Utilizing Finite Element Tools to Model Objective Seat Comfort Results

Paper #:
  • 2012-01-0074

Published:
  • 2012-04-16
Citation:
Dooge, D., Cabane, C., Marca, C., Dwarampudi, R. et al., "Utilizing Finite Element Tools to Model Objective Seat Comfort Results," SAE Technical Paper 2012-01-0074, 2012, https://doi.org/10.4271/2012-01-0074.
Pages:
7
Abstract:
The comfort assessment of seats in the automotive industry has historically been accomplished by subjective ratings. This approach is expensive and time consuming since it involves multiple prototype seats and numerous people in supporting processes. In order to create a more efficient and robust method, objective metrics must be developed and utilized to establish measurable boundaries for seat performance.Objective measurements already widely accepted, such as IFD (Indentation Force Deflection) or CFD (Compression Force Deflection) [1], have significant shortcomings in defining seat comfort. The most obvious deficiency of these component level tests is that they only deal with a seats' foam rather than the system response. Consequently, these tests fail to take into account significant factors that affect seat comfort such as trim, suspension, attachments and other components. The only way to take these factors into account is to perform a sub-system level test on a complete seat assembly.This project focuses on the finite element modeling of the Hardness Distribution test [2]. Defining the force-deflection behavior is a critical portion of embedding performance requirements into the seat development process. By utilizing the PAM-COMFORT simulation tool & methodology, hardness distribution and force-deflection can be predicted. This approach allows for objective seat performance parameters to be analyzed prior to physical prototypes.The following study was designed to: (1) demonstrate the benefits of manufacturing the seat utilizing finite element methods, (2) determine the relative error between analytical and experimental hardness distribution results, and (3) identify future model improvement and growth capability.
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