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The automotive industry is currently experiencing a massive transformation, one like it has not quite seen in the past. With the advent of highly software-driven, always on, connected vehicles, the automotive industry is experiencing itself at a crossroads. While the traditional component-driven design approach to vehicle development worked in the favor of the industry for decades due to vehicles being mostly mechanical in nature, the industry now finds itself struggling to develop well-integrated vehicle solutions with the large dependency on software systems. The fast-paced nature of the software world makes it imperative to approach the development of automobiles from a Systems Engineering perspective. A function-based approach to the development of vehicle architectures can ensure cohesive systems development and a well-integrated vehicle.

This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

Metal cutting/machining is a widely used manufacturing process for producing high-precision parts at a low cost and with high throughput. In the automotive industry, engine components such as cylinder heads or engine blocks are all manufactured using such processes. Despite its cost benefits, manufacturers often face the problem of machining chips and cutting oil residue remaining on the finished surface or falling into the internal cavities after machining operations, and these wastes can be very difficult to clean. While part cleaning/washing equipment suppliers often claim that their washers have superior performance, determining the washing efficiency is challenging without means to visualize the water flow. In this paper, a virtual engineering methodology using particle-based CFD is developed to address the issue of metal chip cleanliness resulting from engine component machining operations. This methodology comprises two simulation methods.

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.

A DMS (Driver Monitoring System) is one of the most important safety features that assist in the monitoring functions and alert drivers when distraction or drowsiness is detected. The system is based in a DSMC (Driver Status Monitoring Camera) mounted in the vehicle's dash, which has a predefined set of operational requirements that must be fulfilled to guarantee the correct operation of the system. These conditions represent a trade space analysis challenge for each vehicle since both the DSMC and the underlying vehicle’s requirements must be satisfied. Relying upon the camera’s manufacturer evaluation for every iteration of the vehicle’s design has proven to be time-consuming, resources-intensive, and ineffective from the decision-making standpoint.

Auto industry has faced constant challenges in the economic, technology and global trend in the recent years. This is changing the corporative mindset to find creative and innovative processes and methods to evolve the product development system to adjust and deliver competitive products that satisfy customers expectations. Integrating the work from different teams in an organization has been moving from simple roles and responsibilities definition with effective communication channels to a new vision where teamwork progresses in harmony and embraces change to satisfy customers as part of the process. The path to evolve work in engineering that relies on several computational tools continues. In this article, it is presented an integration of different tools to manage vehicle program changes using model-based systems engineering, the present work improves the reaction capabilities of the teams and enables to adjust to changes in the development of a vehicle.

Fretting fatigue was observed in standard cylindrical fatigue samples at the regions in contact with the grips of the test frames during fatigue testing for AlSi10Mg aluminum alloy produced by laser powder bed fusion process (L-PBF). The failure of the fatigue sample grips occurs much earlier than the failure of the gauge section. This results in a damaged sample and the sample cannot be reused to continue the test. This type of failure is rarely seen in materials produced by traditional manufacturing processes. In this study, X-ray residual stress analysis was performed to understand the cause of failure for L-PBF AlSi10Mg with the as-built surface condition. The result indicates that the fretting fatigue failure was caused by the strong tensile residual stress in the as-built state combining with the fretting wear between the sample and the grip. A few potential solutions to avoid the fretting fatigue failure were investigated.

In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal assignments with the directional costs as the output. The tool has been tested for several plants at Ford and has proved its robustness by saving millions of dollars. The developed tool can easily be applied to other manufacturing systems and original equipment manufacturers (OEMs).

In modern automobiles, many new complex features are enabled by software and sensors. When combined with the variability of real-world environments and scenarios, validation of this ever-increasing amount of software becomes complex, costly, and takes a lot of time. This challenges automakers ability to quickly and reliably develop and deploy new features and experiences that their customers want in the marketplace. While traditional validation methods and modern virtual validation environments can cover most new feature testing, it is challenging to cover certain real-world scenarios. These scenarios include variation in weather conditions, roadway environments, driver usage, and complex vehicle interactions. The current approach to covering these scenarios often relies on data collected from long vehicle test trips that try to capture as many of these unique situations as possible. These test trips contribute significantly to the validation cost and time of new features.

Laser welding of the magnesium-bearing AA5xxx aluminum alloys is often beset by keyhole instability, especially in the lap through joint configuration. This phenomenon is characterized by periodic collapse of the keyhole leaving large voids in the weld zone. In addition, the top surface can exhibit undercut and roughness. In full penetration welds, keyhole instability can also produce a spikey root and severe top surface concavity. These discontinuities could prevent a weld from achieving engineering specification compliance, pose a craftsmanship concern, or reduce the strength and fatigue performance of the weld. In the case of a full penetration weld, a spikey root could compromise part fit-up and corrosion protection, or damage adjacent sheet metal, wiring, interior components, or trim.

