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In this paper a diagnostic design process is proposed for developmental vehicles where mainstream design process is not well-suited. First a review of current practice in on-board vehicle fault diagnostics design is presented with particular focus on the application of this process to the development of the Ford Escape Hybrid Electric Vehicle (HEV) program and a demonstration Fuel Cell Electric Vehicle (FCEV) program. Based on the review and evaluation of these experiences, a new tool for diagnostics design is proposed that promises to make the design more traceable, to reduce the repetition of work, and to improve understandability and reuse.

A uniaxial stress-strain curve obtained from a conventional tensile test is only valid up to the point of uniform elongation, beyond which a diffuse neck begins to develop, followed by localized necking and eventual fracture. However Finite Element Analysis for sheet metal forming requires an effective stress-strain curve that extends well beyond the diffuse necking point. Such an extension is usually accomplished by analytical curve fitting and extrapolation. Recent advancement in Digital Image Correlation (DIC) techniques allows direct measurement of full-range stress-strain curves by continuously analyzing the deformation within the diffuse neck zone until the material ruptures. However the stress-strain curve obtained this way is still approximate in nature. Its accuracy depends on the specimen size, the gage size for analysis, and the material response itself.

When a vehicle with a partially filled fuel tank undergoes sudden acceleration, braking, turning or pitching motion, fuel sloshing is experienced. It is important to establish a CAE methodology to accurately predict slosh phenomenon. Fuel slosh can lead to many failure modes such as noise, erroneous fuel indication, irregular fuel supply at low fuel level and durability issues caused by high impact forces on tank surface and internal parts. This paper summarizes activities carried out by the fuel system team at Ford Motor Company to develop and validate such CAE methodology. In particular two methods are discussed here. The first method is Volume Of Fluid (VOF) based incompressible multiphase Eulerian transient CAE method. The CFD solvers used here are Star CD and Star CCM+. The second method incorporates Fluid-Structure interaction (FSI) using Arbitrary Lagrangian-Eulerian (ALE) formulation.

Failure mode and fatigue behavior of dissimilar laser welds in lap-shear specimens of aluminum and copper sheets are investigated. Quasi-static tests and fatigue tests of laser-welded lap-shear specimens under different load ranges with the load ratio of 0.1 were conducted. Optical micrographs of the welds after the tests were examined to understand the failure modes of the specimens. For the specimens tested under quasi-static loading conditions, the micrograph indicates that the specimen failed through the fusion zone of the aluminum sheet. For the specimens tested under cyclic loading conditions, two types of failure modes were observed under different load ranges. One failure mode has a kinked crack initiating from the interfacial surface between the aluminum and copper sheets and growing into the aluminum fusion zone at an angle close to 90°.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

The efforts of the ISO “Test Variability Task Force” have been aimed at improving the understanding and at reducing brake dynamometer test variability during performance testing. In addition, dynamometer test results have been compared and correlated to vehicle testing. Even though there is already a vast amount of anecdotal evidence confirming the fact that different procedures generate different friction coefficients on the same brake corner, the availability of supporting data to the industry has been elusive up to this point. To overcome this issue, this paper focuses on assessing friction levels, friction coefficient sensitivity, and repeatability under ECE, GB, ISO, JASO, and SAE laboratory friction evaluation tests.

The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.

Optimal material selection for a part becomes quite challenging with dynamically changing data from various sources. Multiple manufacturing locations with varying supplier capabilities add to the complexity. There is need to balance product attribute requirements with manufacturing feasibility, cost, sourcing, and vehicle program strategies. The sequential consideration of product attribute, manufacturing, and sourcing aspects tends to result in design churns. Ford R&A is developing a web based material recommender tool to help engineers with material selection integrating sourcing, manufacturing, and design considerations. This tool is designed to filter the list of materials for a specific part and provide a prioritized list of materials; and allow engineers to do weight and cost trade-off studies. The initial implementation of this material recommender tool employs simplified analytical calculators for evaluation of structural performance metrics of parts.

A three-step process was developed to estimate fracture criteria for a human body model. The process was illustrated via example wherein skull fracture criteria were estimated for the Ford Human Body Model (FHBM)~a finite element model of a mid-sized human male. The studied loading condition was anterior-to-posterior, blunt (circular/planar) cylinder impact to the frontal bone. In Step 1, a conditional reference risk curve was derived via statistical analysis of the tests involving fractures in a recently reported dataset (Cormier et al., 2011a). Therein, Cormier et al., authors reported results for anterior-to-posterior dynamic loading of the frontal bone of rigidly supported heads of male post mortem human subjects, and fracture forces were measured in 22 cases. In Step 2, the FHBM head was used to conduct some underlying model validations relative to the Cormier tests. The model-based Force-at-Peak Stress was found to approximate the test-based Fracture Force.

Vehicle sound quality has become an important basic performance requirement. Traditionally, automobile noise studies were focused on quietness. It is now necessary for the automobile to be more than quiet. The sound must be pleasing. This paper describes a development process to improve both vehicle noise level and sound quality. Formal experimental design techniques were utilized to quantify various hardware effects. A-weighted sound pressure level, Speech Intelligibility, and Composite Rating of Preference were the three descriptors used to characterize the vehicle's sound quality. Engineering knowledge augmented with graphical and statistical techniques were utilized during data analysis. The individual component contributions to each of the sound quality descriptors were also quantified in this study.

There is a recognized need in the industry to improve the quality of our CFD (Computational Fluid Dynamics) processes. One part of that initiative is to measure the accuracy of the current processes and identify opportunities for improvement. This report documents the results of a disciplined calibration process that uses statistical analyses techniques to assess CFD quality. The process is applied to UH3D, a Navier-Stokes solver used at Ford to model vehicle front-end geometry and engine cooling systems. The study is focused on a Taurus under relatively ideal circumstances to address one of the major deliverables from the analytical process, i.e., what is the accuracy of the front-end cooling airflow predictions? To address this question, high quality isothermal experiments and calculations were conducted on twenty-three front-end configurations at four non-idle operating conditions.

