Deep learning prediction of gamma-ray-attenuation behavior of KNN–LMN ceramics
Roya Boodaghi Malidarre, Seher Arslankaya, Melek Nar, Yasin Kırelli, Işık Yeşim Dicle Erdamar, Nurdan Karpuz, Serap Özhan Doğan, Parisa Boodaghi Malidarreh
Abstract
The significance and novelty of the present work is the preparation of non-lead ceramics with the general formula of (1 − x)K 0.5 Na 0.5 NbO 3 –xLaMn 0.5 Ni 0.5 O 3 (KNN–LMN) with different values of x (0 < x < 20) (mol%) to examine the shielding qualities of the KNN–LMN ceramics. This is done by carrying out Phy-X/PSD calculation and predicting the attenuation behavior of the samples by utilizing the deep learning (DL) algorithm. From the attained results, it is seen that the higher the x (concentration of LMN in the KNN–LMN lead-free ceramics), the better the shielding proficiency observed in terms of gamma-shielding performance for the chosen KNN–LMN-based lead-free ceramics. In all sections, good agreement is observed between Phy-X/PSD results and DL predictions.