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In this paper a design methodology for automotive heat exchangers has been applied which brings robustness into the design process and helps to optimize the design goals: as to maintain an optimal coolant temperature and to limit the vehicle underhood air temperature within a tolerable limit. The most influential design factors for the heat exchangers which affect the goals have been identified with that process. The paper summarizes the optimization steps necessary to meet the optimal functional goals for the vehicle as mentioned above. Taguchi's [1] Design for Six Sigma (DFSS) methods have been employed to conduct this analysis in a robust way.

A D.C. permanent magnet motor powered fan can serve as a cooling package air flow meter. This allows for continuous air flow monitoring during vehicle operation with applications to more precise air flow control schemes. In the freewheel mode, the air flow is a linear function of the open circuit voltage of the motor. In the powered mode, the motor voltage and current can be used with a motor and fan model to predict fan air flow. The model is explained and verifying test results are presented. Comparison of the accuracy and complexity vs. that of arrays of precision anemometers is provided.

In this paper, a transient thermal analysis model for Diesel fuel systems is presented. The purpose of this work is to determine the fuel temperature at various locations along the system, especially inside the tank and at the returned fuel inlet to the tank. Due to the fact that the fuel level is continuously changing during any driving condition, the fuel mass inside the tank is also continuously changing. Consequently, the fuel temperature will change even under steady driving or idle conditions, therefore, this problem should be analyzed using transient thermal analysis models. Effective thermal management requires controlling the surface temperature of the fuel tank, fuel lines and the fuel temperature at the fuel return line as well as inside the tank [1, 2]. Based on the thermal analysis results, it is possible to determine the major source of heat input at several locations of the fuel system.

Carburized parts often see use in powertrain components for the automotive industry. These parts are commonly quenched and tempered after the carburizing process. The present study compared the austempering heat treatment to the traditional quench-and-temper process for carburized parts. Samples were produced from SAE 8620, 4320, and 8822 steels and heat treated across a range of conditions for austempering and for quench-and-tempering. Distortion was examined through the use of Navy C-Ring samples. Microstructure, hardness, and Charpy toughness were also examined. X-ray diffraction was used to compare the residual stress found in the case of the components after the quench-and-temper and the austempering heat treatments. Austempering samples showed less distortion and higher compressive residual stresses, while maintaining comparable hardness values in both case and core. Toughness measurements were also comparable between both processes.

This paper introduces a methodology to calculate confidence bounds for a normal and Weibull distribution using Monte Carlo pivotal statistics. As an example, a ready-to-use lookup table to calculate one-sided lower confidence bounds is established and demonstrated for normal and Weibull distributions. The concept of one-sided lower tolerance limits for a normal distribution was first introduced by G. J. Lieberman in 1958 (later modified by Link in 1985 and Wei in 2012), and has been widely used in the automotive industry because of the easy-to-use lookup tables. Monte Carlo simulation methods presented here are more accurate as they eliminate assumptions and approximations inherent in existing approaches by using random experiments. This developed methodology can be used to generate confidence bounds for any parametric distribution. The ready-to-use table for the one-sided lower tolerance limits for a Weibull distribution is presented.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

In this paper, the development of random vibration testing schedules for durability design verification of engine mounted products is presented, based on the equivalent fatigue damage concept and the 95th-percentile customer engine usage data for 150,000 miles. Development of the 95th-percentile customer usage profile is first discussed. Following that, the field engine excitation and engine duty cycle definition is introduced. By using a simplified transfer function of a single degree-of-freedom (SDOF) system subjected to a base excitation, the response acceleration and stress PSDs are related to the input excitation in PSD, which is the equivalent fatigue damage concept. Also, the narrow-band fatigue damage spectrum (FDS) is calculated in terms of the input excitation PSD based on the Miner linear damage rule, the Rayleigh statistical distribution for stress amplitude, a material's S-N curve, and the Miles approximate solution.