In the engineering design attempt to minimize customer complaints related to belt misalignment, a maximum belt pulley entry and pulley exit angle was chosen as a critical target for avoiding excessive belt wear and chirp noise. Calculating belt misalignment due to pulley misalignment from the perfect plane between two grooved pulleys is done using statistical stackup calculations in three dimensional space. This is usually done using available computer statistical simulation models and the most current component detail drawings. For backside pulleys (flat pulleys), such calculations can be performed only if the position of the belt as it contacts a backside pulley is known. Since the shape of the grooves no longer fixes the position of a belt at pulley entry point, the position of the belt at the backside pulley can not be determined by the position of the pulley groove.

Current trends in the auto industry requiring tighter dimensional specifications combined with the use of lightweight materials, such as aluminum, are a challenge for the traditional manufacturing processes. The hemming process, a sheet metal bending operation used in the manufacturing of car doors and hoods, poses problems meeting tighter dimensional tolerances. Hemming is the final operation that is used to fasten the outer panel with the inner panel by folding the outer panel over the inner panel. Roll in/out is one of the main quality concerns with hemming, and keeping it under tolerance is a high priority issue for the auto manufacturers. Current hemming process technology, given the mechanical properties of current materials, has reached its saturation limit to deliver consistent dimensional quality to satisfy customers and at the same time meet government standards.

Finite element analysis (FEA) predictions of magnesium beams are compared to load versus displacement test measurements. The beams are made from AM60B die castings, AM30 extrusions and AZ31 sheet. The sheet and die cast beams are built up from two top hat sections joined with toughened epoxy adhesive and structural rivets. LS-DYNA material model MAT_124 predicts the magnesium behavior over a range of strain rates and accommodates different responses in tension and compression. Material test results and FEA experience set the strain to failure limits in the FEA predictions. The boundary conditions in the FEA models closely mimic the loading and constraint conditions in the component testing. Results from quasi-static four-point bend, quasi-static axial compression and high-speed axial compression tests of magnesium beams show the beam's behavior over a range of loadings and test rates. The magnesium beams exhibit significant material cracking and splitting in all the tests.

With the increasing demand for safety, energy saving and emission reduction, Advanced High Strength Steels (AHSS) have become very attractive steels for automobile makers. The usage of AHSS steels is projected to grow significantly in the next 5-10 years with new safety and fuel economy regulations. These new steels have significant manufacturing challenges, particularly for welding and stamping. Welding of AHSS remains one of the technical challenges in the successful application of AHSS in automobile structures due to heat-affected zones (HAZ) at the weld joint. In this study Gas Metal Arc Welding (GMAW) of a lap joint configuration consisting of 1.0 mm Usibor® 1500 steel to uncoated Dual Phase 780 (DP780) steel was investigated. The objective of the study was to understand the wire feed rate (WFR) and torch (or robot) travel speed (TTS) influence on lap joint tensile strength.

Environmental awareness and fuel economy legislation has resulted in greater emphasis on developing more fuel efficient vehicles. As such, achieving fuel economy improvements has become a top priority in the automotive field. Companies are constantly investigating and developing new advanced technologies, such as hybrid electric vehicles, plug-in hybrid electric vehicles, improved turbo-charged gasoline direct injection engines, new efficient powershift transmissions, and lighter weight vehicles. In addition, significant research and development is being performed on energy management control systems that can improve fuel economy of vehicles. Another area of research for improving fuel economy and environmental awareness is based on improving the customer's driving behavior and style without significantly impacting the driver's expectations and requirements.

This work presents a statistical approach for simulation based on Monte Carlo method. As an exercise of the method a CAE vehicle dynamics model was specifically created to evaluate the likelihood to meet a given target driving a maneuver for given inputs variations. In the exercise, three different inputs were chosen as stochastic inputs (also called noise factors) and all relevant information about their statistics has been raised, based in components information. The chosen inputs are: front/rear dampers curves, front/rear ride heights and tire surface temperature. A brief description of the Monte Carlo technique is presented. The choice of this method is due to the reduced number of simulations required to have a given accuracy in comparison with other approaches, especially for multivariable system. As output variable for the exercise, the tire patch height was chosen and the resulting probability density function of it is presented.

Internal combustion engines are designed to meet the high power, low fuel consumption and also, low exhaust emissions. The engine running conditions is valid the concept that, the expectative is very high because of the variety of operating conditions like cold start, frequent start and stop, time high speed and load, traditional gasoline, mix of gasoline and alcohol and finally, alcohol fuel only. Considering such demand, this paper explains the relationship between the reliability bathtub curve, specifically the "Infant Mortality" portion. The bathtub curve describes failure rate as a function of time. The "Infant Mortality" portion of the curve is the initial section for which the failure (death) rate decreases with time (age). In general, these problems are related to manufacturing aspects or poor design definitions. With development of technology, hard failures, the ones that cause dependability, are becoming rare.

The Proposal of this paper is to demonstrate the diagnosis methodology based on Bayesian Net and fuzzy logic to indicate failure probability for vehicle systems and other segments, for instance medical and Aeronautic. This work was initially developed in a dissertation that has succeeded in developing a system of fault diagnosis for diesel engines and now this article demonstrated the possibility of extending this methodology to other vehicle systems and other segments science. Also extension to other areas was also added to the technique of logic that adds the advantage of supporting the fuzzy logic in complex environments, such as the vehicle approached the diagnosis, aerospace and medical.