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In the developement process, the engineer is required to design, validate and deliver the components for manufacturing, in an as short as possible lead time. For that, the engineer may use some available tools to save not only time, but also cost. This work presents a zero prototype approach applyied to a plastic component, whose main accomplishment was the decreasing of lead time development due to the intensive use of virtual tools (CAD/CAE). As a result, the product was delivered in a short time, with no need of building physical prototypes, thus reducing development cost.

On the development process of truck vehicles, the dynamic behavior must be considered together with the costs involved in this development. Objective measurements, subjective evaluations and CAE simulations are used in order to support this development process. Ride comfort, acceleration and braking performance, handling and NVH are examples of attributes considered in the dynamic behavior evaluation of a tuck. Some characteristics of steering column vibration, noise and harshness are relevant to guarantee driver comfort level and vehicle safety. In this work, CAE models validated by experimental measurements were used to identify cab and vehicle modes of vibration which have significant influence on steering column response. Using this procedure, an alternative was proposed in order to decrease the amplitudes of cab and steering column vibration.

Vehicle chassis mounted cantilevered components should meet two critical design targets: 1) NVH criterion to avoid resonance with road noise and engine vibration and 2) satisfied durability performance to avoid any incident in structure failure and dysfunction. Generally, two types of testing are performed to validate chassis mounted cantilevered component in the design process: shaker table testing and vehicle proving ground testing. Shaker table testing is a powered vibration endurance test performed with load input summarized from real proving ground data and accurate enough to replicate the physical test. The proving ground test is typically performed at critical milestones with full vehicles. Most tests are simplified lab testing to save cost and effort. CAE procedures that virtually replicate these lab tests is even more helpful in the design verification stages.

A new methodology to predict vehicle interior wind noise using CFD results has been developed. The CFD simulation replaces wind tunnel testing for providing flow field information around vehicle greenhouse. A loadcase model based on the CFD results is used to excite an SEA vehicle model. This new approach has been demonstrated on a production vehicle with success for the frequency range of 250-10K Hz. The CAE prediction of interior wind noise agrees within 0.2 sones from wind tunnel testing. The model has been used to evaluate wind noise performance with different door glass design parameters. A glass thickness change from 3.8 mm to 4.8 mm results in 1.1 sones improvement, which agrees well to 1.4 sones improvement from testing. Laminated glass with about 3 times higher damping results in 2.5 sones improvement. This methodology using CFD results can be used in the early stage of product development to impact designs.

Recent trends show a growing demand for improved powertrain noise and vibration quality. In particular, there is little customer acceptance of vibration and noise (“boom”) at engine idle speeds. CAE analysis is being used increasingly as an aid for reducing overall vehicle level responses. Traditionally, analytical idle response is evaluated for only one particular engine order at a time. An efficient Excel based tool called VeCTRA (Vehicle Cascade & Target Response Analysis) was developed to accurately assess the effects of multiple powertrain orders on the vehicle level idle response. VeCTRA is capable of predicting the overall vehicle level response (tactile and acoustic) as well as determining the contribution from each engine order and the specific component excitations within an order. VeCTRA is capable of using analytical or experimentally measured sensitivity and/or excitation data.

This paper describes the development of the control system for a new type of mechanical turbocharger, the Active Control Turbocharger (ACT). The main difference of ACT compared to its predecessor, the Variable Geometry Turbocharger (VGT), lies in the inlet area modulation capability which follows an oscillating (sinusoidal) profile in order to match as much as possible the similar profile of the emitted exhaust gases entering the turbine in order to capturing the highly dynamic, energy content existent in exhaust pulses. This paper describes the development of a new controller in an adaptive framework in order to improve the response of the ACT. The system has been modelled using a one-dimensional Ricardo WAVE engine simulation software and the control system which actuates the nozzle (rack) position is modelled in Matlab-Simulink and uses a map-based structure coupled with a PID controller with constant parameters.

A traditional vane type oil pump used inside the engines and the transmissions has equal angles or spacing between the vanes. The equal spacing intensifies pressure fluctuations generated within the pump leading to narrowband pressure spikes at the pump main order and its harmonics. Unequal spacing, however, can relax the severity of the spikes by breaking down the narrowband peaks and distributing them over a larger frequency range. Optimization of the angles within the pump design constraint can maximize the benefit of unequal spacing in reducing the pressure pulsations for a lower risk of engine or transmission whine. The scope of this paper is around the optimization process for vane spacing and different objective functions which can be used to obtain optimized solutions. The simulation results for optimized spacing based on two different objective functions for 7, 8 and 9 vanes are presented. The design constraints for the optimization are discussed as well.

