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supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id acadia17_164
authors Brugnaro, Giulio; Hanna, Sean
year 2017
title Adaptive Robotic Training Methods for Subtractive Manufacturing
source ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 164-169
summary This paper presents the initial developments of a method to train an adaptive robotic system for subtractive manufacturing with timber, based on sensor feedback, machine-learning procedures and material explorations. The methods were evaluated in a series of tests where the trained networks were successfully used to predict fabrication parameters for simple cutting operations with chisels and gouges. The results suggest potential benefits for non-standard fabrication methods and a more effective use of material affordances.
keywords design methods; information processing; construction; robotics; ai & machine learning; digital craft; manual craft
series ACADIA
email giuliobrugnaro@gmail.com
full text file.pdf (1,382,721 bytes)
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