Alive (2019)
article⁄Alive (2019)
abstract⁄In the context of datadriven culture, built space still maintains low responsiveness and adaptability. Part of this reality lies in the low resolution of live information we have about the behavior and condition of surfaces and materials. This research addresses this issue by exploring the development of a deformationsensing composite membrane material system following a bottomup approach and combining various technologies toward solving related technical issuesexploring conductivity properties of graphene and maximizing utilization within an architecturerelated proofofconcept scenario and a workflow including design, fabrication, and application methodology. Introduced simulation of intended deformation helps optimize the pattern of graphene nanoplatelets GNP to maximize membrane sensitivity to a specific deformation type while minimizing material usage. Research explores various substrate materials and graphene incorporation methods with initial geometric exploration. Finally, research introduces data collection and machine learning techniques to train recognition of certain types of deformation single point touch on resistance changes. The final prototype demonstrates stable and symmetric readings of resistance in a static state and, after training, exhibits an 88 prediction accuracy of membrane shape on a labeled sample dataset through a pretrained neural network. The proposed framework consisting of a simulation based, graphenecapturing fabrication method on stretchable surfaces, and includes initial exploration in neural network training shape detection, which combined, demonstrate an advanced approach to embedding intelligence.
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| Year |
2019 |
| Authors |
Koshelyuk, Daniil; Talaei, Ardeshir; Garivani, Soroush; Markopoulou, Areti; Chronis, Angelos; Leon, David Andres; Krenmuller, Raimund. |
| Issue |
ACADIA 19:UBIQUITY AND AUTONOMY |
| Pages |
664-673 |
| Library link |
N/A |
| Entry filename |
alive |
