Taylor‐based smart flower optimization algorithm with the deep residual network to predict mechanical materials properties

Abstract The expedience of materials processing is of great significance and increased the industrial interest in meeting the needs of contemporary engineering applications. The inspection of mechanical properties is extensively explored by scientists, but the prediction of properties with the deep model is limited. This article presents an optimized deep residual network (DRN) to predict mechanical properties of materials. The quantile normalization is applied for improved processing. The DRN is pre‐trained with an optimization model for initializing the best set of attributes and tuning the parameters of the model. Here, Taylor‐Smart Flower Optimization Algorithm (Taylor‐SFOA) is adapted for training DRN by tuning optimum weights. The proposed Taylor‐SFOA helps to effectively offer precise mapping amidst mechanical properties and processing parameters. The optimal features are selected with the Ruzicka and Motyka. The selected features are fused with a dice coefficient to choose distinct features for attaining effective performance. The method yielded better outcomes with improved generalization. The Taylor‐SFOA‐based DRN provided better outcomes with smallest Mean absolute error (MAE) of 0.049, Mean square error (MSE) of 0.116, Root Mean square error (RMSE) of 0.340, memory footprint of 37.700 MB, and training time of 9.633 Sec.