4D printing parameters optimisation for bi-stable soft robotic gripper design
Ali Zolfagharian, Mohammad Lakhi, Sadegh Ranjbar, Morteza Sayah Irani, Marwan Nafea, Mahdi Bodaghi
- 发表年份
- 2023
- 引用次数
- 44
- 访问权限
- 开放获取
摘要
Abstract Four-dimensional (4D) printing is an emerging additive manufacturing (AM) technology that adds a time-dependent reconfiguration dimension to three-dimensional (3D) printed products. It enables the creation of on-demand, dynamically controllable shapes, or properties in response to external stimuli such as temperature, magnetic field, and light. Thermally responsive structures are among the most popular types of currently available 4D-printed structures due to their convenience. However, applications like soft robots are hindered by the temperature-sensitive structures' stagnating actuation. This research was driven by a requirement for a rapid and effective design and optimisation strategy for 4D-printed bi-stable thermally responsive structures for use in soft robotics. In this study, the response surface method (RSM) optimization with the aid of numerical solutions was used to investigate effective parameters in the design of a bi-stable, 4D-printed soft robotic gripper. This approach is proposed to accelerate the actuation of thermally responsive shape-morphing structures that can be controlled by the in situ strains and post-manufacturing heat stimuli as variable parameters. By using RSM solution the individual effects as well as the coupling effects of variable parameters on the output responses, including the maximum strain energy and the average distance between the clamps of the structure, are evaluated. The obtained results can be employed to develop the designation and improve the acceleration of soft robotic grippers such as fast buckling and bending, which is desirable for soft robotic applications. Graphical abstract
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002