Session: 25-02 Deformation Modeling

Submission Number: 176593

A Correlation Method for Determining Chaboche Hardening Parameters From Tensile Properties 

Abstract

Accurate constitutive modeling of cyclic plasticity is vital for predicting material behavior under complex loading conditions. However, determining Chaboche model parameters through traditional experimental methods is costly and time-consuming, creating significant barriers to accurate cyclic modelling. The Chaboche model, integrating nonlinear kinematic hardening with Voce’s isotropic hardening, offers a robust framework for capturing cyclic plasticity in materials. Its reliance on extensive cyclic testing for parameter identification limits practical applications in industries such as aerospace, automotive, and structural engineering, where accurate solutions are critical to maintain mechanical integrity. A data-driven methodology was developed to predict Chaboche parameters directly from uniaxial tensile properties. A comprehensive database of steel alloys, compiled from peer-reviewed literature, was classified into three distinct groups. Advanced regression techniques mapped tensile properties to Chaboche parameters, generating predictive models per group. The resulting models produced hysteresis loops that closely aligned with experimental data from literature, demonstrating high predictive accuracy. Partitioning materials based on yield and tensile properties significantly enhanced model precision, ensuring reliability across a diverse range of steel alloys and various loading conditions. This approach reduces the need for resource-intensive cyclic experiments to create initial models and offers a scalable and efficient alternative for parameter identification. By enabling rapid, high-fidelity predictions, these correlation models streamline cyclic plasticity modeling, advancing material design and optimization in critical engineering applications.

Keywords: Constitutive Modeling, Cyclic Plasticity, Chaboche Model, Data-Driven Modeling, Material Parameter Identification.

Presenting Author: Adedotun Banjo University of Central Florida

Presenting Author Biography: Adedotun Banjo is a Ph.D. student in Aerospace Engineering at the University of Central Florida. His research focuses on constitutive modeling, cyclic plasticity, and data-driven approaches for predicting material behavior under complex loading conditions. He is developing correlation-based and computational frameworks for estimating Chaboche hardening parameters from tensile properties to improve simulation accuracy in aerospace and structural materials. His broader interests include fatigue, failure, and fracture mechanics, life prediction of metallic components, finite element analysis, viscoplasticity, and the integration of AI techniques in materials design and structural integrity assessment. He is proficient in MATLAB and Python for advanced data analysis, numerical modeling, and simulation of material behavior.

Authors:

Adedotun Banjo University of Central Florida
William David Day PSM - Power Systems Mfg
Nathan O'nora PSM - Power Systems Mfg
Ali Gordon University of Central Florida