Session: 25-02 Deformation Modeling

Submission Number: 175393

Predictive Method for Obtaining Chaboche Parameters for Various Materials: Torsional Loading 

The type of loading that a material undergoes can greatly impact the lifespan of that material and how it will perform during service. To be able to better understand the effect of different loadings on the lifespans of materials, the material must be tested under various conditions. While experimental testing is the most accurate method of determining how a material will perform, simulations are an alternative that can limit the costs that are accrued from experimental testing. One such method of simulations that can give insight into stress and strain on a material from different loading scenarios is the Chaboche model. Furthermore, the Chaboche model has numerous parameters that increase the accuracy of the depiction of the hardening or softening of a material, but makes the determination of those parameters a complex endeavor. In this paper a new method of using a predictive model was developed to account for torsional loadings on FCC focused metals or engineering metals that behave similarly to FCC metals. As the model is focused on FCC or similarly behaving engineering metals, a time-independent Chaboche model is used with three backstress parameters. An optimization algorithm of the parameters to better match the experimental data is included. The results included show that the identification procedure used in this paper can be utilized for torsional loadings on a material along with axial loadings. This identification of Chaboche parameters is paramount for increasing the ease of use of simulations of materials undergoing alternate loading methods besides axial loading.

Presenting Author: Navindra Wijeyeratne Floridapolytechnic University

Presenting Author Biography: Dr. Navindra Wijeyeratne is an assistant professor of mechanical engineering at Florida Polytechnic University. His expertise includes mechanics of materials, solid mechanics, fatigue analysis, constitutive modeling, and computational modeling using finite element analysis (FEA). His research focuses on enhancing next-generation engineering materials, specifically additively manufactured superalloys, by using multiscale material modeling and artificial neural networks (ANN) to improve computational efficiency. He also has extensive experience in the constitutive modeling of Nickel-base superalloys, employing crystal viscoplasticity (CVP) theory to more accurately describe material behavior and address uncertainties in the fatigue performance of these materials for critical aerospace components.

Authors:

Noah Al-Shaer Florida Polytechnic University
Navindra Wijeyeratne Floridapolytechnic University
Ali Gordon University of Central Florida