Search Results

The safety and reliability of ground vehicles is a motivating factor for periodic maintenance which includes fluids, lubrication, cleaning, repairs, and general observation of key subsystems. The scheduling of maintenance activities can occur at different rates such as daily, weekly, or perhaps operating time based on collected historical data and general guidelines. The availability of a digital twin (DT), which offers a virtual representation of the vehicle behavior, enables virtual system simulations for different operating cycles to explore the dynamic behavior. When field operating fleet data can be integrated with the digital twin estimates, then this supplemental information can be combined with the existing maintenance plan to provide a more comprehensive approach. In this paper, a digital twin with a statistical based predictive maintenance strategy is investigated for a wheeled military ground vehicle.

In the ever-evolving landscape of automotive technology, the need for robust security measures and dependable vehicle performance has become paramount with connected vehicles and autonomous driving. The Unified Diagnostic Services (UDS) protocol is the diagnostic communication layer between various vehicle components which serves as a critical interface for vehicle servicing and for software updates. Fuzz testing is a dynamic software testing technique that involves the barrage of unexpected and invalid inputs to uncover vulnerabilities and erratic behavior. This paper presents the implementation of fuzz testing methodologies on the UDS layer, revealing the potential vulnerabilities that could be exploited by malicious entities. By employing both open-source and commercial fuzzing tools and techniques, this paper simulates real-world scenarios to assess the UDS layer’s resilience against anomalous data inputs.

Determining the validity of the design space early in the conceptualization of a project can make the difference between project success and failure. Early assessment of technical feasibility, project risk, technical readiness and realistic performance expectations based on models with different levels of fidelity, uncertainty, and technical robustness is a challenging mission critical task for large procurement projects. Tradespace exploration uses model-based engineering analysis, design exploration methods, and multi-objective optimization techniques to enable project stakeholders to make informed decisions and tradeoffs concerning the scope, schedule, budget, performance and risk profile of a project. As the intersection with a number of project stakeholders, tradespace studies can provide a significant impact upon the direction and decision-making in a project.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

Strategies for evaluating the impact of mission requirements on the design of mission-specific vehicles are needed to enable project managers to assess potential benefits and associated costs of changes in requirements. Top-level requirements that cause significant cascaded difficulties on lower-level requirements should be identified and presented to decision-makers. This paper aims to introduce formal methods and computational tools to enable the analysis and allocation of mission requirements.

Over the years, the complexity of autonomous vehicle development (and concurrently the verification and validation) has grown tremendously in terms of component-, subsystem- and system-level interactions between autonomy and the human users. Simulation-based testing holds significant promise in helping to identify both problematic interactions between component-, subsystem-, and system-levels as well as overcoming delays typically introduced by the default full-scale on-road testing. Software in Loop (SiL) simulation is utilized as an intermediate step towards software deployment for autonomous vehicles (AV) to make them reliable. SiL efforts can help reduce the resources required for successful deployment by helping to validate the software for millions of road miles. A key enabler for accelerating SiL processes is the ability to use Simulation as a Service (SaaS) rather than just isolated instances of software.

This paper argues the importance of engineering heuristics and introduces an educational data-driven tool to help novice engineers develop their engineering heuristics more effectively. The main objective in engineering practice is to identify opportunities for improvement and apply methods to effect change. Engineers do so by applying ‘how to’ knowledge to make decisions and take actions. This ‘how to’ knowledge is encoded in engineering heuristics. In this paper, we describe a tool that aims to provide heuristic knowledge to users by giving them insight into heuristics applied by experts in similar situations. A repository of automotive data is transformed into a tool with powerful search and data visualization functionalities. The tool can be used to educate novice automotive engineers alongside the current resource intensive practices of teaching engineering heuristics through social methods such as an apprenticeship.

Collaborative robots are more and more used in smart manufacturing because of their capability to work beside and collaborate with human workers. With the deployment of these robots, manufacturing tasks are more inclined to be accomplished by multiple humans and multiple robots (MH-MR) through teaming effort. In such MH-MR collaboration scenarios, the task distribution among the multiple humans and multiple robots is very critical to efficiency. It is also more challenging due to the heterogeneity of different agents. Existing approaches in task distribution among multiple agents mostly consider humans with assumed or known capabilities. However human capabilities are always changing due to various factors, which may lead to suboptimal efficiency. Although some researches have studied several human factors in manufacturing and applied them to adjust the robot task and behaviors.

Tensile behavior of advanced high strength steel (AHSS) grades with strengths up to 980 MPa has been extensively studied. However, limited data is found in literature on the tensile behavior of steels with tensile strengths of the order of 1180 MPa, especially at nominal strain rates up to 500/s. This paper examines tensile flow behavior to fracture of four different 1180 MPa grade steels at strain rates of 0.005/s, 0.5/s, 5/s, 50/s and 500/s using an experimental methodology that combines a servo-hydraulic tester and high speed digital image correlation. Even though the strength increase with the strain rate is consistent between the four different materials, the total elongation increase with the strain rate varies widely. Some insights as to why this occurs from examination of the steel microstructure and variation of retained austenite with strain are offered.

Adiabatic heating during plastic straining can slow the diffusionless shear transformation of austenite to martensite in steels that exhibit transformation induced plasticity (TRIP). However, the extent to which the transformation is affected over a strain rate range of relevance to automotive stamping and vehicle impact events is unclear for most third-generation advanced high strength TRIP steels. In this study, an 1180MPa minimum tensile strength TRIP steel with carbide-free bainite is evaluated by measuring the variation of retained austenite volume fraction (RAVF) in fractured tensile specimens with position and strain. This requires a combination of servo-hydraulic load frame instrumented with high speed stereo digital image correlation for measurement of strains and ex-situ synchrotron x-ray diffraction for determination of RAVF in fractured tensile specimens.

