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Door mirrors have a major impact on wind noise observed at the driver's ear. The mirror distance and angle with respect to the front side glass will influence the front window buffeting characteristics of the vehicle as well. Optimizing the mirror angle to minimize or eliminate buffeting while maintaining acceptable wind noise performance can provide additional customer satisfaction. Changes to the mirror angle were investigated experimentally for both wind noise and buffeting effects. Experimental vehicle interior noise and buffeting data was taken at multiple yaw angles and wind speeds using a full scale aero acoustic wind tunnel. In addition, experimental wind noise attributes for the different mirror angles was also used to determine the optimal angle. The resulting angle measurement will be used as a best practice mirror angle for optimal wind noise and front window buffeting performance on future vehicle programs.

The staircase fatigue testing method is a recognized method for determining the fatigue limit of powertrain components. The purpose of this paper is to improve upon existing standards by adding common practices that will ensure a higher degree of statistical accuracy in the data. This includes specifying appropriate sample sizes, stress increments and initial load conditions, as well as making suggestions for appropriate methods of analyzing the data. Two methods (Dixon and Mood method and probit analysis method) are selected and compared in terms of relative percent difference on four parameters (mean, standard deviation, B10 fatigue strength and B50 fatigue strength). The staircase data are obtained by simulations from normal and lognormal fatigue limit distributions.

This paper describes a program of coastdown and wind tunnel tests conducted with the objective of establishing a correlation between the aerodynamic drag force measured at the Lockheed-Martin Low-Speed Wind Tunnel (LSWT) and that inferred from coastdown results on the test track. The result of this correlation establishes, in principle, the capability to project what the aerodynamic drag force inferred by a future coastdown test will be (for a future, as-yet unavailable property) based on a current database of wind tunnel results. The correlation is accompanied by a rigorous uncertainty analysis to assess the quality of the correlation and its supporting data.

Historically, pump noise can be a contributor to customer dissatisfaction with automatic transmissions. In this paper, a Shainin experiment was conducted to identify all probable root causes for pump noise on a production RWD transmission. Sample transmissions were selected following subjective evaluations. Noise was objectively measured in the lab using a microphone and an accelerometer. The study was conducted following a systematic Shainin statistical engineering methodology, which included the following major steps: selection of the test measure using the isoplot technique, selection of Best of Best (BOB) and Worst of Worst (WOW) transmissions, assessment of assembly variation, component search, and pair-wise comparisons. The study successfully highlighted the key variables on the drive gear involute profile, which are now being tightly controlled for improved noise characteristics.

In this paper, loads acting on driveline components during an entire proving ground (PG) durability schedule are used to demonstrate the methodology of optimizing driveline performance reliability using both physical and computational methods. It is well known that there is an effect of driver variability on the driveline component loads. Yet, this effect has not been quantified in the past for lack of experimental data from multiple drivers and reliable data analysis methods. This paper presents the data reduction techniques that are used to identify the extreme driver performance and to extrapolate the short-term measurement to long-term data for driveline performance reliability. The driveline loading variability is made evident in the rotating moment histogram domain. This paper also introduces the concept for a simulation model to predict the driveline component loads based on a complete proving grounds schedule. A model-to-test correlation is also performed in this paper.

During the initial vehicle design phase and as the first prototypes are built, extensive on-board instrumentation and data acquisition is required at the proving grounds (PG). The data is used for various types of testing and analysis. During this phase of development very few parts and assembly components are available for physical test. The objective is to develop a component test for the truck box. This test can be run without suspension parts during the early stages of the vehicle development. A further objective is to correlate the test to FEA models and actual Proving Ground full vehicle test results.

Novel testing procedures and analytical methodologies to assess the performance of hybrid electric vehicle batteries have been developed. Tests include both characterization and cycle life and/or calendar life, and have been designed for both Power Assist and Dual Mode applications. Analytical procedures include a battery scaling methodology, the calculation of pulse resistance, pulse power, available energy, and differential capacity, and the modeling of calendar- and cycle-life data. Representative performance data and examples of the application of the analytical methodologies including resistance growth, power fade, and cycle- and calendar-life modeling for hybrid electric vehicle batteries are presented.

Based on the results of “An Evaluation of the Mechanical Properties of Wheel Force Sensors and their Impact on to the Data Collected During Different Driving Manoeuvres” Herrmann et al. (SAE Paper 05M-254) a second, detailed investigation has been started to acquire additional information. In this previous investigation, it has been found out, that a difference in mass can be clearly identified in the signals. The current paper summarizes the results of a detailed investigation, which has been performed at DaimlerChrysler Stress Lab in Auburn Hills, with a fully equipped vehicle - a set of 2/4 Wheel Force Sensors plus several acceleration sensors as well. Through careful research and testing it is expected that the differences in the dynamic behavior can be specified with better accuracy than in the previous study.

