Data mining and design of electromagnetic properties of Co/FeSi filled coatings based on genetic algorithms optimized artificial neural networks (GA-ANN)

Abstract It is difficult to establish analytical relationships between coating parameters (type and filling ratios of absorbent) and electromagnetic (EM) properties based on traditional trial-and-error methods. Data mining is expected to predict the EM properties of coatings filled with arbitrary set type/content of absorbent based on the measured parameters within a few samples, to solve this problem. In this study, a method for predicting EM properties of coatings was established based on GA-ANN algorithm. The proposed model was found be capable of predicting EM properties of coatings, not only in wide range of filling ratio but also in case of mixed absorbents. The GA-ANN model presented lower error and higher accuracy to compare with the conventional ANN methods. Additionally, GA was used to optimize the design of coatings via using of mixed absorbent aiming to obtain broadband electromagnetic absorption (EMA) characteristics. The optimized mixture coating with the thickness of 2.06 mm contained 13.5 wt% of Co and 55.6 wt% of FeSi particles, whose effective absorption bandwidth with reflection loss less than −10 dB (ERL10) could reach 9.3 GHz.