Application of Control Algorithm in the Design of Automatic Crimping Device for Connecting Pipe and Ground Wire

Authors

  • Congbing Sheng State Grid Henan Electric Power Company, Puyang, Henan, 457000, China
  • Peng Xing State Grid Henan Electric Power Company, Puyang, Henan, 457000, China
  • Xiuzhong Cai State Grid Henan Electric Power Company, Puyang, Henan, 457000, China
  • Zheng Shao State Grid Henan Electric Power Company, Puyang, Henan,457000, China

DOI:

https://doi.org/10.12694/scpe.v25i2.2606

Keywords:

Intelligent fully automatic, Crimping of conductor and ground wire, Device design, Single chip microcomputer, PID control algorithm, Radiographic testing methods

Abstract

Due to the low effectiveness and poor quality of manual crimping of grounding wires, the author proposes the design of an automatic crimping device for connecting tube grounding wires based on intelligent fully automatic technology. The device consists of a microcontroller, an upper computer control interface, an electric push rod, an infrared sensor, a pressure transmitter, and other devices. The staff used the upper computer monitoring interface to set the relevant parameters for grounding wire crimping, and used X-ray digital imaging technology to measure the crimping size of the grounding wire. The size met the set parameter conditions. Through the PID control algorithm in the microcontroller, the stepper motor was controlled to push the clamp to move, completing the automatic crimping of the grounding wire. The X-ray detection method was introduced to detect the quality of the grounding wire after the crimping was completed. The experimental results show that the average deviation between the measured crimping size of the grounding wire and the actual measurement size by the automatic crimping device is only 0.06 mm, indicating that its measurement results are accurate; The success rate of crimping exceeds 95%. The above experimental results verify that the designed crimping device has high stability and reliability, and good quality detection effect.

 

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Published

2024-02-24

Issue

Section

Special Issue - Deep Learning-Based Advanced Research Trends in Scalable Computing