Neural network aided flexible joint optimization with design of experiment method for nuclear power plant inspection robot.

Gang Wang; Jiawei Li; Xinmeng Ma; Xi Chen; Jixin Wang; Songjie Han; Biye Pan; Ruxiao Tian

Introduction: 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. Methods: 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. Results: 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, 450Nm/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. Discussion: 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.

NUCLEAR power plants; MEAN square algorithms; EXPERIMENTAL design; NUCLEAR energy; FLEXIBLE structures; ROBOTS

Frontiers in Neurorobotics, 2023, Vol 17, p1

1662-5218

Academic Journal

10.3389/fnbot.2023.1049922