4.7 Article

Fatigue reliability and hazard assessment of road load strain data for determining the fatigue life characteristics

Journal

ENGINEERING FAILURE ANALYSIS
Volume 123, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engfailanal.2021.105314

Keywords

Fatigue reliability; Mean stress; Durability; Variable amplitude loading; Strain data

Funding

  1. Universiti Kebangsaan Malaysia [FRGS/1/2019/TK03/UKM/02/1, DCP-2017-020/1]

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This study assesses the fatigue reliability under random road load strain data from various road conditions, and models the reliability, cycle to failure, and hazard rate based on experimental data to determine fatigue life characteristics.
This paper aims to assess the fatigue reliability associated for determining the fatigue life characteristics under random strain loads from various road load conditions. Fatigue failure for automotive components is mainly due to the random loads during the operating condition. However, there are still limited studies to associate fatigue reliability from fatigue life data for random loads, especially in the automotive industry. Experimentally measured random road load strain data at a sampling rate of 500 Hz in 60 s were captured from three different road conditions. The time history of the road load strain data illustrated low amplitude and high-frequency events on uneven surface roads. Fatigue life is assessed using the Coffin-Manson, Morrow and Smith-Watson-Topper models based on the Rainflow cycle counting technique. The fatigue life was estimated to be within a range of 3.21 x 10(2) to 1.08 x 10(5) cycles/block with a mean cycle to failure of 3.36 x 10(4) to 1.20 x 10(5) cycles/block. The Gumbel distribution is identified as the suitable distribution for the predicted fatigue lives data to model the reliability, hazard rate and mean cycle to failure. Hence, the distribution of reliability, cycle to failure and hazard rate is modelled and proposed for determining fatigue life characteristics under random road load strain data.

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