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A displacement-based CAE analysis is applied to liftgate chucking noise problems. A CAE simulation model of a small-size sport utility vehicle (SUV) is simulated with a set of realistic road loads as a time transient simulation. The model contains a trimmed vehicle, a liftgate and structural body-liftgate interface components such as the latch-striker wire, contact wedges and slam bumpers. Simulation design of experiments (DOE) is carried out with the model. As performance measures, the relative displacements at the contact points of the interface components are selected, since they are considered the direct cause of liftgate chucking. As design variables, body structure stiffness, liftgate stiffness, liftgate opening stiffness, stiffness characteristics of the interface components and additional liftgate mass are selected. Results of the simulation DOE is post-processed, and response surface models (RSM) are fit for the performance measures.

The present study defines the functional requirements for a liftgate and the body in order to avoid rattle, squeak, and other objectionable noises. A Design For Six Sigma (DFSS) methodology was used to study the impact of various constraint components such as bumpers, wedges, and isolated strikers on functional requirements. These functional requirements include liftgate frequency, acoustic cavity frequency, and the stiffness of the liftgate body opening. It has been determined that the method of constraining the gate relative to the body opening has a strong correlation to the noise generated. The recommended functional performance targets and constraint component selection have been confirmed by actual testing on a vehicle. Recommendations for future liftgate design will be presented.

The Hood ajar sensing system provides customer feedback regarding the latch positional state of hood. If the sensing system is not robust to variation due to manufacturing, thermal conditions, and assembly, diagnostic failures can result. Executing various elements of the design for six sigma process can reduce the potential for diagnostic failures. This paper presents a method for achieving quality improvements by developing transfer functions, and using them for sensitivity and variance analysis. Control parameters were optimized to minimize non-conformal situations in the presence of various noise conditions.