Browse Topic: Analysis methodologies
This paper presents the first systematic examination of Large Language Model (LLM) capabilities for automating the development of Failure Mode and Effects Analysis (FMEA) utilizing architectural diagrams as input. Although prior research has examined LLMs for FMEA tasks, our methodology incorporates innovative aspects, such as the direct analysis of architectural diagrams for component extraction, prediction of failure modes, causes, estimation of risk and a human-in-the-loop (Hu-IL) validation framework. We examine the capability of general-purpose LLMs to accurately automate the creation of FMEA by formulating a methodology that extracts components and signals from architectural diagrams, conducts automated component classification, and produces a comprehensive FMEA form sheet encompassing Severity, Occurrence, and Detectability (S/O/D) scoring. Our methodology is grounded in structured prompt engineering theory, utilizing scope bounding techniques to reduce hallucination while
Weather-strip sealing systems are critical to automotive closure performance, influencing water- and dust-tightness, aerodynamic noise control, and overall NVH quality. Conventional validation often relies on flat or straight JIG-based tests that inadequately represent the curved, angled, and non-uniform geometries of real closures such as doors, tailgates, hoods, roofs, and fixed or movable glass. This disparity limits the predictive accuracy of sealing performance in actual vehicles. This study proposes a vehicle-integrated validation framework that mirrors true geometric and contact conditions. The methodology combines finite element analysis (FEA) of both flat JIG and full-vehicle CAD geometries with experimental JIG tests, establishing a baseline for pressure distribution, compression load, and sealing contact behavior. A comparative analysis highlights significant deviations between flat-section predictions and vehicle-specific closure profiles. Results demonstrate that the
A computational study using the Volume of Fluid (VOF) method in SimericsMP+ was conducted to investigate fuel sloshing in automotive fuel tanks under both crash and sudden stop conditions. The SEALs method was employed to rapidly generate the fuel tank mesh, enabling efficient simulation setup. At the outset, a benchmark sloshing case was simulated and compared against experimental data, showing excellent agreement to validate the simulation method. This simulation method was then applied to the fuel tank sloshing scenarios mimicking crash and sudden stop conditions. The study initially focused on a crash scenario in which fuel waves impact valves, pumps, and other internal structures. Capturing these localized impact forces is critical for evaluating the risk of component failure and potential leakage. A baffle-equipped tank was simulated and compared with sensor data. Results show that the computed shock forces on valves and baffles closely matched the measurements, demonstrating the
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with the widely used Si-IGBT. However, SiC MOSFET is a more expensive device, installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET from over temperature. When a junction temperature estimation
The Audio system is an important part of the design of a vehicle cabin. In the vehicle development process, the audio system needs to be tuned for optimal acoustic performance. Traditionally, this process is performed physically on vehicles. In this paper, a methodology is developed to numerically simulate the acoustic performance of the audio system across the full audible frequency range. To provide validation of the method, the p/v acoustic transfer functions (ie., the sound pressure p at the passengers’ ears divided by the voltage inputs v) are measured for different speakers in a production vehicle. As the sound perceived by the passengers depends on both the source and the path, the method development is split into two parts: (a) characterization of parameters that describe the loudspeaker as a source and (b) representation of the vehicle cabin as a path. The speaker parameters are characterized from sound radiation data measured in a 2pi chamber. To represent the vehicle cabin
In recent years, computer-aided engineering (CAE) has become an essential practice in design and durability analysis of industrial components such as weldments. The current analytical trend for CAE-based fatigue life prediction of weldments includes procedures based on design guidelines, mesh-sensitive methods (e.g., local strain-life approach) and mesh insensitive methods (e.g., Volvo and Verity methods). As an inherent characteristic of weldments, the geometry of the weld is often simplified in failure analysis and important hotspots such as start/stop of the weld beads are not considered in the design process. However, such critical locations cannot be avoided in complex welded structures. Therefore, incorporating main geometrical details of the weld can improve the accuracy of critical regions identification and damage calculation using mesh-sensitive CAE-based methodologies. Herein, a framework for life prediction of welded components including the weld geometry is discussed and
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