4D-Printed Soft Pneumatic Actuators Guided by Machine Learning and Finite Element Models
DOI:
https://doi.org/10.37255/jme.v15i4pp126-135Keywords:
soft pneumatic actuators, 3D printing, Machine Learning, Finite element models, 4D Printing, Soft robots.Abstract
This paper explores the field of four-dimensional 4D-printed soft pneumatic actuators (SPAs) made completely by additive manufacturing. After manufacturing, these actuators can produce bending motions when exposed to vacuum or pressurized air stimuli. Their functionality is determined by a combination of material qualities and geometric details, which gives the pressures and motion trajectories required for fine activities like non-invasive surgery and food handling. This introduces an innovative approach to achieve four-dimensional (4D) printing of soft pneumatic actuator robots (SPAs). This method leverages nonlinear machine learning (ML) techniques combined with finite element modelling (FEM). The core of this methodology involves the development of a precise FEM that emulates experimental actuation. The primary purpose of this FEM is to generate essential training data for the subsequent ML modelling phase. The wide range of 3D printers and materials used to create pressurized air-bending-style SPAs is thoroughly examined in this paper. It also examines different approaches to modelling and regulating these actuators and provides a comparative study. This paper concludes with a summary of general considerations regarding future directions and the inherent difficulties in developing these state-of-the-art actuators
Downloads
References
1. C. Tawk and G. Alici, ‘Finite Element Modeling in the Design Process of 3D Printed Pneumatic Soft Actuators and Sensors’, Robotics, vol. 9, no. 3, Art. no. 3, Sep. 2020, doi: 10.3390/robotics9030052.
2. A. Zolfagharian, A. Kaynak, M. Bodaghi, A. Z. Kouzani, S. Gharaie, and S. Nahavandi, ‘Control-Based 4D Printing: Adaptive 4D-Printed Systems’, Appl. Sci., vol. 10, no. 9, p. 3020, Apr. 2020, doi: 10.3390/app10093020.
3. S. Ta Liu, ‘Machine Learning: A Practical Approach on the Statistical Learning’, Technometrics, vol. 62, no. 4, pp. 560–561, Oct. 2020, doi: 10.1080/00401706.2020.1825645.
4. G. Runge, M. Wiese, and A. Raatz, ‘FEM-based training of artificial neural networks for modular soft robots’, 2017 IEEE Int. Conf. Robot. Biomim. ROBIO, pp. 385–392, Dec. 2017, doi: 10.1109/ROBIO.2017.8324448.
5. C. Tawk, Y. Gao, R. Mutlu, and G. Alici, ‘Fully 3D Printed Monolithic Soft Gripper with High Conformal Grasping Capability’, in 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China: IEEE, Jul. 2019, pp. 1139–1144. doi: 10.1109/AIM.2019.8868668.
6. A. Zolfagharian, A. Kaynak, and A. Kouzani, ‘Closed-loop 4D-printed soft robots’, Mater. Des., vol. 188, p. 108411, Mar. 2020, doi: 10.1016/j.matdes.2019.108411.
7. G. Alici, T. Canty, R. Mutlu, W. Hu, and V. Sencadas, ‘Modeling and Experimental Evaluation of Bending Behavior of Soft Pneumatic Actuators Made of Discrete Actuation Chambers’, Soft Robot., vol. 5, no. 1, pp. 24–35, Feb. 2018, doi: 10.1089/soro.2016.0052.
8. Y. He, L. Zhu, G. Sun, M. Yu, and M. Dong, ‘Design, Measurement and Shape Reconstruction of Soft Surgical Actuator Based on Fiber Bragg Gratings’, Appl. Sci., vol. 8, no. 10, p. 1773, Sep. 2018, doi: 10.3390/app8101773.
9. A. Y. Lee, A. Zhou, J. An, C. K. Chua, and Y. Zhang, ‘Contactless reversible 4D-printing for 3D-to-3D shape morphing’, Virtual Phys. Prototyp., vol. 15, no. 4, pp. 481–495, Oct. 2020, doi: 10.1080/17452759.2020.1822189.
10. A. Zolfagharian, M. A. P. Mahmud, S. Gharaie, M. Bodaghi, A. Z. Kouzani, and A. Kaynak, ‘3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control’, Virtual Phys. Prototyp., vol. 15, no. 4, pp. 373–402, Oct. 2020, doi: 10.1080/17452759.2020.1795209.
11. H. K. Yap, H. Y. Ng, and C.-H. Yeow, ‘High-Force Soft Printable Pneumatics for Soft Robotic Applications’, Soft Robot., vol. 3, no. 3, pp. 144–158, Sep. 2016, doi: 10.1089/soro.2016.0030.
12. K. Elgeneidy, N. Lohse, and M. Jackson, ‘Data-Driven Bending Angle Prediction of Soft Pneumatic Actuators with Embedded Flex Sensors’, IFAC-Pap., vol. 49, no. 21, pp. 513–520, Jan. 2016, doi: 10.1016/j.ifacol.2016.10.654.
13. K. Elgeneidy, N. Lohse, and M. Jackson, ‘Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors – A data-driven approach’, Mechatronics, vol. 50, pp. 234–247, Apr. 2018, doi: 10.1016/j.mechatronics.2017.10.005.
14. R. Guachi, F. Bini, M. Bici, F. Campana, F. Marinozzi, and L. Guachi, ‘Finite element analysis in colorectal surgery: non-linear effects induced by material model and geometry’, Comput. Methods Biomech. Biomed. Eng. Imaging Vis., vol. 8, no. 2, pp. 219–230, Mar. 2020, doi: 10.1080/21681163.2019.1679669.
Downloads
Published
Issue
Section
How to Cite
