Inspection of ratcheting models for pathological error sensitivity and overparametrization

TL;DR

This paper introduces a sensitivity analysis algorithm for plastic ratcheting models, assessing parameter reliability amidst experimental errors, and explores the link between error sensitivity and overparametrization using titanium alloy data.

Contribution

A new mechanics-based metric and step-by-step sensitivity estimation algorithm for plastic ratcheting models are proposed, enhancing parameter reliability assessment.

Findings

Sensitivity analysis distinguishes reliable and unreliable parameters.

The new isotropic hardening rule improves simulation accuracy.

A relation between error sensitivity and overparametrization is established.

Abstract

Accurate analysis of plastic strain accumulation under stress-controlled cyclic loading is vital for numerous engineering applications. Typically, models of plastic ratcheting are calibrated against available experimental data. Since actual experiments are not exactly accurate, one should check the identification protocols for pathological dependencies on experimental errors. In this paper, a step-by-step algorithm is presented to estimate the sensitivities of identified material parameters. As a part of the sensitivity analysis method, a new mechanics-based metric in the space of material parameters is proposed especially for ratcheting-related applications. The sensitivity of material parameters to experimental errors is estimated, based on this metric. For demonstration purposes, the accumulation of irreversible strain in the titanium alloy VT6 (Russian analog of Ti-6Al-4V) is analysed. Three types of phenomenological models of plastic ratcheting are considered. They are the Armstrong-Frederick model as well as the first and the second Ohno-Wang models. Based on real data, a new rule of isotropic hardening is proposed for greater accuracy of simulation. The ability of the sensitivity analysis to determine reliable and unreliable parameters is demonstrated. The plausibility of the new method is checked by alternative approaches, like the consideration of correlation matrices and validation of identified parameters on "unseen" data. A relation between pathological error sensitivity and overparametrization is established.