Simulated Annealing ANN Approach for Parameter Optimization of Micro-scaled Flow Channels Formation by Electrochemical Machining

Abstract

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the micro-scaled flow channels' electrochemical machining parameters and the shape of the channel 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 a "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 channel 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 disappear. When the voltage is 25V, the machining morphology of the channel is the best.