Purpose
This study aims to determine the reliability assessment based on the predicted fatigue life of leaf spring under random strain loading.
Design/methodology/approach
Random loading data were extracted from three various road conditions at 200 Hz using a strain gauge for a duration of 100 s. The fatigue life was predicted using strain-life approaches of Coffin–Manson, Morrow and Smith–Watson–Topper (SWT) models.
Findings
The leaf spring had the highest fatigue life of 1,544 cycle/block under highway data compared uphill (1,299 cycle/block) and downhill (1,008 cycle/block) data. Besides that, the statistical properties of kurtosis showed that uphill data were the highest at 3.81 resulted in the presence of high amplitude in the strain loading data. For fatigue life-based reliability assessment, the SWT model provided a narrower shape compared to the Coffin–Manson and Morrow models using the Gumbel distribution. The SWT model had the lowest mean cycle to failure of 1,250 cycle/block followed by Morrow model (1,317 cycle/block) and the Coffin–Manson model (1,429 cycle/block). The SWT model considers the mean stress effects by interpreting the strain energy density that will influence the reliability assessment.
Research limitations/implications
The reliability assessment based on fatigue life prediction is conducted using the Gumbel distribution to investigate the behaviour of fatigue random loading, where most previous studies had concentrated on a Weibull distribution on random data.
Originality/value
Thus, this study proposes that the Gumbel distribution is suitable for analysing the reliability of random loading data in assessing with the fatigue life prediction of a heavy vehicle leaf spring.
