Fibre Acustic Projectile Pavilion University of Tokyo 2017/18
Prof. Yusuke Obuchi (Obuchi Lab)
Professors
Prof. Jun Sato (Sato Lab) Shohei Furuichi
Shuntaro Nozawa
Deborah Lopez Lobato Ao Y ang
Project Management
Hadin Charbel
Mika Kaibara Portugaise
Nathália Barros Abate Rotelli Chen
Shuta Takagi
Second Year Students
Rūta Stankevičiūtė
Xiaoke
Ziyi Wu
Anran Wang Lindstam Sher Lin Tan Yuqing Shi
Dominika Demlova Priya Murugeswaran Sofia Iino Cotado
Otto Malcolm
Ruoyu Chen Wanting Liu
First Year Students
Yuxi Zhu
Aleksandar Kirilov Mladenov
Machiko Asahara
Additional Student Support
Kyosuke Kawamura Taisei Coorporation
Sponsor and Project Collaborator
Statement Can the integration of digital technologies in design and construction processes be used for expressing personal tendencies through the creation of new architectural practices as well as novel aesthetics? The University of Tokyo Digital Fabrication Lab (DFL) has annually been involved in an experimental pavilion installation, aiming at the application of digital technologies to architectural construction. Its projects are the embodiments of human-machine interaction, demonstrating its unique fabrication systems that make a worker behave as part of a 3D printer; translating human understanding of space through sound into design. At the DFL, the role played by digital technologies in design and construction does not necessarily have to make them more efficient and streamlined. Their potential is instead focused on personalising the fabrication process and human natural tendencies. In our view, the act of making as such becomes a design in itself by means of computer assistance. The diversity that humans collectively show is organically expressed in the architecture. PAFF is an architecture made by reference to sound rather than visual information. We developed a sound guidance system whereby a worker’s task is to recognise a sound source in a three-dimensional space, and shoot fibres into various targets. PAFF is expressed through the accumulation of the multiagent system, revealing personal differences in the way that people perceive space through sound.
built pavilion at University of Tokyo campus
positioning on the site
exploded diagram of PAFF
mapping individual sound-space accuracy with virtual sound sources in virtual space
individual profile details
Otto
Domi
Sissi
Ruta
Anran
Carol
Chisato
Domi
Jack
Inoue
Mika
Otto
Pria
Rachel
Ruta
Ryo
Sher
Sissi
Sofi
Natsuki
Yuki
Yuta
individual sound-space maps
Material
Machine Learning Process
The main material in the PAFF pavilion is coconut
The machine learning system consists of three
fibre, also known as coir. It is the natural fibre
parts. First, the data of input angles and actual
extracted from the coconut husk after these husks
shooting angles are imported into the system
are cured to decompose their pulp, then dried (a
to calculate the deviations happened on certain
process called retting) and spun either by hand or
vertical angles.
with the use of machines. Then we used the neural network component Coconut fibre have the highest toughness amongst
incorporated in a grasshopper plugin for computing
natural fibres - even being researched to be used
the correlation between the input vertical angles
as reinforcement in concrete structures. But usually
and the deviations. A curve is generated so we can
it is used as floor mats, brushes, in horticulture and
have a basic understanding of this relationship.
agricultural fields and much more. It is also relatively waterproof and easy to stuck to itself and other
Finally, the shooting range will be remapped from
rough materials thanks to the morphology of the
the real situation to a machine learning corrected
fibre.
range. By applying this modified shooting range, the accuracy of a user is corrected to his/her
Thanks to several tests it was decided that the optimal weight was 4-6g, and that the fibre compressed into a ball was more efficient than when in its fluffy and natural state - although both configurations were used.
projectile of fiber elevation
comfortable range.
STEP01: Point With Deviation
STEP02: Machine Learning Curve
STEP03: Shooting Range Remapping
machine learning to adjust guidance
1
3
2
6 5
4
1 2 3 4 5
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User Control Panel Vive Tracker Data Input Head Movement Tracking Blowing Tool Movement Tracking Aiming Target Input
7
- Aiming Target Compensation via Machine Learning - Aiming Target Acousticalization
8
- Aiming Output & Deviation Record
6
8 7
grasshopper script
scaled model testing
1:1 testing in lab environment
47 individually handmade meshes
placement of pre-bent columns and shooting of fiber
site with scafold
projectile station
details of fiber canopy