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Adaptive Robotic Carving: Training Methods for the Integration of Material Performances in Timber Manufacturing

Brugnaro, G; Hanna, S; (2019) Adaptive Robotic Carving: Training Methods for the Integration of Material Performances in Timber Manufacturing. In: Willmann, J and Block, P and Hutter, M and Byrne, K and Schork, T, (eds.) Robotic Fabrication in Architecture, Art and Design 2018 (ROBARCH 2018). (pp. pp. 336-348). Springer: Cham, Switzerland. Green open access

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Abstract

The paper presents the developments of a series of methods to train a fabrication system for the integration of material performances in timber manufacturing processes, combining robotic fabrication together with different sensing strategies and machine learning techniques, and their further application within a prototypical design to manufacturing workflow. The training cycle, spanning from the recording of skilled human experts to autonomous robotic explorations, aims to encapsulate different layers of instrumental knowledge into a design interface, giving designers the opportunity to engage with material and tool affordances as process driver. The training methods are evaluated in a series of experiments and design iterations, proving their potential in the development of customized design to manufacturing workflows and integration of material performances, with a specific focus on timber.

Type: Proceedings paper
Title: Adaptive Robotic Carving: Training Methods for the Integration of Material Performances in Timber Manufacturing
Event: Robotic Fabrication in Architecture, Art and Design 2018 (ROBARCH 2018), 9 - 14 Sep 2018, Zürich, Switzerland
ISBN-13: 9783319922942
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-92294-2_26
Publisher version: http://dx.doi.org/10.1007/978-3-319-92294-2_26
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Material Behaviors, Machine Learning, Instrumental Knowledge, Subtractive Manufacturing.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture
URI: https://discovery.ucl.ac.uk/id/eprint/10057677
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