Studies regarding the automotive communication networks has been a fast-growing subject of research through automotive companies. One of the main issues during a vehicle network prototype development is the necessity of a programmed hardware for gateway modules. The gateway developed by an automotive supplier, usually implemented at an Electronic Control Unit (ECU), is one of the last remaining modules to have a software available because it needs to wait the message list completion of all ECUs prior to set up its own message list and network interfaces. This paper proposes a method to design and test Controlled Area Network (CAN) gateways using Vector CANoe software by developing ways to facilitate a scalable code production in CAPL scripts. Use cases will use gatewayed signals virtually validated prior to software deployment, along with codes proposals and common cases examples.

The automotive industry is passing for a big transformation, due to technologies advance. The electrical technologies are also on a good rising curve, calling the attention of the Original Equipment Manufacturer (OEMs). This scenario generates the demand for a faster method to train their new hired engineers, when compared with usual on the job training. Model in the Loop (MiL) consists in one of the real-time embedded systems test phases, which is developed in a computational environment, performing a mathematical modeling of the system, presenting an interface that allows the visualization of its dynamics and the signals involved. Two powerful software in industry that apply MiL are the Matlab and Simulink. A project involving these applications was proposed for a team of new hired engineers, developing models of several vehicle Electronic Control Units (ECUs), with some scope reduction as an example the functional requirements reduction.

Exhaust sensors and actuators used in automotive applications are subjected to wide variety of operating ambient conditions , the performance of these actuators is challenging especially at cold ambient operating conditions, active exhaust tuning valves with position sensors are used to adjust the sound levels, or noise, vibration and harshness (NVH) from a control unit within the vehicle that leads to an improved driving experience wherein the driver selects their preferred sound levels. However, the operating behavior is crucially influenced by the characteristics of the drive cycle and ambient temperature. The study in this paper is intended to evaluate the icing formation at the start of drive cycle and at different ambient temperature conditions. The test data were obtained through real road and chassis dyno testing at different ambient conditions.

This paper presents a decoupled solution for mapping and validating complex and dynamic user interfaces (UI). Creating unique and satisfying user experiences are becoming the focus of products whereas digital user interfaces are a big part of this delivery. This tendency is coming to complex real-time systems, thus, growing the need of a proper validation of digital UIs considering its intrinsic requirements and limitations. The previous framework that ran the touchscreen tests required changes in case of UI updates while the matrix-like structure proposed gives a correlation between all to all clickable objects thus mapping all possible pathways to the many different screens.

A wet clutch model is required in automotive propulsion system simulations for enabling robust design and control development. It commonly assumes Coulomb friction for simplicity, even though it does not represent the physics of hydrodynamic torque transfer. In practice, the Coulomb friction coefficient is treated as a tuning parameter in simulations to match vehicle data for targeted conditions. The simulations tend to deviate from actual behaviors for different drive conditions unless the friction coefficient is adjusted repeatedly. Alternatively, a complex hydrodynamic model, coupled with a surface contact model, is utilized to enhance the fidelity of system simulations for broader conditions. The theory of elastic asperity deformation is conventionally employed to model clutch surface contact. However, recent examination of friction material shows that the elastic modulus of surface fibers significantly exceeds the contact load, implying no deformation of fibers.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARs and RADARs. This information is processed by the on-board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this could result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge.

With the rapid development of artificial intelligence, the autonomous vehicles (AV) have attracted considerable attention in the automotive industry. However, different factors negatively impact the adoption of the AVs, delaying their successful commercialization. Accretion of atmospheric icing, especially wet snow, on AV sensors causes blockage on their lenses, making them prone to lose their sight, in turn, increasing potential chances of accidents. In this study, two different designs are proposed in order to prevent snow accretion on the lenses of AVs via air flow across the lens surface. In both designs, lenses made of plain glass and superhydrophobic coated glass surfaces are tested. While some researchers have shown promise of water repellency on superhydrophobic surfaces, more snow accretion is observed on the superhydrophobic surfaces, when compared to the plain glass lenses.

As an alternative lightweight material, chopped carbon fiber reinforced Sheet Molding Compound (SMC) composites, formed by compression molding, provide a new material for automotive applications. In the present study, the monotonic and fatigue behavior of chopped carbon fiber reinforced SMC is investigated. Tensile tests were conducted on coupons with three different gauge length, and size effect was observed on the fracture strength. Since the fiber bundle is randomly distributed in the SMC plaques, a digital image correlation (DIC) system was used to obtain the local modulus distribution along the gauge section for each coupon. It was found that there is a relationship between the local modulus distribution and the final fracture location under tensile loading. The fatigue behavior under tension-tension (R=0.1) and tension-compression (R=-1) has also been evaluated.

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.