Two finite element methodologies for simulating oil-canning tests on closure assemblies are presented. Reflecting the experimental conditions, the simulation methodologies assume load-controlled situations. One methodology uses an implicit finite-element code, namely ABAQUS®, and the other uses an explicit code, LS-DYNA®. It is shown that load-displacement behavior predicted by both the implicit and explicit codes agree well with experimental observations of oil-canning in a hood assembly. The small residual dent depth predictions are in line with experimental observations. The method using the implicit code, however, yields lower residual dent depth than that using the explicit code. Because the absolute values of the residual dent depths are small in the cases examined, more work is needed, using examples involving larger residual dent depth, to clearly distinguish between the two procedures.

Traditionally, Knee Air Bag (KAB) is constructed of a woven nylon or polyester fabric. Recently, Ford developed an injection molded air bag system for the passenger side called Active Glove Box (AGB). This system integrates a plastic bladder welded between the glove box outer and inner doors. This new system is smaller and lighter, thus improving the roominess and other creature comforts inside the passenger cabin while providing equivalent restraint performance as traditional knee airbag system. This patented technology allows positioning of airbags in new locations within the vehicle, thus giving more freedom to designers. The first application of this technology was standard equipment on the 2015 Ford Mustang. Given that this technology is first in the industry, it was a challenge to design, test and evaluate the performance of the system as there is no benchmark to compare this technology. A CAE driven design methodology was chosen to overcome this challenge.

Electric vehicles are highly sensitive to variations in environmental factors (like temperature, drive style, grade, etc.). The distribution of real-world range of electric vehicles due to these environmental factors is an important consideration in target setting. This distribution can be obtained by running several simulations of an electric vehicle for a number of high-frequency velocity, grade, and temperature real-world trip profiles. However, in order to speed up simulation time, a unique set of drive profiles that represent the entire real-world data set needs to be developed. In this study, we consider 40,000 unique velocity and grade profiles from various real-world applications in EU. We generate metadata that describes these profiles using trip descriptor variables. Due to the large number of descriptor variables when considering second order effects, we normalize each descriptor and use principal component analysis to reduce the dimensions of our dataset to six components.

The paradigm for utilizing computer-aided engineering (CAE) to analyze automotive steering and suspension designs is rapidly changing. CAE's role has expanded beyond mere analysis to designing and improving product reliability and robustness. This paper presents an approach for avoiding the steering wheel nibble failure mode by improving robustness and therefore reliability through the use of CAE. For this paper, reliability is the ability of the system to avoid failure modes. A failure mode is any customer perceived deviation from ideal and avoiding failure modes naturally improves reliability. [1]

For many years, the use of in-mold fasteners has been avoided for various reasons including: not fully understanding the load cases in the part, the fear of quality issues occurring, the need for servicing, or the lack of understanding the complexity of all failure modes. The most common solution has been the use of secondary operations to provide attachments, such as, screws, metal clips, heat staking, sonic welding or other methods which are ultimately a waste in the process and an increase in manufacturing costs. The purpose of this paper is to take the reader through the design process followed to design an in-molded attachment clip on plastic parts. The paper explores the design process for in-molded attachment clips beginning with a design concept idea, followed by basic concept testing using a desktop 3D printer, optimizing the design with physical tests and CAE analysis, and finally producing high resolution 3D prototypes for validation and tuning.

This paper reports a study of the dynamic characteristics of body mounts in body on frame vehicles and their effects on structural and occupant CAE results. The body mount stiffness and damping are computed from spring-damper models and component test results. The model parameters are converted to those used in the full vehicle structural model to simulate the vehicle crash performance. An effective body mount in a CAE crash model requires a set of coordinated damping and stiffness to transfer the frame pulse to the body. The ability of the pulse transfer, defined as transient transmissibility[1]1, is crucial in the early part of the crash pulse prediction using a structural model such as Radioss[2]. Traditionally, CAE users input into the model the force-deflection data of the body mount obtained from the component and/or full vehicle tests. In this practice, the body mount in the CAE model is essentially represented by a spring with the prescribed force-deflection data.

