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Although great advances have been made over the last two decades in the automotive structural design process, tradition and experience guide many design choices even today. The need for innovative tools is stronger now more than ever before as the design engineer is confronted with more complex, often contradictory design requirements such as cost, weight, performance, safety, time to market, life cycle, aesthetics, environmental impact, changes in the industry's business models, etc. The ever-increasing use of optimization tools in engineering design generates solutions that are very close to the limits of the design constraints, hardly allowing for tolerances to compensate for uncontrollable factors such as manufacturing imperfections. Optimum designs developed without consideration of uncertainty can lead to non-robust designs.

As the environment becomes more competitive, the necessity of cost reduction actions in all companies has arrived at a level of extreme importance. Nowadays, identifying variable cost reduction opportunities is not difficulty, the difficulty is implementation. Usually, the Product Development Team generates long lists of cost reduction opportunities that for some reasons are not implemented. The highlighted reason is the lack of a dedicated team in a robust process of opportunity identification, development and implementation.

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 discusses a Design For Six Sigma (DFSS) approach to a driveline system NVH design process as used on a RWD IRS (Independent Rear Suspension) vehicle program. It is shown how this approach helped understand the ways variability in the driveline system (mount properties and locations) affected output to the rest of the vehicle. A series of CAE and DFSS tools were used to deliver a distribution that described the vehicle Driveline Roughness performance, to identify the critical control factors that affected the performance, and to reduce the response sensitivity to variability of these critical factors. Other driveline issues were factored into the process indirectly.

Recent applied mathematics research on the properties of the invertible shift-invariant discrete wavelet transform has produced new ways to visualize, separate, and synthesize impulsive sounds, such as thuds, slaps, taps, knocks, and rattles. These new methods can be used to examine the joint time-frequency characteristics of a sound, to select individual components based on their time-frequency localization, to quantify the components, and to synthesize new sounds from the selected components. The new tools will be presented in a non-mathematical way illustrated by two real-life sound quality problems, extracting the impulsive components of a windshield wiper sound, and analyzing a door closing-induced rattle.

Custom functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. Typical examples include mathematical functions, filtering routines and lookup tables. The custom routines are very efficient and have been in production for many years [ 1 ]. These hand-crafted functions can be reused in new control algorithm designs being developed using Model Based Design (MBD) tools. The next generation of vehicle control software may contain a mix of both automatically generated software and manually developed code. At Ford Motor Company, the code is automatically generated from control algorithm models that are developed using The MathWorks tool chain. Depending on the project-specific needs, the control algorithm models are automatically translated to efficient C code using either The Math Works Real-Time Workshop Embedded Coder (RTW-EC) or dSPACE TargetLink production code generators.

This paper focus on the design approach of mapping the equivalent bead to the physical bead geometry. In principle, the physical character and geometry of equivalent bead is represented as restraining force (N/mm) and a line (bead center line). During draw development, the iterations are performed to conclude the combination of restraining force that obtains the desired strain state of a given panel. The objective of physical bead design to determine a bead geometry that has the capacity to generate the same force as specified in 2D plane strain condition. The software package ABAQUS/CAE/Isight with python script is utilized as primary tool in this study. In the approach, the bead geometry is sketched and parameterized in ABAQUS/CAE and optimized with Isight to finalize the bead geometry.

The air suspension development and application has becoming increasingly applied also in commercial vehicles, offering to the driver more dynamic comfort as well as contributing to the reduction of impact loads on highways. Through this project pursuit show the analysis and application of an air suspension system for commercial tractor vehicles application. A special focus was given to pneumatic actuation system, responsible for leveling and control of suspension′s stiffness under different conditions of usage, laden and unladen. The project was conducted starting with the vehicle dynamic performance analysis, evaluating the pneumatic suspension circuit modifications in order to obtain the vehicle dynamic behavior improvement, ensuring directional stability under different maneuvering conditions. For entire development were also used quality tools, considering the possible failure modes and effects as well as virtual simulation tools (Adams) and bench validations.

Use of sensors to monitor dynamic performance of machine tools at Ford’s powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up.

Modern vehicles become increasingly software intensive. Software development therefore is critical to the success of the manufacturer to develop state of the art technology. Standards like ISO 26262 recommend requirement-based verification and test cases that are derived from requirements analysis. Agile development uses continuous integration tests which rely on test automation and evaluation. All these drove the development of a new model-based software verification environment. Various aspects had to be taken into account: the test case specification needs to be easily comprehensible and flexible in order to allow testing of different functional variants. The test environment should support different use cases like open-loop or closed-loop testing and has to provide corresponding evaluation methods for continuously changing as well as for discrete signals.

Often, ergonomists need to determine the maximum acceptable load or force for a given task. Ergonomic tools, like the NIOSH Lifting Guidelines (Waters et al, 1993) and the Liberty Mutual Tables (Snook & Ciriello, 1991)), provide such loads for selected population percentiles. In contrast, the UGS Jack Static Strength Prediction tool (JSSP), based on the University of Michigan’s 3D Static Strength Prediction Program (3DSSPP), uses force(s) as inputs and calculates the percentage of the male or female population that would be capable (%Cap) for a given task. Typically, the %Cap threshold will be a fixed number determined from corporate or government guidelines (e.g. 75% of females). Thus, in order to find the acceptable load, users of JSSP must iterate through loads until they find a %Cap that is just below their predetermined threshold.

