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Research on ANN based on Simulated Annealing in Parameter Optimization of Micro-scaled Flow Channels Electrochemical Machining

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2023, 9(3), pp.93-98
  • DOI : 10.20465/KIOTS.2023.9.3.093
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : March 31, 2023
  • Accepted : May 29, 2023
  • Published : June 30, 2023

Min, Byung-Won 1

1목원대학교

Accredited

ABSTRACT

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

Citation status

* References for papers published after 2023 are currently being built.