Growing demand for fuel-efficient or light weight vehicle has become a challenge for vehicle development. Upfront engineering process provides more opportunities for engineers to improve body weight efficiency. To accelerate the upfront body development process, the parametric concept modeling technology is commonly employed to generate parametric three-dimensional geometry, joints, modular components, concept welding, and finite element meshes. The topology optimization which determines the best structural layout without weight penalty has also been used during the conceptual design stage. The objective of this research is to explore the feasibility of integrating the advanced parametric concept modeling and both topology optimization and structural optimization technologies into upfront body architecture development process.

Squeak and rattle is one of the major concerns in vehicle design for customer satisfaction. Traditionally squeak and rattle problems are found and fixed at a very late design stage due to lack of up-front CAE prevention and prediction tools. A research work at Ford reveals a correlation between the squeak and rattle performance and diagonal distortions at body closure openings and fastener accelerations in an instrument panel. These findings make it possible to assess squeak and rattle performance implications between different body designs using body-in-prime (B-I-P) and vehicle low frequency noise, vibration and harshness (NVH) CAE models at a very early design stage. This paper is concerned with applications of this squeak and rattle assessment method for up-front body designs prior to a prototype stage.

This paper presents a method of using CAE to determine the pre-load and torque applied to a U-Bolt rear Spring Seat. In this paper it is review two U-bolt design and the stresses generated by the pre-load torque applied, based in this study a process to determine the minimal preload and the torque is discussed. By this process it is possible to determine the minimum Torque and the correct pre-load in the U-Bolt element and assuring the correct fastening of the components avoiding over stress in the Bar elements.

Transfer functions, one of core components in Design for Six Sigma (DFSS), provide the needed relationships between design, process and materials parameters and the CTQs (Critical-to-Quality characteristics) in the product and process development cycle. Transfer function provides direct method for understanding and representing an over all product and process function. Transfer function also provides a strategy for customer voice cascade, function decomposition, physical modeling and concept generation. The concept of transfer function is not new. However, the development of transfer function is not trivial and is a creative and challenging task. In part I of this paper, we will discuss how to develop a transfer function in the DFSS framework. In part II of this paper, we devote our efforts in the discussion of selecting the best transfer function for design evaluation and optimization.

Trail-Braking (TB) is a common cornering technique used in rally racing to negotiate tight corners at (moderately) high speeds. In a previous paper by the authors it has been shown that TB can be generated as the solution to the minimum-time cornering problem, subject to fixed final positioning of the vehicle after the corner. A TB maneuver can then be computed by solving a non-linear programming (NLP). In this work we formulate an optimization problem by relaxing the final positioning of the vehicle with respect to the width of the road in order to study the optimality of late-apex trajectories typically followed by rally drivers. We test the results on a variety of corners. The optimal control inputs are approximated by simple piecewise linear input profiles defined by a small number of parameters. It is shown that the proposed input parameterization can generate close to optimal TB along the various corner geometries.

Virtual Verification (VV) of engineering designs is a critical enabler in the Product Development (PD) process to reduce the time-to-market in a cost efficient manner. Reliance on cost effective VV methods have significantly increased with increased pressure to meet customer expectations for new products at reduced PD budgets. Computer Aided Engineering (CAE) is one such VV method that affords an engineer to make decisions about the ability of the designs to meet the design criteria even before a prototype is built. The first step of the CAE process is meshing which is a time consuming, manual and laborious process. Also mesh development time and accuracy significantly varies with the (1) component (trim body, engine, suspension, brakes, etc.), (2) features predominantly occurring in the component (welds, ribs, fillets, etc.), meshing guidelines based on which the model needs to be developed (durability, safety, NVH, etc.), and the expertise of the meshing engineer involved.

In structural design optimization, it is challenging to determine the optimal dimensions and material for each component simultaneously. Material selection of each part is always formulated as a categorical design variable in structural optimization problems. However, it is difficult to solve such mixed-variable problems using the metamodelbased strategy, because the prediction accuracy of metamodels deteriorates significantly when categorical variables exist. This paper investigates two different strategies of mixed-variable metamodeling: the “feature separating” strategy and the “all-in-one” strategy. A supervised learning-enhanced cokriging method is proposed, which fuses multi-fidelity information to predict new designs’ responses. The proposed method is compared with several existing mixed-variable metamodeling methods to understand their pros and cons. These methods include Neural Network (NN) regression, Classification and Regression Tree (CART) and Gaussian Process (GP).