Making the optimal grade

IF 1.9 4区工程技术 Q3 ENGINEERING, MECHANICAL

Advances in Mechanical Engineering Pub Date : 2023-08-01 DOI:10.1177/16878132231187797

James Baldwin

引用次数: 0

Abstract

Researchers in Vietnam and Canada have developed a ready to use model to predict critical loads for functionally graded materials. Their neural network-based technique is ideal for users working on functionally graded plates and does not require advanced coding skill to implement [Le T-T, Duong HT, Phan HC. Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate. Advances in Mechanical Engineering. 2023;15(5). doi:10.1177/16878132231175002].

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做出最佳成绩

越南和加拿大的研究人员开发了一个现成的模型来预测功能梯度材料的临界载荷。他们的神经网络技术非常适合在功能梯度板上工作的用户，不需要高级编码技能即可实现[Le T-T，Duong HT，Phan HC。神经网络结构的优化和闭合方程的推导，以预测功能梯度材料板的极限载荷。机械工程进展。2023；15（5）doi:10.1177/16878132232175002]。

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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来源期刊

Advances in Mechanical Engineering 工程技术-机械工程

CiteScore

3.60

自引率

4.80%

发文量

353

审稿时长

6-12 weeks

期刊介绍： Advances in Mechanical Engineering (AIME) is a JCR Ranked, peer-reviewed, open access journal which publishes a wide range of original research and review articles. The journal Editorial Board welcomes manuscripts in both fundamental and applied research areas, and encourages submissions which contribute novel and innovative insights to the field of mechanical engineering