 19.6%,

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Research status and prospect of machine learning in construction 3D printing

Geng

Luo

Liu³

et al. 2023

Case Studies in Construction Materials

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3D Printing Deformation Estimation Using Artificial Vision Strategies for Smart-Construction

Cited by 5 publications

References 24 publications

Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures

Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures

Posicionamiento de ayuda visual utilizando compresión de nube de puntos y cámaras RGB-D para manipuladores robóticos

Research status and prospect of machine learning in construction 3D printing

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