The first set of SAE J2643 Standard Reference Elastomers (SRE) was developed in 2004. It was composed of a group of 10 compounds covering multiple elastomer families. Since then, more advanced materials from many elastomer families have been introduced to the automotive industry. The purpose of this study is to add a few more reference compounds to SAE J2643, to enhance the portfolio on FKM, AEM and ACM to reflect advancements in elastomer technology, and make it suitable for a variety of fluids, such as transmission fluid and engine oil. Fourteen standard elastomer compounds were involved in this study, covering various materials currently used in automotive powertrain static and dynamic sealing applications. Participants include OEMs, major rubber manufacturers, a fluid additive company and an independent lab. Manufacturers of each test compound provided formulations, designated ingredients from defined sources, and detailed mixing and molding procedures.

Automotive thermal protection is one of the key areas in the vehicle development process. Critical decisions are usually based on temperature measurement during vehicle testing. Thermocouples are most widely used to determine the temperature of each component during specific test cycle. Therefore, the reliability and accuracy of the thermocouple measurements are of significant importance to the design and release engineers. Errors associated with temperature measurements of automotive components may be caused by radiation from exhaust surfaces such as exhaust manifold, catalytic converter, muffler or exhaust pipes. Other sources of error may be caused by the effect of ambient temperature or airflow if thermocouples are not properly installed. Several errors could arise from the attachment method of the thermocouple to the component or material of interest.

Noise and vibration signals which are stationary are frequently analyzed for frequency content using Fourier Transform methods. Frequency content can be clearly displayed, but temporal characteristics of signals can easily be obscured in a frequency spectrum. Several commonly available methods of analyzing nonstationary signals are available, such as short-time Fourier Transform and wavelet analysis. Smearing of data in the time and/or frequency domains leads to limited usefulness of these methods in analyzing rapidly varying signals. This also applies to stationary signals with perceivable temporal characteristics. The Wigner Distribution is a time-frequency analysis which can analyze rapidly varying signals and show the effects of rapid changes in signal characteristics. It is appealing because it fully preserves all the information present in the original signal.

A process for simultaneously optimizing the mechanical performance and minimizing the weight of an automotive body-in-white will be developed herein. The process begins with appropriate load path definition though calculation of an optimized topology. Load paths are then converted to sheet metal, and initial critical cross sections are sized and shaped based on packaging, engineering judgment, and stress and stiffness approximations. As a general direction of design, section requirements are based on an overall vehicle “design for stiffness first” philosophy. Design for impact and durability requirements, which generally call for strength rather than stiffness, are then addressed by judicious application of the most recently developed automotive grade advanced high strength steels. Sheet metal gages, including tailored blanks design, are selected via experience and topometry optimization studies.

This paper presents methods for analyzing and visualizing the relationship between input torque, clutch torque, output torque and input acceleration during the inertia phase of a shift. The methods presented are an expansion of the lever analogy [1]. The methods are useful for understanding how geartrain inertia affects control, both its magnitude and distribution. Clutch energy and shift speeds are also easy to calculate and understand using the tools presented. Lastly the methods show why the optimum control strategies for various transmission configurations (such as DCT's, planetary transmissions, etc.) are different in the inertia phase.

The ultimate goal of reliability engineering is to prevent design failure modes in the field. Effective design verification can be a powerful tool toward achieving this goal. Reducing development time, minimizing cost, and improving quality are further challenges which drive effective design verification. This paper explains the key steps required to develop an effective design verification plan and report (DVP&R). In addition, lessons learned will be discussed using specific examples of undesirable practices. Design for Six Sigma (DFSS) verification phase requirements are also examined.

The paper studies the distributions of residual stresses in auto components after induction hardening. Three prototype parts are analyzed in this paper. Firstly, the temperature fields of the analyzed parts are quantitatively simulated during quenching by simulating surface heating to the austenitization temperature of the material. Secondly, the formation and states of the residual stresses are predicted. Therefore the distribution of residual stress is simulated and shows compressive stresses on the surface of components so that the strength can be improved. The simulated results by computer are compared with experimental results. The good comparison indicates that the results obtained by the FEA analysis are reliable. Thus, it can be concluded that the FEA (Finite element analysis) program is effectively developed to simulate heating and quenching processes and residual stresses distribution.

