Multi-Disciplinary Robust Optimization for Performances of Noise & Vibration and Impact Hardness & Memory Shake 2009-01-0341
This paper demonstrates the benefit of using simulation and robust optimization for the problem of balancing vehicle noise, vibration, and ride performance over road impacts. The psychophysics associated with perception of vehicle performance on an impact is complex because the occupants encounter both tactile and audible stimuli. Tactile impact vibration has multiple dimensions, such as impact hardness and memory shake. Audible impact sound also affects occupant perception of the vehicle quality. This paper uses multiple approaches to produce the similar, robust, optimized tuning strategies for impact performance. A Design for Six Sigma (DFSS) project was established to help identify a balanced, optimized solution. The CAE simulations were combined with software tools such as iSIGHT and internally developed Kriging software to identify response surfaces and find optimal tuning. Multiple methods were employed because:
The simulation and optimization tools have inherent advantages and disadvantages.
The problem of impact performance requires a cross-functional approach for balanced solution.
Multiple approaches facilitate evaluation of which approaches work best.
The project showed that two different approaches yielded very similar results. Greater understanding of vehicle sensitivities to component properties through response surfaces and transfer functions were established. An innovative memory shake matrix was developed for future vehicle development. It is imperative to balance the ride & handling and noise & vibration for robustness of the vehicle performance.
Citation: Bennur, M., Hogland, D., Abboud, E., Wang, T. et al., "Multi-Disciplinary Robust Optimization for Performances of Noise & Vibration and Impact Hardness & Memory Shake," SAE Technical Paper 2009-01-0341, 2009, https://doi.org/10.4271/2009-01-0341. Download Citation
Mallikarjuna Bennur, Derek Hogland, Edward Abboud, Thomas Wang, Mathew Rudnick