Deficiencies in understanding port flow have created an elongated design process involving computer automated engineering tools and empirical results. Analytical flow models could solve the problem however the error in existing algorithms often exceed ten percent. These discrepancies come from the fact that the flow direction relative to the outflow area has not been properly treated. The current paper describes how Design For Six Sigma was used to develop a new transfer function based upon a finite set of empirical results and the port geometry. The error between the volumetric flow rate transfer function and the observed result is less than ten percent. This accuracy is high enough to bypass the computer automated engineering process and its associated week-long delays.

On August 9, 1995, Environmental Protection Agency (EPA) established the Agency's service information regulations. These regulations, in part, required each Original Equipment Manufacturer (OEM) to either provide enhanced information to equipment and tool companies, or make its OEM-specific diagnostic tool available for purchase by aftermarket technicians. In 2001, Ford Motor Company developed a Microsoft Access database containing tested and verified vehicle data providing necessary module information from Ford Motor Company's specific diagnostic tools. The database was presented to the Equipment and Tool Institute (ETI) during the 2001 ETI Tech Week in Detroit. The format is being considered a recommended practice by ETI, and could be used by any OEM seeking a guideline for providing the necessary On-Board Diagnostics (OBD) protocol information.

A Web-based analytical tool that provides solutions to support vehicle attribute target setting with integration of attribute data, target setting best practices and data mining techniques is introduced at Ford Motor Company. With this tool, quality of attribute target setting is to be significantly improved. Major features of the tool include attribute data preservation, single data repository, standard report-out process, expertise sharing, up-front target balancing and customer satisfaction analysis that are enablers of the improvement to set better initial target ranges and conduct a more efficient attribute trade-off process. Method of the tool development, advantages, disadvantages, and one application example are given in this paper.

Pickup box drum drop test is critical in vehicle development to determine the impact strength of the floor panels. Physical hardware tests on prototypes have been used to assess whether the performance of the future pickup box meets design requirements. In order to reduce costs and shorten development cycle, CAE methodology was developed to accurately model the drum drop test. In this paper, a CAE procedure for modeling the drum drop test is proposed. Dynamic explicit finite element code LS-Dyna was used to simulate the non-linear impact process of a drum onto the box floor. The permanent plastic damages on the floor panel were recorded in both simulation and experiments. Very good correlation between the simulation results and the physical hardware tests was achieved. It indicates that the methodology developed is an effective tool in evaluating the performances of the pickup box floor panels.

Flanging is a secondary operation in sheet metal forming processes. Traditionally, the design of flange shape and trim line is based on an engineer's experience. It takes several iterations to achieve the desired flange geometry because of potential splits. In this paper, an efficient CAE-based tool is developed to quickly predict the formability of a given flange design and enable the optimization of trim lines. A numerical algorithm is formulated in this CAE tool to convert the 3D flanging process into an equivalent in-plane deformation problem. The developed CAE tool is also integrated with the optimization software LS-OPT for trim line design.

In the past twenty years, the explicit finite element method has been successfully employed for crash simulation. At present, crashworthiness analysis is still basically a calibration based engineering practice, but not a fully predictive process. The increasing expectations and requirements on CAE are even more challenging. To develop a predictive and reliable CAE tool, it is important to investigate the root causes that affect the numerical accuracy and the availability of the analytical method. Some of the challenging issues are discussed here from both theoretical and engineering aspects, such as convergence of explicit finite element method, locking-free shell element, analysis of material rupture, and modeling of spot weld.

Lean Six Sigma is an approach that is gaining momentum both in manufacturing and service industries. Design for Lean Six Sigma (DFLSS) is an outgrowth of the DFSS and Lean Six Sigma approaches. The essence of DFLSS is to ensure design quality and predictability during the early design phases and the approach employs a structured integrated product development methodology and a comprehensive set of robust tools to drive product quality, innovation, faster time to market, and lower product costs. When it comes to automotive Product Development, applying lean principles and DFSS together becomes more of a challenge within the existing PD system. While the benefits of DFLSS present an attractive proposition in a fiercely competitive market it brings its own challenges as to how to deploy it for maximum benefits. This paper examines the challenges, potential and opportunities for DFLSS in the automotive industry and presents a vision for integrating it in to the Product Development System.

This paper presents an image analysis of a laboratory-based rollover crash test using camera-matching photogrammetry. The procedures pertaining to setup, analysis and data process used in this method are outlined. Vehicle roll angle and rate calculated using the method are presented and compared to the measured values obtained using a vehicle mounted angular rate sensor. Areas for improvement, accuracy determination, and vehicle kinematics analysis are discussed. This paper concludes that the photogrammetric method presented is a useful tool to extract vehicle roll angle data from test video. However, development of a robust post-processing tool for general application to crash safety analysis requires further exploration.

Vehicle audio system performance is an important attribute for final costumers. In this sense, its evaluation is an important aspect for selecting the design and validation process for automobile manufacturers. Usually the vehicle audio system performance is evaluated only by subjective judgment. However the design requirements demands objective measurements to set targets establish benchmarking and apply refinements to the design. Thus, in order to evaluate and improve sound system performance, it has been established a subjective evaluation process on reproducing and analyzing customer perception in a more reliable way. To support this information, objective evaluations have been used based on total harmonic distortion (THD), normalized frequency response (NFR) methods and spectrogram, which have been shown as straight and fast objective tools. Reinforcing the objective evaluations, qualitative time-frequency spectrogram has been used.