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

doi:10.3389/fnbot.2023.1049922

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

Front Neurorobot. 2023 Feb 8:17:1049922. doi: 10.3389/fnbot.2023.1049922. eCollection 2023.

Authors

Gang Wang¹, Jiawei Li¹, Xinmeng Ma¹, Xi Chen^{2

3}, Jixin Wang¹, Songjie Han¹, Biye Pan¹, Ruxiao Tian¹

Affiliations

¹ Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China.
² College of Mechanical and Electrical Engineering, Heilongjiang Institute of Technology, Harbin, China.
³ College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.

Abstract

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, 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.

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.

Keywords: flexible joint; inspection robot; neural network; optimization; topology.

Grants and funding

This work was funded by The National Natural Science Foundation of China (Grant No. 52001116), The National Natural Science Foundation of Heilongjiang Province (Grant Nos. YQ2020E033 and YQ2020E028), in part by The China Postdoctoral Science Foundation funded project under Grant 2018M630343, The Heilongjiang Postdoctoral Science Foundation funded project under Grant 18649, and The Research Fund from Science and Technology on Underwater Vehicle Technology under Grant 2021-SYSJJ-LB06909.