A Case Study on Durability Analysis of Automotive Lower Control Arm Using Self Transducer Approach

2018-01-1208

04/03/2018

Features
Event
WCX World Congress Experience
Authors Abstract
Content
A competitive market and shrinking product development cycle have forced automotive companies to move from conventional testing methods to virtual simulation techniques. Virtual durability simulation of any component requires determination of loads acting on the structure when tested on the proving ground. In conventional method wheel force transducers are used to extract loads at wheel center. Extracted wheel center forces are used to derive component loads through multi-body simulation. Another conventional approach is to use force transducers mounted directly on the component joineries where load needs to be extracted. Both the methods are costly and time-consuming. Sometimes it is not feasible to place a load cell in the system to measure hard point loads because of its complexities. In that case, it would be advantageous to use structure itself as a load transducer by strain gauging the component and use those strain values to extract hard point loads in virtual simulation.
In this paper, the author presents the self-transducer technique coupled with virtual iteration approach to extract loads on suspension lower control arm using RLDA strain data with a case study. Dynamic strains at multiple locations on lower control arm (LCA) are measured on different tracks of proving ground. Loads at the LCA hard points were extracted using virtual iteration approach with the help of FEMFAT LAB VI software. These loads were used to capture the LCA failure on the proving ground test. Author showcases the usefulness of the approach over the conventional approach in product development.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-1208
Pages
6
Citation
Sharma, S., Sahu, A., Londhe, A., and Kangde, S., "A Case Study on Durability Analysis of Automotive Lower Control Arm Using Self Transducer Approach," SAE Technical Paper 2018-01-1208, 2018, https://doi.org/10.4271/2018-01-1208.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-1208
Content Type
Technical Paper
Language
English