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Realistically experiencing the sound and vibration data through actually listening to and feeling the data in a full-vehicle NVH simulator remarkably aids the understanding of the NVH phenomena and speeds up the decision-making process. In the case of idle vibration, the sound and vibration of the idle condition are perceived simultaneously, and both need to be accurately reproduced simultaneously in a simulated environment in order to be properly evaluated and understood. In this work, a case is examined in which a perceived idle quality of a vehicle is addressed. In this case, two very similar vehicles, with the same powertrain but somewhat different body structures, are compared. One has a lower subjective idle quality rating than the other, despite the vehicles being so similar.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

OEMs are racing to develop lightweight vehicles as government regulations now mandate automakers to nearly double the average fuel economy of new cars and trucks by 2025. Lightweight materials such as aluminum, magnesium and carbon fiber composites are being used as structural members in vehicle body and suspension components. The reduction in weight in structural panels increases noise transmission into the passenger compartment. This poses a great challenge in vehicle sound package development since simply increasing weight in sound package components to reduce interior noise is no longer an option [1]. This paper discusses weight saving approaches to reduce noise level at the sources, noise transmission paths, and transmitted noise into the passenger compartment. Lightweight sound package materials are introduced to treat and reduce airborne noise transmission into multi-material lightweight body structure.

Designing a vehicle body involves meeting numerous performance requirements related to different attributes such as NVH, Durability, Safety, and others. Multi-Disciplinary Optimization (MDO) is an efficient way to develop a design that optimizes vehicle performance while minimizing the weight. Since a body design evolves in course of the product development cycle, it is essential to repeat the MDO process several times as a design matures and more accurate data become available. This paper presents a real life application of the MDO process to reduce weight while optimizing performance over the design cycle of the 2015 Mustang. The paper discusses the timing and results of the applied Multi-Disciplinary Optimization process. The attributes considered during optimization include Safety, Durability and Body NVH. Several iterations of MDO have been performed at different milestones in the design cycle leading to a significant weight reduction of the already optimized design by over 16kg.

The Multi Material Lightweight Vehicle (MMLV) developed by Magna International and Ford Motor Company is a result of a US Department of Energy project DE-EE0005574. The project demonstrates the lightweighting potential of a five passenger sedan, while maintaining vehicle performance and occupant safety. Prototype vehicles were manufactured and limited full vehicle testing was conducted. The Mach-I vehicle design, comprised of commercially available materials and production processes, achieved a 364kg (23.5%) full vehicle mass reduction, enabling the application of a 1.0-liter three-cylinder engine resulting in a significant environmental benefit and fuel reduction. This paper reviews the mass reduction and structural performance of aluminum, magnesium, and steel components for a lightweight multi material door design for a C/D segment passenger vehicle. Stiffness, durability, and crash requirements are assessed.

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.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

High ductility cast aluminum alloys are seeing more use in vehicles as a greater effort is made to replace components made from heavier steel and iron alloys with lighter weight alloys such as aluminum. High ductility cast aluminum has significant advantages by allowing for complex shape and considerable consolidation of parts in body structures. However, joining can be a challenge because one popular method for aluminum joining, self-piercing riveting (SPR), requires a ductility of greater than 10%, forcing the common high ductility Al alloys to undergo a T6 heat treatment which adds cost and potential distortion issues to Al component. In this study, friction stir spot welding was investigated as a potential joining technique for this material in the as-cast condition. Samples of as-cast Aural-2™ alloy were joined to Aural-2™, 5754, and 6061 alloys, to determine the manufacturing feasibility, weld strength, and fatigue strength using this joining technique.

Structural adhesives are widely used across the automotive industry for several reasons like scale-up of structural performance and enabling multi-material and lightweight designs. Development engineers know in general about the effects of adding adhesive to a spot-welded structure, but they want to quantify the benefit of adding adhesives on weight reduction or structural performance. A very efficient way is to do that by applying analytical tools. But, in most of the relevant non-linear load cases the classical lightweight theory can only help to get a basic understanding of the mechanics. For more complex load cases like full car crash simulations, the Finite Element Method (FEM) with explicit time integration is being applied to the vehicle development process. In order to understand the benefit of adding adhesives to a body structure upfront, new FEM simulation tools need to be established, which must be predictive and efficient.

When a window opens to provide the occupant with fresh air flow while driving, wind throb problems may develop along with it. This work focuses on an analytical approach to address the wind throb issue for passenger vehicles when a front window or sunroof is open. The first case of this paper pertains to the front window throb issue for the current Ford Escape. Early in a program stage, CAA (Computational Aeroacoustics) analysis predicted that the wind throb level exceeded the program wind throb target. When a prototype vehicle became available, the wind tunnel test confirmed the much earlier analytical result. In an attempt to resolve this issue, the efforts focused on a design proposal to implement a wind spoiler on the side mirror sail, with the spoiler dimension only 6 millimeters in height. This work showed that the full vehicle CAA analysis could capture the impact of this tiny geometry variation on the wind throb level inside the vehicle cabin.

For purposes of reducing development time, cost and risk, the majority of new vehicles are derived strongly or at least generally from a surrogate vehicle, often of the same general size or body style. Previous test data and lessons learned can be applied as a starting point for design of the new vehicle, especially at early phases of the design before definite design decisions have been finalized and before prototype of production test hardware is available. This is true as well of vehicle NVH development where most new vehicles being developed are variants of existing vehicles for which the main noise transfer paths from sources of interest are already understood via test results and existing targets. The NVH targets for new vehicles are defined via benchmarking, market considerations, and other higher-level decisions. The objective is then to bridge the gap between test data from surrogate vehicles to direct support of the NVH development of new vehicle programs.

