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Structure enhancement based on data monitored in a traditional side impact evaluation is primarily a trial and error exercise resulting in a large number of computer runs. This is because how the structure gets loaded and the degree of contribution of local structural components to resist the impact while absorbing energy during a side collision is not completely known. Developing real time complete load profiles on a body side during the time span of an impact is not an easy task and these loads cannot be calculated from that calculated at the barrier mounting plate. This paper highlights the load distribution, calculated by a procedure using computer aided engineering (CAE) tools, on a typical 2-door vehicle body side when struck by moving deformable barriers used in the insurance institute for highway safety (IIHS), EuroNCAP and LINCAP side impact evaluations.

The Ford GT Program Team was allocated just 22 months from concept to production to complete the Electrical and Electronics systems of the Ford GT. This reduced vehicle program timing - unlike any other in Ford's history -- demanded that the team streamline the standard development process, which is typically 54 months. This aggressive schedule allowed only 12 weeks to design the entire electrical and electronic system architecture, route the wire harnesses, package the components, and manufacture and/or procure all components necessary for the first three-vehicle prototype build.

The Ford GT powertrain (see Figure 1) is an integrated system developed to preserve the heritage of the LeMans winning car of the past. A team of co-located engineers set out to establish a system that could achieve this result for today's supercar. Multiple variations of engines, transaxles, cooling systems, component locations and innovations were analyzed to meet the project objectives. This paper covers the results and achievements of that team.

The High Voltage Power Distribution Unit (PDU) is constructed of magnesium in support of Fuel Cell Electric Vehicle (FCEV) weight reduction efforts. The PDU distributes and controls a nominal 75 kilowatts of power generated by the Fuel Cell, the primary source of High Voltage power, to all the vehicle loads and accessories. The constraints imposed on the design of the PDU resulted in a component highly susceptible to general and galvanic corrosion. Corrosion abatement was the focus of the PDU redesign. This paper describes the redesign efforts undertaken by Ford personnel to improve the part robustness and corrosion resistance.

The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.

A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

Software development for automotive application requires several iterations in order to tune parameters and strategy logic to operate accordantly with optimal performance. Thus, in this paper we present an optimizer method and tool used to tune calibration parameters related to torque estimation for a hybrid automatic transmission application. This optimizer aims to minimize the time invested during the software calibration and software development phases that could take significant time in order to cover the different driving conditions under which a hybrid automatic transmission can operate. For this reason, an optimization function based on the Nelder-Mead simplex algorithm using Matlab software helps to find optimized calibration values based on a cost function (square sum error minimization).

Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling will become increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations.

As part of the International Lubricant Standardization and Approval Committee's (ILSAC) goal of developing a global automatic transmission fluid (ATF) specification, members have been evaluating test methods that are currently used by various automotive manufacturers for qualifying ATF for use in their respective transmissions. This report deals with comparing test methods used for determining torque capacity in friction systems (shifting clutches). Three test methods were compared, the Plate Friction Test from the General Motors DEXRON®-III Specification, the Friction Durability Test from the Ford MERCON® Specification, and the Japanese Automotive Manufacturers Association Friction Test - JASO Method 348-95. Eight different fluids were evaluated. Friction parameters used in the comparison were breakaway friction, dynamic friction torque at midpoint and the end of engagement, and the ratio of end torque to midpoint torque.

In order to evaluate the THOR-50M as a front impact Anthropomorphic Test Device (ATD) for vehicle safety design, the ATD was compared to the H3-50M in matching vehicle crash tests for 20 unique vehicle models from 2 vehicle manufacturers. For the belted driver condition, a total of fifty-four crash tests were investigated in the 56.3 km/h (35 mph) front rigid barrier impact condition. Four more tests were compared for the unbelted driver and right front passenger at 40.2 km/h (25 mph) in the flat frontal and 30-degree right oblique rigid barrier impact conditions. The two ATDs were also evaluated for their ability to predict injury risk by comparing their fleet average injury risk to Crash Investigation Sampling System (CISS) accident data for similar conditions. The differences in seating position and their effect on ATD responses were also investigated.

Described in this paper is a component test methodology to evaluate the door trim armrest performance in an Insurance Institute for Highway Safety (IIHS) side impact test and to predict the SID-IIs abdomen injury metrics (rib deflection, deflection rate and V*C). The test methodology consisted of a sub-assembly of two SID-IIs abdomen ribs with spine box, mounted on a linear bearing and allowed to translate in the direction of impact. The spine box with the assembly of two abdominal ribs was rigidly attached to the sliding test fixture, and is stationary at the start of the test. The door trim armrest was mounted on the impactor, which was prescribed the door velocity profile obtained from full-vehicle test. The location and orientation of the armrest relative to the dummy abdomen ribs was maintained the same as in the full-vehicle test.

As part of an ongoing technical collaboration between Ford and Rouge Steel Company, a comprehensive study of door slam event was undertaken. The experimental phase of the project involved measurements of accelerations at eight locations on the outer panel and strains on six locations of the inner panel. Although slam tests were conducted with window up and window down, results of only one test is presented in this paper. The CAE phase of the project involved the development of suitable “math” model of the door assembly and analysis methodology to capture the dynamics of the event. The predictability of the CAE method is examined through detailed comparison of accelerations and strains. While excellent agreement between CAE and test results of accelerations on the outer panel is obtained, the analysis predicts higher strains on the inner panel than the test. In addition, the tendency of outer panel to elastically buckle is examined.

This paper describes the testing and constitutive model development of polyurethane foams for characterization of their material dynamic properties. These properties are needed not only for understanding their behavior, but also for supplying essential input data to foam models, which help provide design directions through simulations of foam selection for cushioning occupant head impacts against the vehicle door and upper interior. Polyurethane foams of varying densities were tested statically and dynamically under uniaxial compressive impact loading at constant velocities of various rates and different temperatures. The test results were utilized for developing a constitutive model of polyurethane foams by taking the density, strain rate and temperature effects into consideration. Uniaxial constitutive models are developed in two ways.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production.

In this paper, we proposed a distributed Engineering Computer Aided Learning System. Instead of attending engineering teaching sessions, engineering students are able to interact with the software to gain the same amount of teaching materials. Besides, they will interact with other engineering students from other Engineering schools. The proposed software has the ability to examine the student step by step to reach certain goals. The training and the examination will be different based on the student level and his learning process. Using this system the role of excellent professor can be achieved. The software will have two sessions, i.e. test session and learning session. The software provides the capability of knowledge sharing between multi schools and different educational systems that can provide the students with a large set of training materials. The system was built using JAVA programming language.

Full vehicle dynamic crash tests are commonly used in the development of rollover detection sensors, algorithms and occupant protection systems. However, many published studies have utilized component level rollover test fixtures for rollover related occupant kinematics studies and restraint system evaluation and development. A majority of these fixtures attempted to replicate only the rotational motion that occurs during the free flight phase of a typical full vehicle rollover crash test. In this paper, a description of the methods used to design a new dynamic component rollover test device is presented. A brief summary of several existing rollover component test methods is included. The new system described in this paper is capable of replicating the transfer of lateral energy into rotational vehicle motion that is present in many tripped laboratory based rollover crash tests.