The iron-oxide scale total thickness and FeO layer thick percentage of 72A high carbon steel (0.67%-0.73% C) coils have markedly influence on coil stripping performance. With using artificial neural networks and mathematical method the BP network model is established to realize the complex non-linear mapping relationship between the cooling schemes during oxidation process of high carbon steel wire and the formed FeO layer thickness percentage after oxidation. Compared between measured parameters and network simulation results

it is obtained that the training accuracy of BP network is very high and has better generalization ability to quite well response the effect of each factor on FeO layer thickness percentage. The production application results show that based on BP network simulation results to improve cooling process

suitable decreasing wire laying-off temperature and increasing cooling speed at 850-720℃，the FeO layer thickness percentage of scale decreases in order to improve the mechanical peeling performance.