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RISS 인기검색어
- 검색키워드
- 저자 : Shahrum Abdullah (검색결과 3 건)
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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,Yat Sheng Kong 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.6
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 rootmean-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 random strain loading of leaf spring.
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Singh Salvinder,Abdullah Shahrum,Nik Abdullah Nik Mohamed 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.8
We studied the effect of mean stress correction factor using the Rainflow counting technique to assess the fatigue damage of an automobile crankshaft under service loading by considering the stochastic process of the Markov chain. The failure of the crankshaft will cause serious damage to the engine and also to other connecting subcomponents. The service loading is computationally generated from the Discrete Markov chain model and the fatigue cycle is counted using the Rainflow counting technique with the consideration of the local minima and maxima load. To quantify the fatigue damage, the strain-life curve using the fatigue mean stresses was used to model the fatigue failure of the material used in for the crankshaft at N f = 10 6 . The fatigue mean stresses were used to estimate the effects of the mean stress on the fatigue strength of the component under service loading condition. Statistical verification with the boundary condition of the 90% confidence level was performed to observe the difference between the stochastic algorithms when compared towards the fatigue life behavior of the ductile cast iron material. We concluded that for the practical application, the proposed stochastic model provides a highly accurate assessment of fatigue damage prediction for improving the safety and controlling the risk factors in terms of structural health monitoring.
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Airee Afiq Abd Rahim,Shahrum Abdullah,Salvinder Singh Karam Singh,Mohammad Zaki Nuawi 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.1
This paper discusses the determination of the optimum decomposition level by using the discrete wavelet transform (DWT) method for an automobile suspension system. The DWT method has been widely adopted in signal processing analyses. For the purpose of this study, a car was driven on two types of road conditions: highway and bumpy. Strain signals were measured based on the response gained from the coil spring. These signals were decomposed into 14 levels of decomposition, in which the percentage of wavelet energy for Levels 1 through 4 were 100 % similar to the original signals for both roads. The results were evaluated by comparing the fatigue life values for each decomposition level to the original signal. Based on the comparison of both roads, levels 1 to 3 show a difference of less than 20 % in fatigue life compared to the original signal. Thus, the accuracy of wavelet-based signal processing has proven to be applied in the fatigue durability analysis for automotive applications.
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