Derivation of the Orthotropic Nonlinear Elastic Material Law Driven by Low-Cost Data(DDONE)

作     者：Qian Xiang Hang Yang K.I.Elkhodary Zhi Sun Shan Tang Xu Guo Qian Xiang;Hang Yang;K.I.Elkhodary;Zhi Sun;Shan Tang;Xu Guo

作者机构：State Key Laboratory of Structural Analysis for Industrial EquipmentDepartment of Engineering MechanicsDalian University of TechnologyDalian116023China International Research Center for Computational MechanicsDalian University of TechnologyDalian116023China The Department of Mechanical EngineeringThe American University in CairoNew Cairo11835Egypt 

出 版 物：《Acta Mechanica Solida Sinica》 (固体力学学报（英文版）)

年 卷 期：2022年第35卷第5期

页      面：800-812页

核心收录：

学科分类：08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：The support of Project MKF20210033 is acknowledged 

主　　题：Data-driven Orthotropic nonlinear elastic materials Constitutive law Finite element analysis Artificial neural network 

摘      要：Orthotropic nonlinear elastic materials are common in nature and widely used by various ***,there are only limited constitutive models available in today s commercial software(e.g.,ABAQUS,ANSYS,etc.)that adequately describe their mechanical ***,the material parameters in these constitutive models are also difficult to calibrate through low-cost,widely available experimental ***,it is paramount to develop new ways to model orthotropic nonlinear elastic *** this work,a data-driven orthotropic nonlinear elastic(DDONE)approach is proposed,which builds the constitutive response from stress–strain data sets obtained from three designed uniaxial tensile *** DDONE approach is then embedded into a finite element(FE)analysis framework to solve boundary-value problems(BVPs).Illustrative examples(e.g.,structures with an orthotropic nonlinear elastic material)are presented,which agree well with the simulation results based on the reference material *** DDONE approach generally makes accurate predictions,but it may lose accuracy when certain stress–strain states that appear in the engineering structure depart significantly from those covered in the data *** DDONE approach is thus further strengthened by a mapping function,which is verified by additional numerical examples that demonstrate the effectiveness of our modified ***,artificial neural networks(ANNs)are employed to further improve the computational efficiency and stability of the proposed DDONE approach.