Strain gages have been widely used for measuring strain or deformation. They are very reliable and accurate. However, for application on fabric material, strain gages have their limitations. In this paper, digital image correlation (DIC) is used to measure the deformation around the rear window on a convertible top. The test needed to be non destructive, the vehicle and convertible top could not be damaged. The deformation or strain measured on the fabric was used to estimate the force experienced at the interface between the glass and the fabric during an opening/closing application. A speckle pattern was created on the convertible fabric where deformation was to be measured with washable paint. The image of the measured area was first recorded. The convertible top was then latched down and the fabric was stretched. A second image was recorded again. Based on the two images, the deformation/strain between the two conditions was measured.

Physical modeling has been used by the industry to improve development time and produce a quality product. In this paper, we will describe two methods used in system control to take advantage of the physical model. One method describes a complete transmission physical model with a full system control utilizing co-simulation techniques. Data will be presented, and comparison to vehicle data will be conducted and verified. The second method will illustrate how to utilize the physical model to improve system design and modification. In this method, vehicle data will be used as inputs to the model, the model output will be verified against vehicle output data. The two methods are excellent tools for the Design For Six Sigma process (DFSS design).

This paper will define the process for correlating fatigue based customer duty cycle with laboratory bench test data. The process includes the development of the Median and Design Load-Life curve equations. The Median Load-Life curve is a best fit linear regression; whereas, the Design Load-Life curve incorporates component specific reliability and confidence targets. To account for the statistical distribution of fatigue life, due to sample size, the one-side lower-bound tolerance limit method ( Lieberman, 1958 ) will be utilized. This paper will include a correlation between the predicted design fatigue life and the actual product life.

Environmental concerns and government regulations are factors that have led to an increased focus on fuel economy in the automotive industry. This paper identifies a method used to improve the efficiency of a front-wheel-drive (FWD) automatic transmission. In order to create improvements in large complex systems, it is key to have a large scope, to include as much of the system as possible. The approach taken in this work was to use Design for Six Sigma (DFSS) methodology. This was done to optimize as many of the front-wheel-drive transmission components as possible to increase robustness and efficiency. A focus of robustness, or consistency in torque transformation, is as important as the value of efficiency itself, because of the huge range of usage conditions. Therefore, it was necessary to find a solution of the best transmission component settings that would not depend on specific usage conditions such as temperatures, system pressures, or gear ratio.

In recent trend, there is a huge demand for lightweight chassis frame, which improves fuel efficiency and reduces cost of the vehicle. Stiffness based optimization process is simple and straightforward while durability (life) based optimizations are relatively complex, time consuming due to a two-step (Stress then life) virtual engineering process and complicated loading history. However, durability performances are critical in chassis design, so a process of optimization with simplified approach has been developed. This study talks about the process of chassis frame weight optimization without affecting current durability performance where complex durability load cases are converted to equivalent static loadcases and life targets are cascaded down to simple stress target. Sheet metal gauges and lightening holes are the parameters for optimization studies. The optimization design space is constrained to chassis unique parts.

There is a continual need to apply heat treatment processes in innovative ways to optimize material performance. One such application studied in this research is carburizing followed by austempering of low carbon alloy steels, AISI 8620, AISI 8822 and AISI 4320, to produce components with high strength and toughness. This heat treatment process was applied in two steps; first, carburization of the surface of the parts, second, the samples were quenched from austenitic temperature at a rate fast enough to avoid the formation of ferrite or pearlite and then held at a temperature just above the martensite starting temperature to partially or fully form bainite. Any austenite which was not transformed during austempering, upon further cooling formed martensite or was present as retained austenite.