Abstract
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.
Highlights
The control and stability of a ground vehicle depends on the friction between road surfaces and tyres where it has been reported to fluctuate rapidly [1,2]
The elastic-energy-based model converted the acceleration into strain data for predicting fatigue life of an automobile coil spring
The elastic-energy model generated strain signals consisting of statistical properties in the same range, with measured data of predicted fatigue lives ranging from 1.37 × 105 to 8.13 × 108 blocks to failure
Summary
The control and stability of a ground vehicle depends on the friction between road surfaces and tyres where it has been reported to fluctuate rapidly [1,2]. Metals 2019, 9, 213 lag correlation function has been introduced to create artificial data, and the generated stochastic road profile was used to optimize the passive suspension system of a car. Until recently, another loading profile generation method using the test tailoring approach has been proposed by Xu et al [7]. Marzbanrad et al optimized a passive suspension system to achieve minimum vehicle pitch angle using approximately-generated road data [14] Another optimization research on semi-active suspension was based on suspension deflection, which utilized a step input road disturbance [15]. The strain converted time and displacement data can be used to study the vehicle oscillation and fine-tune the vehicle suspension system design
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