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In this paper, we study the sensitivity of a vehicle impact harshness (IH) performance to the suspension bushing rates. A mid-sized uni-body SUV is selected for this study, with the acceleration responses at the driver seat track and the steering wheel as objective functions. A sensitivity study is conducted using an ADAMS full vehicle model including a tire model and flexible body structure representation over an IH event. The study resulted in the identification of key bushings that affect the IH performance and its sensitivity to the bushing rates. Based on the results, we came-up with an “optimal” bushing set that minimizes impact harshness, which was subjectively verified to result in significant improvement in IH.

Laser lap welding quality is a non-linear response based on a host of categorical and numeric material and process variables. This paper describes a Grammatical Evolution approach to the structure identification of the laser lap welding process and compares its performance with linear regression and a neuro-fuzzy inference system.