Typical automotive body structures use resistance spot welding for most joining purposes. New materials, such as Advanced High Strength Steels (AHSS) are increasingly used in the construction of automotive body structures to meet increasingly higher structural performance requirements while maintaining or reducing weight of the vehicle. One of the challenges for implementation of new AHSS materials is weldability assessment. Weld engineers and vehicle program teams spend significant efforts and resources in testing weldability of new sheet metal stack-ups. In this paper, we present a methodology to determine the weldability of sheet metal stack-ups using an Artificial Neural Network-based tool that learns from historical data. The paper concludes by reviewing weldability results predicted by using this tool and comparing with actual test results.

A demonstration structure was cast in AM60. The structure, known as the Generic Frame Casting or GFC, was designed specifically to mimic features seen in castings for closure applications. Excised samples were subsequently removed from different areas of the casting and tested under axial loading conditions. Component level tests were also conducted. Comparison of the excised sample results and the component level testing indicated the influence of local properties on the component level deformation. It was shown that varying the casting processing conditions could change the local ductility and yield strength in different areas of casting with the same geometry. Lowering the local ductility decreased the total displacement in a component level test and lowered the amount of energy absorption. Therefore, understanding the processing conditions and their influence on the local properties is important for predicting behavior in a component level test.

The last ten years have experienced a massive integration of consumer electronics devices in vehicles such as mobile phones, audio and video players, USB devices, and internet access capability. Consumers are now demanding the integration of portable and home devices to vehicle systems transforming it to an extension of the home and office thus providing entertainment and connectivity to both short and long trips The integration of devices that were not designed or specified to operate in the vehicle environment has imposed challenges to the engineers designing vehicle electronics systems in particular to the EMC engineers. The need to design the subsystems that are completely integrated with the consumer electronics devices and also compliant with the car makers current specifications has proven to be a major issue due to the fact that one of the components, the consumer electronic devices, cannot be controlled.

The article presents an innovative approach to the implementation of a robust design optimization solution in an automobiles assembly process. The approach of the entire project is specific to the 6 Sigma optimization process, by applying the DMAIC cycle integrated in a robust engineering approach for rendering lean the final product assembly process. According to the improvement cycle, the aspects specific for such a process are presented sequentially starting with the “Define” phase for presenting the encountered problem and continuing with the presentation of the scope of the project and its objectives. The “Improvement” cycle phase is applied by the analysis of the monitored 6 Sigma metrics (defined during the previous “Measure” phase and the cause and effect analysis, done during a brainstorming meeting developed during the “Analyze” phase). There follows a proposal for the innovative robust solution by which the assembly process is optimized.

Vehicle sound quality has become lately one of the main topics of study in the automotive industry which is associated with the quality of the product. Into the automotive development the static operational sound quality is one of the attributes that is considered. The sounds produced through the manipulation of items like the doors and interior components (windows, seats, safety belts, windshield wipers, and others) generated for safety and warning purposes are items related to the vehicle quality for customers. Those sounds based on relative level of intensity, duration, harmony and degree of contribution are elements that the customer will retain in mind, an overall quality impression. The sound produced during gear lever manipulation is important to the customer in order that the event should transmit low intensity and robust and soft impression.

The constant Q transform consists of a geometrically spaced filter bank, which is close to the wavelet transform due to the feature of its increasing time resolution for high frequencies. On the other hand, it can be processed using the well-known FFT algorithm. In this sense, this tool is a middle term between Fourier and wavelet analyses, which can be used for stationary and non-stationary signals. Automotive NVH signals can be stationary (e.g., idle, cruise) or non-stationary, i.e., time-varying signals (e.g., door closing/opening, run-up, rundown). The objective of this work is to propose the use of the constant Q transform, developed originally for musical signal processing, for automotive NVH (run up, impact strip and door closing) time-frequency analyses. Also, similarities and differences of the proposed tool when compared with Fourier and wavelet analyses are addressed.

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.

Over the past several years a task group within the SAE Automotive Corrosion and Protection (ACAP) Committee has conducted extensive on-vehicle field testing and numerous accelerated lab tests with the goal of establishing a standard accelerated test method for cosmetic corrosion evaluations of finished aluminum auto body panels. This project has been a cooperative effort with OEM, supplier, and consultant participation and was also supported in part by DOE through USAMP (AMD 309). The focus of this project has been the identification of a standardized accelerated cosmetic corrosion test that exhibits the same appearance, severity, and type of corrosion products that are exhibited on identical painted aluminum panels exposed to service relevant environments. Multi-year service relevant exposures were conducted by mounting panels on-vehicles in multiple locations in the US and Canada.

Multidisciplinary design optimization (MDO) methods are commonly used for weight reduction in automotive industry. The design variables for MDO are often selected based on critical parts, which usually are close to optimal after many design iterations. As a result, the real weight reduction benefit may not be fully realized due to poor selection of design parameters. In addition, most applications require running design of experiments (DOE) to explore the full design space and to build response surfaces for optimization. This approach is often too costly if too many design variables are simultaneously considered. In this research, an alternative approach to address these issues is presented. It includes two optimization phases. The first phase uses critical parts for design iterations and the second phase use non-critical for weight reduction. A vehicle body structure is used to demonstrate the proposed strategy to show its effectiveness.