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

Oshin Sharma, Deepak Sharma
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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.
基于泰勒的智能花优化算法与深度残差网络用于预测机械材料性能
材料加工的便捷性对满足当代工程应用的需求具有重要意义,并提高了工业界的兴趣。科学家们对力学性能的检测进行了广泛的探索,但利用深度模型进行性能预测却很有限。本文提出了一种优化的深度残差网络(DRN)来预测材料的力学性能。应用量子归一化改进了处理过程。DRN 采用优化模型进行预训练,用于初始化最佳属性集和调整模型参数。在此,泰勒-智能花优化算法(Taylor-SFOA)通过调整最佳权重来训练 DRN。所提出的泰勒-智能花优化算法有助于有效提供机械性能和加工参数之间的精确映射。通过 Ruzicka 和 Motyka 方法选择最佳特征。选定的特征与骰子系数融合,以选择不同的特征,从而获得有效的性能。该方法取得了更好的结果,提高了通用性。基于泰勒-SFOA 的 DRN 的结果更好,平均绝对误差(MAE)最小,为 0.049;平均平方误差(MSE)最小,为 0.116;均方根误差(RMSE)最小,为 0.340;内存占用为 37.700 MB,训练时间为 9.633 秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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