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An automotive cockpit module is a complex assembly, which consists of components and sub-systems. The critical systems in the cockpit module are the instrument panel (IP), the floor console, and door trim assemblies, which consist of many plastic trims. Stiffness is one of the most important parameters for the plastic trims' design, and it should be optimum to meet all the three functional requirements of safety, vibration and durability. This paper presents how the CAE application and various other techniques are used efficiently to predict the stiffness, and the strength of automotive cockpit systems, which will reduce the product development cycle time and cost. The implicit solver is used for the most of the stiffness analysis, and the explicit techniques are used in highly non-linear situations. This paper also shows the correlations of the CAE results and the physical test results, which will give more confidence in product design and reduce the cost of prototype testing.

A semi-empirical engine piston/ring assembly friction model based on the concept of the Stribeck diagram and similarity analysis is described. The model was constructed by forming non-dimensional parameters based on design and operating conditions. Friction data collected by the Fixed-Sleeve method described in [1]* at one condition, were used to correlate the coefficient of friction of the assembly and the other non-dimensional parameters. Then, using the instantaneous cylinder pressure as input together with measured and calculated design and operating parameters, reasonable assembly friction and fmep predictions were obtained for a variety of additional conditions, some of which could be compared with experimental values. Model inputs are component dimensions, ring tensions, piston skirt spring constant, piston skirt thermal expansion, engine temperatures, speed, load and oil viscosity.

Ring gear bolts in differentials are often modified in size to accommodate the additional clamp load that is required due to an increase in torque from a vehicle's powertrain. Depending on a given program several constraints need to be considered. These include cost, validation time, reliability / durability and timing for implementation. In this paper, a Finite Element Analysis (FEA) procedure for analyzing stresses in ring gear bolts within a rear differential assembly is outlined and the computational results are then compared to quasi-static bench test results that were developed to measure bending and tension loads in the ring gear bolts during loading and unloading of the axle pinion. A dynamometer test is then developed to duplicate the failure mode and provide a comparison of the design changes proposed and the expected improvement in durability.

Coordinate measurement gages dominate in the area of dimensional control and variation reduction of automotive body assembly processes. However, coordinate measurement gages do not have the capability to track certain measured features. This incapability introduces inherent measurement error created by part (feature) mislocation in constrained non-measured directions. This inherent measurement error weakens the methods used for process control and variation reduction. In this paper, a principle of measurement uncertainty is developed in order to estimate the measurement error caused by this deficiency. The developed principle describes measurement error, which is independent of any other error related to the mechanical or optical coordinate measurement machines (CMMs, OCMMs). Additionally, an error map determined by the measurement uncertainty principle is created for error compensation.

When performing vibration analysis of complex vehicle structures, it is often important to be able to evaluate the effects of design changes in one or more substructures (e.g., for design optimization). It may also be convenient to allow different components to be modeled independently by different groups or organizations. For both cases, it is inevitable that some substructures will have non-matching finite element meshes at the interface where they are physically connected. Thus, a key challenge is to be able to handle the dynamic assembly of components with non-matching meshes and the subsequent global vibration analysis in a systematic and efficient manner. To tackle this problem, the enhancement of component mode synthesis methods for handling finite element models partitioned into non-matching substructures is considered in this paper. Some existing methods are reviewed, and new methods are developed.

A snap-fit is a form-fitting joint, which is used to assemble plastic parts together. Snap-fits are available in different forms like a projecting clip, thicker section or legs in one part, and it is assembled to another part through holes, undercuts or recesses. The main function of the snap-fit is to hold the mating components, and it should withstand the vibration and durability loads. Snap-fits are easy to assemble, and should not fail during the assembling process. Based on the design, these joints may be separable or non-separable. The non- separable joints will withstand the loads till failure, while separable joints will withstand only for the design load. The insertion and the retention force calculation for the snaps are very essential for snap-fit design. The finite element analysis plays a very important role in finding the insertion and the retention force values, and also to predict the failure of the snaps and the mating components during this process.

Dimensional variation simulation is a computer aided engineering (CAE) method that analyzes the statistical efforts of the component variation to the quality of the final assembly. The traditional tolerance analysis method and commercial CAE software are often based on the assumptions of the rigid part assembly. However, the vehicle functional attributes, such as, ride and handling, NVH, durability and reliability, require understanding the assembly quality under various dynamic conditions while achieving vehicle dimensional clearance targets. This paper presents the methods in evaluating and analyzing the impacts of the assembly variations for the vehicle dynamic performance. Basic linear tolerance stack method and advanced study that applies various CAE tools for the virtual quality analysis in the product and process design will be discussed.

Tuned mass dampers (TMDs) or vibration absorbers are widely used in the industry to address various NVH issues, wherein, tactile-vibration or noise mitigation is desired. TMDs can be classified into two categories, namely, tuned-to-resonance and tuned-to-discrete-excitation. An overwhelming majority of TMD applications found in the industry belong to the tuned-to-resonance category, so much of information is available on design considerations of such dampers; however, little is published regarding design considerations of dampers tuned-to-discrete-excitation. During this study, a problem was solved that occurred at a discrete excitation frequency away from the primary resonance frequency of a steering column-wheel assembly. A solution was developed in multiple stages. First the effects of various factors such as mass and damping were analyzed by using a closed-form solution.

The new Chrysler six-speed transaxle makes use of an underdrive assembly to extend a four-speed automatic transmission to six-speed. It is achieved by introducing double-swap shifts. During double-swap shift, learning the initial clutch torque capacity of the underdrive assembly's subsystem has a direct impact on the shift quality. A new method is proposed to compute and learn the initial clutch torque capacity of the releasing element. In this paper, we will outline a new mathematical method to compute and learn the accurate starting point of the clutch torque capacity for double swap shift control. The performance of the shift is demonstrated and the importance of the adaptation to shift quality is highlighted. An nth order lookup table is presented; this table contains n rows and m columns. Every row defines a relationship between the dependent variable such as actuator duty cycle and one independent variable such as transmission oil temperature, input torque or battery voltage.