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Probabilistic-based fatigue reliability assessment of carbon steel coil spring from random strain loading excitation

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Abstract

This paper aims to assess the fatigue reliability of random loading signals of a suspension coil spring using probabilistic approaches. Strain time histories were acquired while the car was travelling on different road conditions (i.e., in a rural area, in an industrial area, on a university campus, on a highway and on a newly constructed road). Fatigue lives were predicted from the strain histories and fitted into probability density functions. Lognormal distribution was found to be an appropriate way to represent fatigue data. Next, the reliability function and mean-cycles-to-failure (MCTF) were determined. The results indicated that fatigue reliability rapidly deteriorated under rural road conditions, which resulted in a short MCTF of 104 cycles. Meanwhile, the new road signals had the longest MCTF of about 108 cycles. Accordingly, this is due to the rural road having the most surface irregularities, which caused more severe fatigue damage to the coil spring. This study contributed to a greater in-depth understanding of the effect of loading signals on fatigue reliability. This is essential in determining the appropriate service life of the coil spring during its production to ensure vehicle safety and reduce maintenance costs.

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Acknowledgments

The authors are grateful for the support from the Ministry of Education Malaysia and Universiti Kebangsaan Malaysia under the research grant FRGS/1/2019/TK03/UKM/01/3 and DIP-2019-015.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia

    C. H. Chin, S. Abdullah, S. S. K. Singh & A. K. Ariffin

  2. Departmental Chair of Mechatronics, University of Duisburg-Essen, 47057, Duisburg, Germany

    D. Schramm

Authors
  1. C. H. Chin
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  2. S. Abdullah
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  3. S. S. K. Singh
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  4. A. K. Ariffin
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  5. D. Schramm
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Corresponding author

Correspondence to S. Abdullah.

Additional information

C. H. Chin currently studies Ph.D. in Mechanical Engineering at the Universiti Kebangsaan Malaysia. His research is focused on the durability of components in automobile applications and signal processing.

S. Abdullah is a Professor at the Department of Mechanical and Manufacturing Engineering, Universiti Kebangsaan Malaysia (UKM). His research focused on fatigue failure and signal analysis.

S. S. K. Singh is a Lecturer at the Department of Mechanical and Manufacturing Engineering, UKM, Malaysia. His research interests include reliability engineering, damage mechanics, fatigue data analysis, fatigue failure, structural integrity and durability analysis.

A. K. Ariffin is a Professor at the Department of Mechanical and Manufacturing Engineering, UKM. His specialty is in the computational method in engineering under the area of powder mechanics, fracture mechanics, friction, corrosion, finite element/discrete element and parallels computations.

D. Schramm is a Professor and Head of the Chair of Mechatronics at the University of Duisburg-Essen. His researches focused on vehicle dynamics, driver assistance system, vehicle simulators, mechatronic components, and robotics machines.

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Chin, C.H., Abdullah, S., Singh, S.S.K. et al. Probabilistic-based fatigue reliability assessment of carbon steel coil spring from random strain loading excitation. J Mech Sci Technol 36, 109–118 (2022). https://doi.org/10.1007/s12206-021-1209-5

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  • DOI: https://doi.org/10.1007/s12206-021-1209-5

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