id |
acadia21_308 |
authors |
Rossi, Gabriella; Chiujdea, Ruxandra; Colmo, Claudia; El Alami, Chada; Nicholas, Paul; Tamke, Martin; Ramsgaard Thomsen, Mette |
year |
2021 |
title |
A Material Monitoring Framework |
doi |
https://doi.org/10.52842/conf.acadia.2021.308
|
source |
ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 308-317. |
summary |
Through 3d printing, cellulose-based biopolymers undergo a two-staged hybrid fabrication process, where initial rapid forming is followed by a slower secondary stage of curing. During this curing large quantities of water are evaporated from the material which results in anisotropic deformations. In order to harness the potential of 3d printing biopolymers for architectural applications, it is necessary to understand this extended timeline of material activity and its implications on critical architectural factors related to overall element shrinkage, positional change of joints, and overall assembly tolerance. This paper presents a flexible multi-modal sensing framework for the understanding of complex material behavior of 3d printed cellulose biopolymers during their transient curing process. We report on the building of a Sensor Rig, that interfaces multiple aspects of the curing of our cellulose-slurry print experiments, using a mix of image-based, marker-based, and pin-based protocols for data collection. Our method uses timestamps as a common parameter to interface various modes of curing monitoring through multi-dimensional time slices. In this way, we are able to uncover underlying correlations and affects between the different phenomena occuring during curing. We report on the developed data pipelines enabling the Monitoring Framework and its associated software and hardware implementation. Through graphical Exploratory Data Analysis (EDA) of 3 print experiments, we demonstrate that geometry is the main driver for behavior control. This finding is key to future architectural-scale explorations. |
series |
ACADIA |
type |
paper |
email |
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full text |
file.pdf (6,218,441 bytes) |
references |
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, Advanced Materials Technologies 2 (4): 1600084
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, Construction Robotics 2 (1–4): 41–51
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last changed |
2023/10/22 12:06 |
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