Journal of Mechanical Science and Technology 34 (6) 2020 DOI 10.1007/s12206-020-0510-z 2341 Journal of Mechanical Science and Technology 34 (6) 2020 Original Article DOI 10.1007/s12206-020-0510-z Keywords: · Heavy vehicle · Leaf spring · Strain data · Fatigue life · Power spectral density Correspondence to: Salvinder Singh Karam Singh salvinder@ukm.edu.my Citation: Abdullah, L., Singh, S. S. K., Abdullah, S., Azman, A. H., Ariffin, A. K., Kong, Y. S. (2020). The needs of power spectral density in fatigue life prediction of heavy vehicle leaf spring. Journal of Mechanical Science and Technology 34 (6) (2020) 2341~2346. http://doi.org/10.1007/s12206-020-0510-z Received October 24th, 2019 Revised February 26th, 2020 Accepted March 12th, 2020 † Recommended by Editor Chongdu Cho The needs of power spectral density in fatigue life prediction of heavy vehicle leaf spring Lennie Abdullah, Salvinder Singh Karam Singh, Shahrum Abdullah, Abdul Hadi Azman, Ahmad Kamal Ariffin and Yat Sheng Kong Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia Abstract This study characterized the properties of random strain loading data for using power spectral density (PSD) in frequency domain of a heavy vehicle leaf spring. This is due to missing data caused by the sensitivity of the strain gauges in capturing strain signal. Strain signal was captured from a leaf spring component for 100 s at a sampling rate of 200 Hz using strain gauge. Fatigue life prediction was computed using strain-life models: Coffin-Manson, Morrow and Smith-Watson-Topper (SWT). The fatigue strain data showed that downhill data produces the lowest fatigue life prediction at 3.42 × 10 2 cycles/block with high energy of 3.6 × 10 4 μɛ 2 .Hz -1 ; then it was followed by curve and highway data. This was supported by the root- mean-square (RMS) value at 324.24 μɛ as it is directly related towards the PSD based on the energy contained for each captured signal. The correlation of fatigue life and strain amplitude was calculated to identify the distribution of fatigue strain data of leaf spring. Thus, the fatigue strain loading data can be characterized properly based on the energy content in PSD, the statistical parameter in the form of RMS value and the correlation with strain amplitude for ran- dom strain loading of leaf spring. 1. Introduction Leaf springs commonly utilized in heavy vehicles are formed of multiple leaves assembled with linearly rectangular cross sectioned metal plates above each other stacked together [1]. The fabrication of a leaf spring is mechanically robust as it is connected to axles and vehicle frame with solid metal plates layered together in preventing high abrupt vibration transferred to passenger and reducing bumps shifted to the engine and vehicle body [2]. The leaf spring ab- sorbs the sudden strain energy generated by bumps that are caused during the movement of a vehicle over an irregular road and hilly areas [3]. Hence, a leaf spring is subjected to cyclic loading and accumulating fatigue damage. The strain was measured and used to correlate with low-cycle fatigue analysis; they are directly related to stress, fatigue and subsequently, failure [4]. For fatigue life assessment, the fre- quency domain technique is suggested to accelerate the calculations substantially with the loading definition in the domain of frequency [5]. The obtained random loads are random Gaussian process, which it is suitable to characterize in the frequency domain by applying power spectral density (PSD) [6]. Soleimani and Ahmadi [7] analyzed the vibration of the leaf spring and air ride suspensions of a truck suspension system using PSD and discovered the leaf spring produced greater vibration scales than the air ride suspension. Prasad and Sekhar [8] estimated fatigue life shafts based on vibration signal based on rainflow cycle counting of time domain method and the power spectral density of frequency domain. Zhang et al. [9] worked on the program load spectrum compilation approach that proposed an indication in predicting the fatigue life of a parabolic leaf spring. This paper’s aim is to predict the fatigue life of a heavy vehicle leaf spring component to © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020