Fatigue Life Uncertainty Quantification of Front Suspension Lower Control Arm Design

Abstract

The purpose of this study is to investigate the uncertainty of the design variables of a front suspension lower control arm under fatigue-loading circumstances to estimate a reliable and robust product. This study offers a method for systematic uncertainty quantification (UQ), and the following steps were taken to achieve this: First, a finite element model was built to predict the fatigue life of the control arm under bump-loading conditions. Second, a sensitivity scheme, based on one of the global analyses, was developed to identify the model’s most and least significant design input variables. Third, physics-based and data-driven uncertainty quantification schemes were employed to quantify the model’s input parameter uncertainties via a Monte Carlo simulation. The simulations were conducted using 10,000 samples of material properties and geometrical uncertainty variables, with the coefficients of variation ranging from 1 to 3%. Finally, the confidence interval results show a deviation of about 21.74% from the mean (the baseline). As a result, by applying systematic UQ, a more reliable and robust automobile suspension control arm can be designed during the early stages of design to produce a more efficient and better approximation of fatigue life under uncertain conditions.

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization

“…The basic idea of using a surrogate model is not wholly novel. Several researchers have studied and applied a variety of surrogate models, such as the polynomial model [ 13 ], the radial basis function [ 14 ], polynomial chaos expansion [ 15 ], the Kriging method [ 16 ], and the adaptive Gaussian process [ 17 ]. In addition, generating the sample data to develop a surrogate model is a critical activity, since surrogate models are built using the training samples in the parameter space.…”