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

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Humanizing Digital Reality


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.

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This project was undertaken as part of the Sapere Aude Advanced Grant research project Complex Modelling, supported by The Danish Council for Independent Research (DFF). The authors want to acknowledge the collaboration of Autodesk Forge, SICK Sensor Intelligence Denmark, Monash University Materials Science and Engineering, Bollinger + Grohmann, and the robot command and control software HAL.

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Correspondence to Paul Nicholas .

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© 2018 Springer Nature Singapore Pte Ltd.

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Zwierzycki, M., Nicholas, P., Ramsgaard Thomsen, M. (2018). Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. In: De Rycke, K., et al. Humanizing Digital Reality. Springer, Singapore.

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6610-8

  • Online ISBN: 978-981-10-6611-5

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