Durability of automotive structures is a primary engineering consideration that is required to be assessed at every design and development stage. Due to limitations of the analytical and experimental tools, the current practice in the automotive industry is to conduct a new data acquisition over a proving ground schedule whenever there are changes in the suspension parameters. This is a time-consuming and expensive operation. This paper provides guidelines for product teams to determine if a new vehicle data acquisition is needed when there are changes in vehicle parameters, and the corresponding effect on Road Test Simulator (RTS) drive file development. The application of this methodology to a truck with and without tuned suspension parameters is described in detail.

A Draw Bead Simulator (DBS), with modified draw beads, was employed in this study to understand the springback behavior of sheet metal subjected to multiple bending-unbending cycles. The investigations were carried out in both the rolling and the transverse rolling directions on four types of materials: Electro-Galvanized DQ steel, light and heavy gauge Hot-Dip Galvanealed High Strength Steels, and Aluminum alloy AL6111. The sheet geometries, thickness strains, pulling forces and clamping forces were measured and analyzed for the purpose of establishing a benchmark database for numerical predictions of springback. The results indicate that the springback curvature changes dramatically with the die holding force. The conditions at which the springback is minimized was observed and found to depend on the material properties and the sheet thickness. Analysis with an implicit FEM showed that the predicted and the experimental results are in very good agreement.

The springback behavior might be different due to different gap clearances between die and punch. A study using FEA simulation is done to investigate the die gap effect. A 3D brick element and an explicit-implicit method are employed to investigate a few simple problems. A draw form, a crash form with an upper pad and a flange form are investigated separately. Numisheet’93 2D draw bending springback problem is also investigated using an explicit dynamic code. Comparisons between springback simulation results on several different die gaps are illustrated. The Kirchhoff assumption of C° shell element and the Mindlin/Love assumption of thin shell element are also examined on different cases. A case study then is performed on a rail type panel. Conclusions and recommendations for future studies are summarized.

An extensive digital simulation study on lift devices that interact with human operators in DaimlerChrysler automotive assembly plants has been initiated and deployed. This digital mock-up of human-machine workcells is to scientifically evaluate and further certify a number of typical commercial lift devices that are served in car-assembly operations. The entire model is based on human biomechanical Jacobian relationship, as a fundamental kinematic structure, to predict human body instantaneous joint-torque distribution when the human is working with a certain payload. The developed modeling and simulation system will play a pivotal role in proactive ergonomic prediction, verification and digital certification in car advance manufacturing engineering processes.

A springback study on three rail type panels is summarized. Numisheet'96 S-rail, A/S P rail II and a Daimlerchrysler rail are presented with experiment data and FEA simulation predictions. The details of the measurement on experiment samples and simulation models are illustrated. The comparison between the experiment data and the simulation results from four different softwares is made on separate cases. The correlation between experiment data and simulation results is analyzed.

An assessment of stamped part quality and launch readiness occurs at many intervals. This paper will focus on dimensional control activities that take place after Stamping Dies are constructed, but prior to producing the stamped parts. Die certification and die repeatability measurements have been performed at DaimlerChrysler and the results are documented. This die certification process provides an opportunity to uncover and resolve die machining issues with respect to the part math model or pre-engineered compensation model prior to producing parts. Additionally, the die repeatability process is performed to determine the ability of the die gaging to locate the incoming in-process material consistently. This paper will explain the die certification and die repeatability processes and share what we have learned. It will describe the processes, the tools, the participants, the sites, the benefits, and the measurement equipment.

In this paper, responses from a vehicle's suspension, chassis and body, are used to demonstrate a methodology to optimize physical test results. It is well known that there is a variability effect due to an increase of wheel unsprung mass (due to loads measurement fixturing), tire pressure, speed, etc. This paper quantifies loading variability due to Wheel Force Transducer (WFT) unsprung mass by using a rainflow cycle counting domain. Also, presents a proving ground-to-test correlation study and the data reduction techniques that are used in road simulation test development to identify the most nominal road load measurement. Fundamental technical information and analytical methodology useful in overall vehicle durability testing are discussed. Durability testing in a laboratory is designed to correlate fatigue damage rig to road. A Proving Ground (PG) loading history is often acquired by running an instrumented vehicle over one or more PG events with various drivers.

The recent trends of increasing driveline torque and use of traction control devices call for increasingly higher durability capacity from driveline components. Bench and vehicle durability tests are often used to validate designs, but they are not cost-effective and take months to complete. Traditional finite element analysis (FEA) procedures have been used effectively in the re-design of driveline components to reduce stress, and occasionally, to predict fatigue life. But in the case of certain rotating components, such as the Axle Differential Case, where the component sees large stress/strain fluctuations within the course of one complete rotation, even under constant input torque, historical fatigue analysis (when conducted) yields very conservative results. The axle differential case tends to be one of the weakest links in the rear axle assembly. Therefore, there is a crucial need for analytical methods to more accurately predict fatigue life to reduce testing time and cost.