Development of multiple linear regression-based models for fatigue life evaluation of automotive coil springs

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

doi:10.1016/j.ymssp.2018.09.007

Cited by 49 publications

(23 citation statements)

References 45 publications

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“…The analysis of strain data in the automotive industry is challenging and significant because each of the data is long and valuable [48,49]. Hence, the development of a durability model has been aggressively performed, such as regression models for fatigue life prediction [50]. More recently, strain data was also generated through a multi-body dynamics model for automotive suspension fatigue analysis [16].…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Energy-Based Model Generated Strain Signals for Carbon Steel Spring Fatigue Life Assessment

Kong

Abdullah

Schramm

et al. 2019

Metals

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This paper presents the evaluation of the automobile coil-spring strain-displacement relationship for strain signals generation and fatigue life predictions. The development of a strain and spring vertical displacement relationship is significant because measuring vehicle wheel displacements and forces are complex and costly. Hence, there is a need to estimate the strain data using alternative measurement, such as vibration signals. In this analysis, strain and acceleration data were collected from a vehicle that has travelled on different road conditions. Through the material elastic strain energy and spring potential energy relationship, a coil-spring parameterise strain-displacement relationship has been developed and evaluated using a scatter band and correlation approach. Using this proposed model, the strain time histories were obtained based on acceleration data. For fatigue life analysis, most of the predicted fatigue life was distributed in the acceptable range using the scatter band approach where the data correlated at coefficient of determination value (R2) of 0.8788. With a suitable correlation value, this analysis proposed an alternative strain generation method for suspension coil spring fatigue life prediction, which could significantly shorten the spring development time.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Energy-Based Model Generated Strain Signals for Carbon Steel Spring Fatigue Life Assessment

Kong

Abdullah

Schramm

et al. 2019

Metals

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“…In current study, the vertical acceleration of car wheel was measured to represent the vibrational loading [15]. To capture the vibrational excitations from the road, an accelerometer was placed on the lower suspension arm.…”

Section: Road Excitation and Strain Histories Acquisitionmentioning

confidence: 99%

“…The fatigue life of the coil spring was predicted from the strain time histories using strain-life method. This study employed strain-life models because of the localised plastic deformation was significant on fatigue behaviour of the coil spring which had small geometry [15]. The Coffin-Manson, Morrow, and Smith-Watson-Topper (SWT) strain-life models were considered in this study.…”

Section: Establishment Of Wavelet-energy Based Fatigue Life Predictivmentioning

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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“…Multiple linear regressions have a wide range of applications from chemistry, e.g., calibration methods [62], to material science (an example would be fatigue life evaluation as in [63]). In the marine environment, MLR are also applied successfully.…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Determination of Flow Parameters of a Water Flow Around an AUV Body

Hoth

Kowalczyk

2019

Robotics

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Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. AUVs not only perform a given task but also adapt to changes in the environment, e.g., sudden side currents, downdrafts, and other effects which are extremely unpredictable. To navigate properly and allow simultaneous localisation and mapping (SLAM) algorithms to be used, these effects need to be detected. With current navigation systems, these disturbances in the water flow are not measured directly. Only the indirect effects are observed. It is proposed to detect the disturbances directly by placing pressure sensors on the surface of the AUV and processing the pressure data obtained. Within this study, the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested. This is based on CFD simulations using pressure data from specified points on the surface of the AUV. It is shown that support vector machines are most suitable for the given task and yield excellent results.

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Development of multiple linear regression-based models for fatigue life evaluation of automotive coil springs

Cited by 49 publications

References 45 publications

Evaluation of Energy-Based Model Generated Strain Signals for Carbon Steel Spring Fatigue Life Assessment

Evaluation of Energy-Based Model Generated Strain Signals for Carbon Steel Spring Fatigue Life Assessment

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

Determination of Flow Parameters of a Water Flow Around an AUV Body

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