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Inelastic constitutive modeling for viscoplastcity using neural network
이준성,Tomonari Hurukawa 한국지능시스템학회 2005 한국지능시스템학회논문지 Vol.15 No.3
Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.
Formulation for the Parameter Identification of Inelastic Constitutive Equations
Lee, Joon-Seong,Bae, Byeong-Gyu,Hurukawa, Tomonari Computational Structural Engineering Institute of 2010 한국전산구조공학회논문집 Vol.23 No.6
This paper presents a method for identifying the parameter set of inelastic constitutive equations, which is based on an Evolutionary Algorithm. The advantage of the method is that appropriate parameters can be identified even when the measured data are subject to considerable errors and the model equations are inaccurate. The design of experiments suited for the parameter identification of a material model by Chaboche under the uniaxial loading and stationary temperature conditions was first considered. Then the parameter set of the model was identified by the proposed method from a set of experimental data. In comparison to those by other methods, the resultant stress-strain curves by the proposed method correlated better to the actual material behaviors.
Constitutive Parameter Identification of Inelastic Equations Using an Evolutionary Algorithm
이은철(Eun-Chul Lee),이준성(Joon-Seong Lee),古川知成(Tomonari Hurukawa) 한국지능시스템학회 2009 한국지능시스템학회논문지 Vol.19 No.1
본 논문에서는 제안된 진화적 알고리즘을 바탕으로 한 비탄성 구성방정식의 파라미터를 결정하기 위한 방법을 제시한다. 이 방법의 장점은 오차를 갖고 있는 측정된 데이터들이나 모델 방정식들이 부정확하더라도 적절한 파라미터들이 결정되어진다는 것이다. 실험설계는 단축하중과 일정 온도조건하의 샤보쉬 재료모델의 파라미터 결정에 적합하였다. 동시에 모델의 파라미터들은 실험데이터들과 제안한 방법에 의한 값들과 일치하였다. 다른 방법들에 의한 값들과 비교해 본 결과, 제안한 방법에 의한 응력-변형률 선도는 실제적인 재료거동에 비해 좋게 나타났다. This paper presents a method for identifying the parameter set of inelastic constitutive equations, which is based on an Evolutionary Algorithm. The advantage of the method is that appropriate parameters can be identified even when the measured data are subject to considerable errors and the model equations are inaccurate. The design of experiments suited for the parameter identification of a material model by Chaboche under the uniaxial loading and stationary temperature conditions was first considered. Then the parameter set of the model was identified by the proposed method from a set of experimental data. In comparison to those by other methods, the resultant stress-strain curves by the proposed method correlated better to the actual material behaviors.