Adaptive Robotic Carving
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.
KeywordsMaterial behaviors Machine learning Instrumental knowledge Subtractive manufacturing
The project is part of ongoing Ph.D. research conducted by Giulio Brugnaro, supervised by Prof. Bob Sheil and Dr. Sean Hanna, at the Bartlett School of Architecture, University College of London, within the framework of the “InnoChain Training Network,” supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642877.
- Al-Zubaidi, S., Ghani, J.A., Haron, C.H.C.: Application of ANN in milling process: a review. Model. Simul. Eng. 2011, 9 (2011)Google Scholar
- Brugnaro, G., Hanna, S.: Adaptive robotic training methods for subtractive manufacturing. In: ACADIA 2017: Disciplines and Disruption, Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA), Cambridge (MA), pp. 164–169 (2017)Google Scholar
- Carpo, M.: The Alphabet and the Algorithm. MIT Press, Cambridge (2011)Google Scholar
- DeLanda, M.: Material Complexity. In: Leach, N., Turnbull, D., Williams, C. (eds.) Digital Tectonics, pp. 14–21. Routledge, Chichester (2004)Google Scholar
- Noble, D.F.: Forces of Production. Knopf, New York (1984)Google Scholar
- Polanyi, M.: The Tacit Dimension. Anchor Books, Garden City (1967)Google Scholar
- Steinhagen, G., Braumann, J., Brüninghaus, J., Neuhaus, M., Brell-Çokcan, S., Kuhlenkötter, B.: Path planning for robotic artistic stone surface production. In: Reinhardt, D., Saunders, R., Burry, J. (eds.) Robotic Fabrication Architecture, Art and Design 2016, pp. 122–135. Springer International Publishing Switzerland (2016)Google Scholar
- Witt, A.J.: A machine epistemology in architecture. Encapsulated knowledge and the instrumentation of design. Candide J. Archit. Knowl. 3(3), 37–88 (2010)Google Scholar
- Zwierzycki, M., Nicholas, P., Thomsen, M.R.: Localised and learnt applications of machine learning for robotic incremental sheet forming. In: De Rycke, K., Gengnagel, C., Baverel, O., Burry, J., Mueller, C., Nguyen, M.M., Rahm, P., Thomsen, M.R. (eds.) Humanizing Digital Reality, Design Modelling Symposium Paris 2017, pp. 373–382. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6611-5_32