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Silent Block Bush Design and Optimization for Pick-Up Truck Leaf Spring
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Structural elastomer components like bushes, engine mounts are required to meet stringent and contrasting requirements of being soft for better NVH and also be durable at different loading conditions and different road conditions. Silent block bushes are such components where the loading in radial direction of bushes are high to ensure the durability of bushes at high loads, but has to be soft on torsion to ensure good NVH. These requirements present with unique challenge to optimize the leaf spring bush design, stiffness and material characteristics of the rubber.
Traditionally, bushes with varying degree of stiffness are selected, manufactured and tested on vehicle and the best one is chosen depending on the requirements. However, this approach is costly, time consuming and iterative.
In this study, the stiffness targets required for the bush were analysed using static and dynamic load cases using virtual simulation (MSC.ADAMS). Simultaneously, the Road Load Data (RLD) acquired for specific test track was run for to get the loads and cycles on the bush. The data was refined with further runs to get the durability test parameters with load and cycles. Based on the above stiffness and load cycle calculation, elastomer profile were finalised and the material property was chosen to meet all the required performance and durability parameters. With these parameters, a bush was manufactured and found to meet all the performance and durability requirements in vehicle level. This approach, hence, reduced the lead-time and cost involved in development and an optimised design was done without iterations.
CitationHatekar, H., Anthonysamy, B., Saishanker, V., Pavuluri, L. et al., "Silent Block Bush Design and Optimization for Pick-Up Truck Leaf Spring," SAE Technical Paper 2017-01-0455, 2017, https://doi.org/10.4271/2017-01-0455.
Data Sets - Support Documents
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