Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming

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Abstract

While fabrication is becoming a well-established field for architectural robotics, new possibilities for modelling and control situate feedback, modelling methods and adaptation as key concerns. In this paper we detail two methods for implementing adaptation, in the context of Robotic Incremental Sheet Forming (ISF) and exemplified in the fabrication of a bridge structure. The methods we describe compensate for springback and improve forming tolerance by using localised in process distance sensing to adapt tool-paths, and by using pre-process supervised machine learning to predict stringback and generate corrected fabrication models.
Original languageEnglish
Title of host publicationHumanizing Digital Reality : Design Modelling Symposium Paris 2017
Number of pages10
PublisherSpringer
Publication date2017
Pages373-382
ISBN (Print)978-981-10-6610-8
ISBN (Electronic)978-981-10-6611-5
DOIs
Publication statusPublished - 2017

Keywords

  • Machine learning
  • incremental sheet forming
  • Robotic fabrication
  • Digital architecture

Artistic research

  • No

Cite this

Nicholas, P., Zwierzycki, M., & Ramsgaard Thomsen, M. (2017). Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. In Humanizing Digital Reality: Design Modelling Symposium Paris 2017 (pp. 373-382). Springer. https://doi.org/10.1007/978-981-10-6611-5_32
Nicholas, Paul ; Zwierzycki, Mateusz ; Ramsgaard Thomsen, Mette. / Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. Humanizing Digital Reality: Design Modelling Symposium Paris 2017. Springer, 2017. pp. 373-382
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Nicholas, P, Zwierzycki, M & Ramsgaard Thomsen, M 2017, Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. in Humanizing Digital Reality: Design Modelling Symposium Paris 2017. Springer, pp. 373-382. https://doi.org/10.1007/978-981-10-6611-5_32

Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. / Nicholas, Paul; Zwierzycki, Mateusz; Ramsgaard Thomsen, Mette.

Humanizing Digital Reality: Design Modelling Symposium Paris 2017. Springer, 2017. p. 373-382.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Nicholas P, Zwierzycki M, Ramsgaard Thomsen M. Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. In Humanizing Digital Reality: Design Modelling Symposium Paris 2017. Springer. 2017. p. 373-382 https://doi.org/10.1007/978-981-10-6611-5_32