Machine Learning Optimization of Auxiliary Cathode Structure for Thickness Uniformity in Micro-Electroforming

The metal thickness uniformity of the micro-electroforming process determines the structural accuracy, surface finish, and functional characteristics, which directly affect the quality, performance, and reliability of the final parts. However, due to the non-uniformity of the electric field distribution, the problem of thickness non-uniformity in the micro-electroforming process has always been one of the key challenges limiting its application and development. The auxiliary cathode structure can improve the uniformity of electroforming thickness by regulating the cathode electrodeposition rate and electric field distribution and is widely used in the micro-electroforming process. In this study, with the micro-array metal die core as the research object, based on finite element simulations, the effect of the geometrical parameters of the auxiliary cathode on the thickness uniformity of electroforming is analyzed in depth. This study introduces the optimization method of the BP neural network and the NSWOA (Non-dominated Sorting Whale Optimization Algorithm) and achieves a significant improvement in the unevenness of electroforming thickness by designing and adjusting the parameters of the position, width, and shape of the auxiliary cathode. The unevenness of the microarray metal mold core is reduced to 4% from 475% without the auxiliary cathode, using the auxiliary cathode structure designed with this method.