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Since their introduction in automobiles, polymeric materials have enabled designers and engineers to differentiate products based on performance attributes, mechanical response, aesthetics, and manufacturing techniques. A large segment of these applications utilizes polypropylene (PP) resins. One of the attractive features of PP polymers is the ability to tailor their mechanical, thermal and processing performance envelope via modification of their composition and the addition of fillers. Key to the successful application of PP resins in structural systems is the ability of designers and engineers to understand the material response and to properly model the behavior of PP structures upon different mechanical and thermal loading conditions.

To meet automotive legal, consumer and insurance test requirements, the process for designing energy absorption countermeasures usually comprises Finite Element simulations of the specified test. Finite element simulations are used first to see if there is a need for an Energy Absorption countermeasure at all and if so, what type, material and shape. A widely used class of energy absorption countermeasures in automotive interior applications is honeycomb extruded polypropylene foams (HXPP). Under compression, these foams exhibit a constant plateau stress until late densification. This enables these foams to minimize packaging space for a given amount of energy to be absorbed or maximize energy absorption for a given packaging space. Robust and easy to use isotropic CAE material models have been developed for HXPP, however the true material properties are anisotropic and such a material model could be necessary in some cases.

This paper describes the design, synthesis-analysis and development of the unique vehicle structure architecture for the fifth generation Chevrolet Corvette, ‘C5’, which starts in the 1997 model year. The innovative structural layout of the ‘C5’ enables torsional rigidity in an open roof vehicle which exceeds that of all current production open roof vehicles by a wide margin. The first structural mode of the ‘C5’ in open roof configuration approaches typical values measured in similar size fixed roof vehicles. Extensive use of CAE and a systems methodology of benchmarking and requirements rolldown were employed to develop the ‘C5’ vehicle architecture. Simple computer models coupled with numerical optimization were used early in the design process to evaluate every design concept and alternative iteration for mass and structural efficiency.

Side mirror design optimization has been traditionally accomplished using an iterative design and testing process, often resulting in considerable costs in retooling operations, and causing significant delays in production. The ever increasing demand for high quality products has led major automotive companies to set high performance standards for side mirrors. These include structural, vibration, and aesthetic criteria, which affect the functionality and appearance of the mirror. The first two issues have been successfully addressed here using a fundamental design philosophy and a computer aided engineering approach. This approach has helped improve product quality, reduce the costs associated with redesign and retooling and minimize the development cycle time from concept to production.

Robust optimization usually requires numerous functional evaluations, which is not feasible when the functional evaluation is time-consuming. Examples in automobile industry include crash worthiness/safety and fatigue life simulations. In practice, a response surface model (RSM) is often used as a surrogate to the CAE model, so that robust optimization can be carried out. However, if the error in the RSM is significant, the solution based on the RSM can be invalid. This paper proposes a method of finding multiple candidate solutions, all of which have similar predicted performances. This approach is effective in finding the close-to-optimum solutions when the model has error, and providing design alternatives. Examples are provided to illustrate the method.

Since 1997, General Motors Body Manufacturing Engineering - Die Engineering Services (BME-DES) has been working jointly with our software vendor to develop and implement a parallel version of stamping simulation software for mass production analysis applications. The evolution of this technology and the insight gained through the implementation of DMP/MPP technology as well as performance benchmarks are discussed in this publication.

Free form fabrication, or rapid prototyping as it is commonly known, is the creation of a physical entity, directly from numerical description, using an additive process. The mathematical data used is typically in the form of a 3D CAD file, but it may also be obtained from a reverse engineering process. This paper presents a review of three of the leading FFF (free form fabrication) systems which are commercially available. Time constraints will allow us to describe only three of these products. Although this does not do justice to a technology where there are more than 30 different systems in various stages of development, these examples represent the vast majority of machines which are in the marketplace today.

In structural Instrument Panels the conventionally used cross car beam is eliminated by using the plastic structure as a load carrying construction. Due to the continuous search for lowering costs and weight in the development of new cars, the concept has been applied a number of times. Many articles have been published since on this subject, describing the design concepts, engineering development and types of plastic material applied. In general, the structural instrument panel assemblies show to have substantially lower cost and weight compared with conventional cross car beam based instrument panel structures while all of performance requirements are met. Also, improved packaging space, reduction in assembly time and improved recyclability are seen as major advantages. The use of state of the art Computer-Aided Engineering (CAE) has proved to reduce development time and costs.

Lack of good sealing of the interfaces between the various components of the automotive powertrain can affect its functionality, performance and efficiency. The performance characteristics of a gasketted or sealed joint (or assembly) in the powertrain, are its sealability, the strength of the flanges under assembly loads and the long term bolt torque retention under material degradation due to creep. A CAE approach has been presented to design, evaluate and optimize automotive powertrain gaskets, seals and gasket/seal assemblies under assembly conditions. In this approach, the shape of the gasket/seal cross-section is optimized to maximize sealing pressure, and the gasket/seal assembly is optimized for its performance characteristics. The interactions between the gasket/seal and the flange materials under assembly loads govern the performance of the gasket/seal assembly.

Seat assessment is an important necessity for the growing auto industry. The design of seats is driven by customer’s demand of comfort and aesthetics of the vehicle interiors. Some of the few seat assessments are H-point prediction with H-point Machine (HPM); backset prediction with Head Restraint Measuring Device (HRMD); seat hardness and softness. Traditional seat development was through developing series of prototypes to meet requirements which involved higher costs and more time. The seat requirement of H-Point measurement is of focus in this paper. Though there are other commercial available software/methods to perform the H-point measurement simulations, the aim here was to assess the capabilities of an alternate Computer Aided Engineering (CAE) methodology using CAE tools - PRIMER and LS-Dyna. The pre-processing tools - Hypermesh and ANSA have been used for modeling and Hyperview tool used for reviewing the simulations.

When using a math model to estimate the failure rate of a product, or the mean and standard deviation of performance characteristics of the product, one important issue is the accuracy of the estimates. All math models have error. This error will be transmitted to the error in the estimates of failure rate, mean, and standard deviation. This paper presents a method to calculate the bounds on the transmitted error, which can then be used to 1) obtain confidence bounds on estimates of mean, standard deviation, and failure rate; and 2) establish accuracy requirements on math models.

An approach to a solution to conflicts of interest posed by new pedestrian safety requirements is presented here. The effects of various design parameters on pedestrian safety, and the resulting influence on other requirements are examined. Limitations and possible solution envelopes are determined with regard to styling, packaging and functionality. Material choice and the stiffness of the structure are used as variables to fine-tune the system. The paper explores the effects of using current front-end materials and new material options versus what can be achieved by modifying or developing designs and structures to fulfil the set of conflicting requirements. Computer Aided Engineering (CAE) techniques are used extensively for this work, in order to determine the sensitivity of the behaviour of front-end systems to design and material characteristics.

The Alliance of Automotive Manufacturers (Alliance) has produced a document in which Principle 1.4 gives criteria and methods for calculating downvision angles to navigation and telematics displays in vehicles. This paper describes the details of the criteria and methods for determining compliance. Visual displays placed high in the vehicle instrument panel help drivers to use their peripheral vision to monitor the roadway for major developments, even during brief glances to the display. The Alliance has developed two criteria to define the maximum allowable downward viewing angle for displayed information in North American vehicles. One criterion is for use in two-dimensional Computer Aided Design (CAD) analyses, and one is for use in three-dimensional CAD analyses. Alliance Principle 1.4 is consistent with known driver performance research data, and known facts about the peripheral sensitivity of the human visual system.