Mission profiling of road data measurement for coil spring fatigue life

Kong, Y. S.; Abdullah, Shahrum; Schramm, Dieter; Omar, Mohd Zaidi; Haris, Sallehuddin Mohamed; Brückmann, Tobias

doi:10.1016/j.measurement.2017.05.011

Cited by 43 publications

(22 citation statements)

References 17 publications

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“…The passenger car was then drive through different road for acceleration signals collection where the road could be classified into highway, rural area, and residential road. A sampling rate of 1000 Hz was determined during the data collection process to ensure that all of the required information was captured [28]. For signal processing, the collected acceleration signals in forms of VAL were then transformed into PSD for frequency domain vibration fatigue analysis.…”

Section: Parameters Valuementioning

confidence: 99%

Vibration Fatigue Analysis of Carbon Steel Coil Spring under Various Road Excitations

Kong

Abdullah

Schramm

et al. 2018

Metals

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This paper presents the evaluation of frequency-based approach predicted spring using acceleration signals that were collected from various road conditions. Random loadings in the forms of acceleration are nominal and more flexible for vehicle components fatigue assessment. In this analysis, the strain time history of the spring and acceleration signals of the suspension strut was measured from three different road conditions. The acceleration signals were then transformed into power spectra density (PSD). PSD cycle counter, like Lalanne, Dirlik, and narrow band approach, was applied to obtain equivalent load cycles. The stress response was obtained through having the equivalent load cycles with a spring modal frequency response function (FRF) and different stress criterion, like absolute maximum principal and critical plane approaches. Then, the stress response was used to predict the spring fatigue life using stress-life (S-N) approach. The results revealed that the harshest road condition was the rural road where the spring with fatigue life of 4.47 × 107 blocks to failure was obtained. The strain predicted fatigue life was used to validate the frequency-based predictions using a conservative approach. It was found that the Dirlik approach has shown the closest results to the strain life approach, which suggested that the Dirlik approach could be used for spring fatigue life prediction with the acceptable accuracy.

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Section: Parameters Valuementioning

confidence: 99%

Vibration Fatigue Analysis of Carbon Steel Coil Spring under Various Road Excitations

Kong

Abdullah

Schramm

et al. 2018

Metals

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“…After the road excitation signals were processed with the local regularity analysis, it can be observed that only the components in low frequency range of 0-50 Hz were remained in the bump signals. Previous study [7] remarked that the road excitations are classified as low frequency vibrations, with frequency range between 0 to 50 Hz. This strongly supports that the local-regularity-based bumps identification method proposed in this study is capable to extract bumps signal and eliminate unnecessary high frequency noises.…”

Section: Establishment Of Wavelet-energy Based Fatigue Life Predictivmentioning

confidence: 99%

“…The loading histories of coil spring resulted by road surface excitations often contain some spike-like transients, associated with sudden and large amplitude changes. These transients, also known as 'bumps', are originated from the irregular road surface such as the speed bumps and severe potholes [7]. Researchers supported that the bumps in loading histories are high amplitude cycles that contributed to a major portion of fatigue damage in suspension components especially the coil spring [8,9].…”

Section: Introductionmentioning

confidence: 99%

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

Chin¹,

Abdullah²,

Singh³

et al. 2020

IJIE

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This paper aims to study the identification of bumps in vibrational signals and develop bump-energybased durability predictive models for a suspension coil spring. The bump energy of the loading signal is affected by high frequency noises and can lead to inaccurate results. Therefore, it is necessary to eliminate high frequency noise during bump identification. Local regularity analysis was employed to determine the singular points in road signals. Bump signals were then reconstructed from these singular points. Subsequently, bump-energy-based models were developed by correlating with the fatigue lives estimated using the Coffin-Manson, Morrow and Smith-Watson-Topper strainlife models. The results show that the bump signals extracted from the road excitations had a frequency band within 0-50 Hz, indicating that the high frequency noises had been successfully removed during extraction of the bumps. The bump-energy-based models predicted a fatigue life ranging from 3.98x10 4 to 4x10 9 cycles within a 95% confidence interval, where the Coffin-Manson-based model showed the highest fatigue life. This is because the Coffin-Manson model did not consider the mean stress effects. When compared with the experimental results, the Coffin-Mansonbased model indicates the highest accuracy, given its highest R 2 of 0.948. The bump-energy-based models developed in this study contributed an accurate durability prediction of coil springs.

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“…Hence, the spring design, especially in terms of fatigue, must be determined before prototyping [5]. For durability analysis, the spring fatigue design process is usually conducted through strain data collection from various road conditions because this type of loading is realistic [6]. The actual loadings are important for warranty claims because the automotive suspension components should last at least five years [7].…”

Section: Introductionmentioning

confidence: 99%

Determining optimal suspension system parameters for spring fatigue life using design of experiment

Kong

Abdullah

Schramm

et al. 2019

Mechanics & Industry

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This paper presents the optimization of spring fatigue life associated with suspension system parameters using the design of experiment approach. The effects of suspension parameters on spring fatigue life were analyzed because this process can improve spring fatigue life from a distinct perspective. A quarter car model simulation was performed to obtain the force time histories for fatigue life prediction where the suspension parameters were adjusted. Multiple input regression and interaction plots were conducted to identify the interaction between these parameters. A full factorial experiment was performed to determine the optimal suspension settings that would maximize the spring fatigue life. For the regression, a high R 2 value of 0.9078 was obtained, indicating good fitting. The established regression showed normality and homoscedasticity for consistent prediction outcome. Reducing the spring stiffness and sprung mass while enhancing the damping coefficient is therefore suggested to enhance fatigue life.

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Mission profiling of road data measurement for coil spring fatigue life

Cited by 43 publications

References 17 publications

Vibration Fatigue Analysis of Carbon Steel Coil Spring under Various Road Excitations

Vibration Fatigue Analysis of Carbon Steel Coil Spring under Various Road Excitations

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

Determining optimal suspension system parameters for spring fatigue life using design of experiment

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