PMID- 36845069
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230228
IS  - 1662-5218 (Print)
IS  - 1662-5218 (Electronic)
IS  - 1662-5218 (Linking)
VI  - 17
DP  - 2023
TI  - Neural network aided flexible joint optimization with design of experiment method 
      for nuclear power plant inspection robot.
PG  - 1049922
LID - 10.3389/fnbot.2023.1049922 [doi]
LID - 1049922
AB  - 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.
CI  - Copyright (c) 2023 Wang, Li, Ma, Chen, Wang, Han, Pan and Tian.
FAU - Wang, Gang
AU  - Wang G
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
FAU - Li, Jiawei
AU  - Li J
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
FAU - Ma, Xinmeng
AU  - Ma X
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
FAU - Chen, Xi
AU  - Chen X
AD  - College of Mechanical and Electrical Engineering, Heilongjiang Institute of 
      Technology, Harbin, China.
AD  - College of Information and Communication Engineering, Harbin Engineering 
      University, Harbin, China.
FAU - Wang, Jixin
AU  - Wang J
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
FAU - Han, Songjie
AU  - Han S
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
FAU - Pan, Biye
AU  - Pan B
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
FAU - Tian, Ruxiao
AU  - Tian R
AD  - Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering 
      University, Harbin, China.
LA  - eng
PT  - Journal Article
DEP - 20230208
PL  - Switzerland
TA  - Front Neurorobot
JT  - Frontiers in neurorobotics
JID - 101477958
PMC - PMC9944374
OTO - NOTNLM
OT  - flexible joint
OT  - inspection robot
OT  - neural network
OT  - optimization
OT  - topology
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/02/28 06:00
MHDA- 2023/02/28 06:01
PMCR- 2023/01/01
CRDT- 2023/02/27 04:54
PHST- 2022/09/21 00:00 [received]
PHST- 2023/01/23 00:00 [accepted]
PHST- 2023/02/27 04:54 [entrez]
PHST- 2023/02/28 06:00 [pubmed]
PHST- 2023/02/28 06:01 [medline]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fnbot.2023.1049922 [doi]
PST - epublish
SO  - Front Neurorobot. 2023 Feb 8;17:1049922. doi: 10.3389/fnbot.2023.1049922. 
      eCollection 2023.