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.
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References
Autodesk Forge: https://forge.autodesk.com/. Accessed 19 May 2017
Bambach, M., Taleb Araghi, B., Hirt, G.: Strategies to improve the geometric accuracy in asymmetric single point incremental forming. Prod Eng 3, 145–156 (2009). doi:10.1007/s11740-009-0150-8
Behera, A.K., Verbert, J., Lauwers, B., Duflou, J.R.: Tool path compensation strategies for single point incremental sheet forming using multivariate adaptive regression splines. CAD Comput. Aided Des. 45, 575–590 (2013). doi:10.1016/j.cad.2012.10.045
Faro Focus3D 120: http://www.faro.com/en-us/products/3d-surveying/faro-focus3d. Accessed 19 May 2017
Grasshopper 3D: http://www.grasshopper3d.com/. Accessed 19 May 2017
HAL: http://www.food4rhino.com/app/hal-robot-programming-control. Accessed 19 May 2017
Jeswiet, J., Bramley, A.N., Micari, F., et al.: Asymmetric single point incremental forming of sheet metal. CIRP Ann. Manuf. Technol. 54, 623–649 (2005)
Khan, M.S., Coenen, F., Dixon, C., et al.: An intelligent process model: predicting springback in single point incremental forming. Int. J. Adv. Manuf. Technol. 76, 2071–2082 (2014). doi:10.1007/s00170-014-6431-1
KUKA|prc: http://www.robotsinarchitecture.org/kuka-prc. Accessed 19 May 2017
Nicholas, P., Zwierzycki, M., Nørgaard, E., Stasiuk, D., Ramsgaard Thomsen, M.: Adaptive robotic fabrication for conditions of material inconsistency: increasing the geometric accuracy of incrementally formed metal panels. In: Proceedings of Fabricate 2017, pp. 114–121 (2017)
Owl Framework: https://github.com/mateuszzwierzycki/Owl. Accessed 19 May 2017
Paniti, I., Rauschecker, U.: Integration of incremental sheet forming with an adaptive control into cloud manufacturing. In: MITIP (2012)
Ruffini, R., Cao, J.: Using neural network for springback minimization in a channel forming process. SAE Tech. Pap. (1998). doi:10.4271/980082
Robots IO: https://robots.io/wp/. Accessed 19 May 2017
TensorFlow Framework: https://www.tensorflow.org/. Accessed 19 May 2017
Volvox: https://github.com/CITA-cph/Volvox. Accessed 19 May 2017
Acknowledgements
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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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. https://doi.org/10.1007/978-981-10-6611-5_32
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DOI: https://doi.org/10.1007/978-981-10-6611-5_32
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