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The impact response of foam is investigated using Finite Element Analysis (FEA). A procedure will be described for determining the material constants used in the FEA material models. The procedure uses compressive uniaxial, force versus displacement, and triaxial, pressure versus volume-change, data. After the material model is constructed using the uniaxial and triaxial data, FEA is used to predict the results of a free-moving-mass striking rigidly backed foam. The limitations of the current material models are also addressed.

Basic procedures are described for the design and development of flexible drive plates that couple automatic transmissions to engines. An innovative combination of analysis and test techniques were employed during the development of a drive plate for a turbocharged diesel truck engine when premature failures occurred. FEA (finite element analysis) was expanded from use as a preliminary design tool to the prediction of high stress conditions and the loading that caused them. A laboratory test was developed to rapidly assess drive plate design changes based on these FEA predictions.

To understand how the passenger compartment cavity interacts with the surrounding panels (roof, windshield, dash panel, etc) a numerical panel contribution analysis was performed using FEA and BEA techniques. An experimental panel contribution analysis was conducted by Reiter Automotive Systems. Test results showed good correlation with the simulation results. After gaining some insight into panel contributions for power train noise, an attempt was made to introduce beads in panels to reduce vibration levels. A fully trimmed body structural-acoustic FEA model was used in this analysis. A network of massless beam elements was created in the model. This full structural-acoustic FEA model was then used to determine the optimal location for the beads, using the added beams as optimization variables.

Starting in 1965 and continuing through 1972, the Radio Committee of the Motor Vehicles Manufacturers Association (MVMA) has been the coordinator of a number of electromagnetic research projects. These investigations have included extensive applications of the updated SAE Standard, Measurement of Electromagnetic Radiation From Motor Vehicles (20-1000 MHz)-SAE J551a. Furthermore, there were joint testing programs with the Electronic Industries Association which encompassed measuring degradation in the performance of Land Mobile Radio Service receivers resulting from varying levels of impulsive-type radiation from motor vehicles. In addition, efforts were expended in using statistical approaches for testing a number of hypotheses covering a conversion of impulsive vehicle noise data to the interference potential to Land Mobile receivers.

This paper describes techniques that predict and analyze dynamic response of vehicles traversing random rough surfaces. Road irregularities are statistically classified by frequency and amplitude distribution. This classification determines the nature of random inputs to mathematical vehicle models and allows computer prediction of dynamic response of a simulated vehicle. Once inputs and models are defined, parametric analysis with output criteria specified statistically can be performed. This allows prediction of vehicle riding quality and evaluation of design concepts. Statistical analysis of accelerometer measurements on actual vehicles permits verification of the design process and meaningful comparison between vehicles.

Present techniques for classification and analysis of surface roughness are based on a trace of surface profile, or a measure of arithmetic mean, or rms value of the profile height, but this information is not adequate, and a new technique has been developed to classify surface roughness based on frequency content of the variance of the surface profile. A digital computer frequency filter has been devised to allow differentiation between roughness height and waviness or general surface contour. Three representative surfaces have been measured and classified according to this analysis and data are presented in support for roughness classification techniques.

This paper presents a method of accounting for sample variability and sample size in establishing the acceptable bogey levels. The technique makes use of the statistical tolerance theory which accounts for the variability of the sample mean and standard deviation by determining a K-factor adjusted for sample size. The result is a tolerance that is reasonably assumed to cover a specified fraction of the population of parts. The technique, although not as simple as a fixed bogey, does discriminate between designs with different levels of energy management robustness.

A highly competitive market and increased emphasis on quality have gear designers searching for additional tools to produce accurate gearsets in a condensed timeframe. To meet this challenge, a Graphics Engineering method has been developed to enhance traditional gear design techniques. Graphics Engineering links interactive graphics, finite element analysis and solid modeling into a graphics/analysis development package. Starting with gear and cutter data derived by conventional techniques, it provides cutter paths and involute profiles for geometry, strength, and physical property analysis. The comprehensive data obtained through Graphics Engineering provides a powerful tool for the gear designer to increase gearset accuracy and reduce design iterations.