Image Classification for Robotic Plastering with Convolutional Neural Network

Bard, Joshua; Bidgoli, Ardavan; Wei, Wei

doi:10.1007/978-3-319-92294-2_1

Cited by 12 publications

(11 citation statements)

References 17 publications

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“…The ornamental roles relate to the production of aesthetic qualities and variations to the surfaces of the built structure, although this is often neglected in current practice, as described in Ü ber Putz: Oberflächen entwickeln und realisieren by Spiro et al 1 The application of plaster to interior walls and ceilings, as well as to façades, is a craft that requires specific tools, intuition, and a particular skill set. It is a challenging process that is carried out in several steps and in consecutive layers,* as shown in Figure 1 (left, images [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“… Left : Steps of a typical plastering process: (1), (2), (3) application of the base coat; (4), (5) application of the top coat; and (6) application of the smooth coat. Middle : Robotic arm with a smoothening trowel, imitating one step of the manual procedure, Bard et al 2 Top right : Textural detail from surface finishing, Spiro et al 1 Bottom right : Plasterer with a running mold, taken from . …”

Section: Introductionmentioning

confidence: 99%

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Robotic Plaster Spraying: Crafting Surfaces with Adaptive Thin-Layer Printing

Jenny

Lloret-Fritschi

David

et al. 2022

3D Printing and Additive Manufacturing

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Embedded in a long tradition of craftsmanship, inside or outside building surfaces, is often treated with plaster, which plays both functional and ornamental roles. Today, plasterwork is predominantly produced through rationalized, time-, and cost-efficient processes, used for standardized building elements. These processes have also gained interest in the construction robotics field, and while such approaches target the direct automation of standardized plasterwork, they estrange themselves from the inherent qualities of this malleable material that are well known from the past. This research investigates the design potentials of robotic plaster spraying, proposing an adaptive, thin-layer vertical printing method for plasterwork that aims to introduce a digital craft through additive manufacturing. The presented work is an explorative study of a digitally controlled process that can be applied to broaden the design possibilities for the surfaces of building structures. It involves the spraying of multiple thin layers of plaster onto a vertical surface to create volumetric formations or patterns, without the use of any formwork or support structures. This article describes the experimental setup and the initial results of the data collection method involving systematic studies with physical testing, allowing to develop means to predict and visualize the complex-to-simulate material behavior, which might eventually enable to design with the plasticity of this material in a digital design tool.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robotic Plaster Spraying: Crafting Surfaces with Adaptive Thin-Layer Printing

Jenny

Lloret-Fritschi

David

et al. 2022

3D Printing and Additive Manufacturing

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show abstract

“…The latter starts to get attention in published literature, where fabrication setups augmented with various sensing systems show potential of in-line process improvement via feedback control, as e.g. demonstrated in Wolfs (2019), Rodríguez-Gonzálvez and Guidi (2019), Bard et al (2018). In particular, this feedback is relevant in additive manufacturing applications with cementitious materials which are unpredictable and hard to model in their dynamic transition from the soft to hard state.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic quality inspection to detect and classify manufacturing errors is usually carried out by identifying discrepancies between virtual design models and their corresponding as-built structures (Buswell et al 2020). Such way of carrying out inspections promises to bring the fabrication field one step closer towards mass customization and production (Bard et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Different types of sensor feedback systems exist that can support the fabrication process, however, the focus of the presented work is on systems that capture and provide visual as well as 3D information of the objects. In Bard et al (2018), the authors used an RGB camera system and a visual feedback loop for the purpose of quality evaluation of plastered surfaces and the acquired data are analyzed using convolutional neural networks to identify manufacturing errors. Furthermore, Jenny et al (2020) explored the design potential of bespoke plastered elements by using a depth camera capturing the 3D shape of the plastered objects.…”

Section: Introductionmentioning

confidence: 99%

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Geometric Feedback System for Robotic Spraying

Frangež¹,

Taha²,

Feihl³

et al. 2022

PFG

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In this paper, we tackle the task of replacing labor intensive and repetitive manual inspection of sprayed concrete elements with a sensor-based and automated alternative. We present a geometric feedback system that is integrated within a robotic setup and includes a set of depth cameras used for acquiring data on sprayed concrete structures, during and after fabrication. The acquired data are analyzed in terms of thickness and surface quality, with both sets of information then used within the adaptive fabrication process. The thickness evaluation is based on the comparison of the as-built state to a previous as-built state or to the design model. The surface quality evaluation is based on the local analysis of 3D geometric and intensity features. These features are used by a random forest classifier trained using data manually labelled by a skilled professional. With this approach, we are able to achieve a prediction accuracy of 87 % or better when distinguishing different surface quality types on flat specimens, and 75 % when applied in a full production setting with wet and non-planar surfaces. The presented approach is a contribution towards in-line material thickness and surface quality inspection within digital fabrication.

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Puttybot: A sensorized robot for autonomous putty plastering

Liu,

Chen,

Eldosoky

et al. 2024

Journal of Field Robotics

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Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force‐controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large‐scale wall was processed without human involvement.

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Image Classification for Robotic Plastering with Convolutional Neural Network

Cited by 12 publications

References 17 publications

Robotic Plaster Spraying: Crafting Surfaces with Adaptive Thin-Layer Printing

Robotic Plaster Spraying: Crafting Surfaces with Adaptive Thin-Layer Printing

Geometric Feedback System for Robotic Spraying

Puttybot: A sensorized robot for autonomous putty plastering

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