Mohammadi Moslem, Kouzani Abbas Z, Bodaghi Mahdi, Zolfagharian Ali
School of Engineering, Deakin University, Geelong, Victoria 3216, Australia.
Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
J Mech Behav Biomed Mater. 2025 Aug;168:107006. doi: 10.1016/j.jmbbm.2025.107006. Epub 2025 Apr 17.
This research proposes a computational framework for designing a compliant bistable mechanism and fabricating it using 3D printing for customized medical applications. The proposed method reduces upper limb tremors, taking advantage of the nonlinear mechanical properties of flexible structures. The model's development and execution on a single platform streamlines integrated inverse design and simulation, simplifying the customization process. A synthetic human arm model, built to imitate a human wrist, was scanned with a light detection and ranging (LiDAR) sensor to customize the 3D model of the bistable structure. Afterwards, the arm model was used to test the bistable mechanism. Automating the inverse design process with a deep neural network (DNN) and evolutionary optimization decides the optimal bistable mechanism configurations for stiffness and vibration attenuation. The pseudo-rigid-body model (PRBM) of the bistable mechanism was developed to train the machine learning (ML) model in the inverse design, making it computationally affordable to find the optimal parameters of bistable structure for a specific mechanical response based on tremor characteristics. Experimental results showing up to 87.11 % reduction in tremor power while weighing only 27 g to reduce vibrations in various situations suggest its use in 4D printing of wearable orthotic devices for Parkinsonian tremors and related diseases.
本研究提出了一种计算框架，用于设计一种柔顺双稳态机构，并使用3D打印技术将其制造出来，以用于定制化医疗应用。所提出的方法利用柔性结构的非线性力学特性来减少上肢震颤。该模型在单个平台上的开发和执行简化了集成逆设计和模拟，简化了定制过程。为模仿人类手腕构建的合成人体手臂模型，通过激光雷达（LiDAR）传感器进行扫描，以定制双稳态结构的3D模型。之后，该手臂模型被用于测试双稳态机构。利用深度神经网络（DNN）和进化优化实现逆设计过程的自动化，从而确定用于刚度和振动衰减的最佳双稳态机构配置。开发双稳态机构的伪刚体模型（PRBM）以在逆设计中训练机器学习（ML）模型，使得基于震颤特征为特定机械响应找到双稳态结构的最佳参数在计算上变得可行。实验结果表明，在各种情况下，该机构在仅重27克的情况下可使震颤功率降低高达87.11%，这表明其可用于帕金森震颤及相关疾病的可穿戴矫正装置的4D打印。
