Stress–strain curve estimation from load–depth curve of spherical indentation test based on finite element analysis and optimization
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A stress–strain curve is one of the most important indicators of material properties for characterizing constitutive behavior. It can be directly obtained from a conventional uniaxial tensile test or load–displacement curve using an instrumented indentation test (IIT). An IIT uses formulations of the effective indentation strain and stress or the iterative finite element analysis (FEA) of the indentation process using a constitutive model, such as a powerlaw constitutive model. In this study, an iterative FEA is used to obtain the engineering stress–strain curve. However, it focused on considering the realistic plastic flow of specific materials without idealizing the powerlaw constitutive model. A concept for stress–strain estimation using a master stress–strain curve for specific materials was proposed, and its usefulness was validated via FEA and optimization techniques. In the future, this methodology can be extended to deep learning technology to address the issue of time consumption in iterative optimization methods
Abstract
A stress–strain curve is one of the most important indicators of material properties for characterizing constitutive behavior. It can be directly obtained from a conventional uniaxial tensile test or load–displacement curve using an instrumented indentation test (IIT). An IIT uses formulations of the effective indentation strain and stress or the iterative finite element analysis (FEA) of the indentation process using a constitutive model, such as a powerlaw constitutive model. In this study, an iterative FEA is used to obtain the engineering stress–strain curve. However, it focused on considering the realistic plastic flow of specific materials without idealizing the powerlaw constitutive model. A concept for stress–strain estimation using a master stress–strain curve for specific materials was proposed, and its usefulness was validated via FEA and optimization techniques. In the future, this methodology can be extended to deep learning technology to address the issue of time consumption in iterative optimization methods
