Wang Gang, Li Jiawei, Ma Xinmeng, Chen Xi, Wang Jixin, Han Songjie, Pan Biye, Tian Ruxiao
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China.
College of Mechanical and Electrical Engineering, Heilongjiang Institute of Technology, Harbin, China.
Front Neurorobot. 2023 Feb 8;17:1049922. doi: 10.3389/fnbot.2023.1049922. eCollection 2023.
The flexible joint is a crucial component for the inspection robot to flexible interaction with nuclear power facilities. This paper proposed a neural network aided flexible joint structure optimization method with the Design of Experiment (DOE) method for the nuclear power plant inspection robot.
With this method, the joint's dual-spiral flexible coupler was optimized regarding the minimum mean square error of the stiffness. The optimal flexible coupler was demonstrated and tested. The neural network method can be used for the modeling of the parameterized flexible coupler with regard to the geometrical parameters as well as the load on the base of the DOE result.
With the aid of the neural network model of the stiffness, the dual-spiral flexible coupler structure can be fully optimized to a target stiffness, 450 Nm/rad in this case, and a given error level, 0.3% in the current case, with regard to the different loads. The optimal coupler is fabricated with wire electrical discharge machining (EDM) and tested.
The experimental results demonstrate that the load and angular displacement keep a good linear relationship in the given load range and this optimization method can be used as an effective method and tool in the joint design process.
柔性关节是核电设施巡检机器人实现灵活交互的关键部件。本文针对核电站巡检机器人，提出了一种基于实验设计（DOE）方法的神经网络辅助柔性关节结构优化方法。
采用该方法，以刚度的最小均方误差为目标对关节的双螺旋柔性联轴器进行优化。对优化后的柔性联轴器进行了论证和测试。基于DOE结果，神经网络方法可用于对参数化柔性联轴器的几何参数和负载进行建模。
借助刚度神经网络模型，双螺旋柔性联轴器结构可针对不同负载，充分优化至目标刚度（在本案例中为450 Nm/rad）和给定误差水平（在本案例中为0.3%）。采用电火花线切割加工（EDM）制造出优化后的联轴器并进行测试。
实验结果表明，在给定负载范围内，负载与角位移保持良好的线性关系，该优化方法可作为关节设计过程中的有效方法和工具。