This article presents a pointing gesture-based point of interest computation method via pointing rays’ intersections for situated awareness interactions in vehicles. The proposed approach is compared with two alternative methods: (a) a point of interest identification method based on the intersection of the pointing ray with the point cloud (PoC) resulting from the vehicle sensors, and (b) the traditional ray-casting approach, where the point of interest is computed based on the first intersection of the pointing rays with locations stored in a 2D annotated map. Simulation results show that the presented method outperforms by 36.25% the traditional ray casting one. However, as it was expected, the sensor-based computation method is more accurate. The validation of our approach was conducted by experiments performed in a test track facility.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Hybrid vehicle embedded systems and payloads require progressively more accurate and versatile thermal control mechanisms and strategies capable of withstanding harsh environments and increasing power density. The division of the cargo and passenger compartments into convective thermal zones which are independently managed can lead to a manageable temperature control problem. This study investigates the performance of a Peltier-effect thermoelectric zone cooling system to regulate the temperature of target objects (e.g., electronic controllers, auxiliary computer equipment, etc) within ground vehicles. Multiple thermoelectric cooling modules (TEC) are integrated with convective cooling fans to provide chilled air for convective heat transfer from a robust, compact, and solid state device. A series of control strategies have been designed and evaluated to track a prescribed time-varying temperature profile while minimizing power consumption.

Fracture strain data provide essential information for material selection and serve as an important failure criterion in computer simulations of crash events. Traditionally, the fracture strain was measured by evaluating the thinning at fracture using tools such as a microscope or a point micrometer. In the recent decades, digital image correlation (DIC) has evolved as an advanced optical technique to record full-field strain history of materials during deformation. Using this technique, a complete set of the fracture strains (including major, minor, and thickness strains) can be approximated for the material. However, results directly obtained from the DIC can be dependent on the experiment setup and evaluation parameters, which potentially introduce errors to the reported values.

This paper presents development of a multi-scale material model for a 980 MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning heat treatment (QP980), based on integrated computational materials engineering principles (ICME Model). The model combines micro-scale material properties defined by the crystal plasticity theory with the macro-scale mechanical properties, such as flow curves under different loading paths. For an initial microstructure the flow curves of each of the constituent phases (ferrite, austenite, martensite) are computed based on the crystal plasticity theory and the crystal orientation distribution function. Phase properties are then used as an input to a state variable model that computes macro-scale flow curves while accounting for hardening caused by austenite transformation into martensite under different straining paths.

In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.

Autonomous driving technologies can provide better safety, comfort and efficiency for future transportation systems. Most research in this area has mainly been focused on developing sensing and control approaches to achieve various autonomous driving functions. Very little of this research, however, has studied how to efficiently handle sensing exceptions. A simple exception measured by any of the sensors may lead to failures in autonomous driving functions. The autonomous vehicles are then supposed to be sent back to manufacturers for repair, which takes both time and money. This paper introduces an efficient approach to make human drivers able to online teach autonomous vehicles to drive under sensing exceptions. A human-vehicle teaching-and-learning framework for autonomous driving is proposed and the human teaching and vehicle learning processes for handling sensing exceptions in autonomous vehicles are designed in detail.

The strain-induced diffusionless shear transformation of retained austenite to martensite during straining of transformation induced plasticity (TRIP) assisted steels increases strain hardening and delays necking and fracture leading to exceptional ductility and strength, which are attractive for automotive applications. A novel technique that provides the retained austenite volume fraction variation with strain with improved precision is presented. Digital images of the gauge section of tensile specimens were first recorded up to selected plastic strains with a stereo digital image correlation (DIC) system. The austenite volume fraction was measured by synchrotron X-ray diffraction from small squares cut from the gage section. Strain fields in the squares were then computed by localizing the strain measurement to the corresponding region of a given square during DIC post-processing of the images recorded during tensile testing.

The purpose of this research is to characterize the wear resistance of wheel treads made of polymeric woven and non-woven fabrics. Experimental research is used to characterize two wear mechanisms: (1) external wear due to large sliding between the tread and rocks, and (2) external wear due to small sliding between the tread and abrasive sand. Experimental setups include an abrasion tester and a small-scale merry-go-round where the tread is attached to a deformable rolling wheel. The wear resistance is characterized using various measures including, quantitatively, by the number of cycles to failure, and qualitatively, by micro-visual inspection of the fibers’ surface. This paper describes the issues related to each experiment and discusses the results obtained with different polymeric materials, fabric densities and sizes. The predominant wear mechanism is identified and should then be used as one of the criteria for further design of the tread.

Tires manufactured from polyurethane (PU) have been espoused recently for reduced hysteretic loss, but the material provides poor traction or poor wear resistance in the application, requiring inclusion of a traditional vulcanized rubber tread at the contact surface. The tread can be attached by adhesive methods after the PU body is cured, or the PU can be directly cured to reception sites on the rubber chain molecules unoccupied by crosslinked (vulcanizing) sulfur atoms. This paper provides a study of the two bonding options, both as-manufactured and after dynamic loading representative of tire performance in service. Models of each process are introduced, and an experimental comparison of the bonding strength between each method is made. Results are applied to tire fatigue simulation.