Abstract
3D printing is conventionally performed with manipulators with only three degrees of freedom (DOF), resulting in objects consisting of horizontal layers and inherent weakness in the vertical direction. This shortcoming is mitigated by printing along curved surfaces, which requires manipulators with more degrees of freedom, a new way of trajectory planning, and a dynamic control of material extrusion speed - the issue addressed in this paper. Our printing set-up, which we describe in the paper, includes an industrial 6 DOF manipulator. The manipulator has non-negligible inertia as well as other limiting constraints, and thus fails to achieve the programmed speed on many of the printing path segments leading to either overflow or underflow of material and poor object quality. We develop a simple empirical approach to predict the speed on problematic path segments and adjust the speed of extrusion, ensuring the correct amount of material is deposited.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dizon, J.R.C., Espera, A.H., Chen, Q., Advincula, R.C.: Mechanical characterization of 3D-printed polymers. Addit. Manuf. 20, 44–67 (2018)
Allen, R.J.A., Trask, R.S.: An experimental demonstration of effective curved layer fused filament fabrication utilising a parallel deposition robot. Addit. Manuf. 8, 78–87 (2015)
Lim, S., Buswell, R.A., Valentine, P.J., Piker, D., Austin, S.A., De Kestelier, X.: Modelling curved-layered printing paths for fabricating large-scale construction components. Addit. Manuf. 12, 216–230 (2016)
Kraljić, D., Kamnik, R.: Trajectory Planning for additive manufacturing with a 6-DOF industrial robot. In: Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018). Advances in Service and Industrial Robotics, pp. 456–465, January 2019
Cheng, B., Titterington, D.M.: Neural networks: a review from a statistical perspective. Stat. Sci. 9(1), 2–30 (1994)
Choi, B.B., Lawrence, C: Inverse kinematics problem in robotics using neural networks. NASA STI/Recon Technical Report N, 93, October 1992
Simon, D.: The application of neural networks to optimal robot trajectory planning. Robot. Auton. Syst. 11(1), 23–34 (1993)
Hasan, A.T., Hamouda, A.M.S., Ismail, N., Al-Assadi, H.M.A.A.: An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 d.o.f serial robot manipulator. Adv. Eng. Softw. 37(7), 432–438 (2006)
Tang, S.H., Ang, C.K., Ariffin, M.M., Mashohor, S.: Predicting the motion of a robot manipulator with unknown trajectories based on an artificial neural network. Int. J. Adv. Robot. Syst. 11(10), 176 (2014)
Acknowledgements
This work was supported by the European cohesion funds as part of the project Development of multiaxis robotic 3D printing of composite materials - MMO3D under the grant Interreg V-A Slovenia-Austria, ESRR No. SIAT73, and by the Slovenian Research Agency (ARRS) under research program Motion analysis and synthesis in man and machine (P20228).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kraljić, D., Štefanič, M., Kamnik, R. (2020). 3D Printing with 6D of Freedom: Controlling Material Extrusion Speed. In: Berns, K., Görges, D. (eds) Advances in Service and Industrial Robotics. RAAD 2019. Advances in Intelligent Systems and Computing, vol 980. Springer, Cham. https://doi.org/10.1007/978-3-030-19648-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-19648-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19647-9
Online ISBN: 978-3-030-19648-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)