Defect-based fatigue life prediction of A356-T6 aluminium alloy wheels

Read full text
Authors: Nicola Zani, Candida Petrogalli, Silvio Bonometti, Nicola Ferrami, Alessandro Concoreggi, Alessio Franchi, Claudio Sorlini, Giorgio Donzella
Cited by: 1
References: 26

Abstract

This study presents a defect-tolerant methodology for the fatigue assessment of full-scale A356-T6 cast aluminium wheels, explicitly accounting for the influence of casting defects on crack initiation and early propagation. The approach combines rotating-bending fatigue tests on complete wheels, finite-element identification of the fatigue-critical spoke region, metallographic characterization of shrinkage porosity, extreme-value statistical analysis of the maximum Feret diameter, and local residual-stress measurements by hole drilling. The statistically expected maximum defect size in the critical region was subsequently used as the initial defect in a small-crack-based predictive framework, in which the effect of mean stress was included through the measured residual-stress field. To address the requirements of industrial fatigue design, the defect statistics were extended from the single spoke to increasing return periods corresponding to one wheel and to production batches, allowing the derivation of lower-bound S-N predictions representative of increasing production volumes. The results show that the proposed framework captures the experimentally observed fatigue behaviour and provides conservative fatigue thresholds consistent with the statistical occurrence of critical defects. The methodology therefore offers a physically grounded and industrially applicable tool for defect-tolerant design and quality control of safety–critical cast components.

Fields of Study & Subjects

Mechanical Failure Analysis and Simulation Mechanical Engineering and Vibrations Research Fatigue and fracture mechanics

Publication details

Journal / source
Engineering Fracture Mechanics
Publisher
Elsevier BV
Volume
344
Pages
112383–112383
Publication date
18 June 2026
Language
EN