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Browse AllThis specification covers a corrosion-resistant steel in the form of investment castings homogenized and solution and precipitation heat treated to 180 ksi (1241 MPa) tensile strength.
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical
As vehicles are becoming more complex, maintaining the effectiveness of safety critical systems like adaptive cruise control, lane keep assist, electronic breaking and airbag deployment extends far beyond the initial design and manufacturing. In the automotive industry these safety systems must perform reliably over the years under varying environmental conditions. This paper examines the critical role of periodic maintenance in sustaining the long-term safety and functional integrity of these systems throughout the lifecycle. As per the latest data from the Ministry of Road Transport and Highways (MoRTH), in 2022, India reported a total of 4.61 lakh road accidents, resulting in 1.68 lakh fatalities and 4.43 lakh injuries. The number of fatalities could have been reduced by the intervention of periodic services and monitoring the health of safety critical systems. While periodic maintenance has contributed to long term safety of the vehicles, there are a lot of vehicles on the road
Advanced Driver Assistance Systems (ADAS) have become increasingly prevalent in modern vehicles, promising improved safety and reducing accidents. However, their implementation comes with several challenges and limitations. The efficacy of these systems in diverse and challenging road conditions of India, remains as a concern. For deeper understanding of the ADAS feature related concerns in Indian market due to the factors such as unique road conditions, traffic situations, driving patterns, an extensive study was done throughout Indian terrain. The functionality and performance of different ADAS features were evaluated in the real-world scenarios. The objective data of the observations and occurrence conditions were captured with help of data loggers & camera setups inside the vehicle. This research paper represents a comprehensive study on the challenges faced by user while using ADAS enabled cars in Indian road conditions. We captured the performance data of various ADAS features
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
Internal Combustion Engine (ICE) is the heart of an Automobile. The failure of any critical component of the ICE engine will directly affect the performance of the vehicle. The gaskets are among the many vital parts of an IC engine that are essential in ensuring appropriate sealing to prevent gas and liquid leakage and maintain optimal engine efficiency. Engines use a variety of gasket types to accommodate various sealing requirements. Among them the exhaust manifold gaskets are one of the critical gasket elements in ICE engines. Exhaust Gasket acts as a seal between cylinder head and extremely hot exhaust manifold, which prevents the leakage of hot exhaust gases produced during typical engine operating condition. The gaskets are crucial components because they endure extremely high mechanical loads from the exhaust manifold sliding and banana-shaped bending brought on by thermal expansion, as well as extremely high thermal loads from the high exhaust gas temperatures, which are more
In electric and hybrid vehicles, sound package optimization can follow a classical, proven, and structured approach for real-world loads, while also considering new transmission paths that might differ from those in traditional internal combustion engine vehicles. However, AVAS-induced interior noise is sometimes underestimated and therefore not taken into account during the optimization process. Nevertheless, especially at very low speeds, the presence of the AVAS can be perceived as unwanted noise inside the vehicle, potentially compromising interior comfort. In this study, a hybrid boundary element–statistical energy analysis (BEM – SEA) approach is applied to an SEA dual-motor electric vehicle demonstrator model equipped with a baseline, standard sound package to assess AVAS-induced interior noise. A standard AVAS actuator is modeled with a BEM model to compute the sound pressure levels on the exterior subsystems of the vehicle. These results are then transferred to the SEA model
Higher road noise is perceived in the cabin when the test vehicle encounters road irregularities like bump or pothole in the public roads. The transfer of transient road inputs inside the body caused objectionable cabin noise. Measurements are conducted at different road surfaces to identify the patch where the objective data well correlated with the noise measured at the public road. Wavelet analysis is carried out to identify the frequency zones since the events are transient in nature. TPA is carried out in time domain to identify the nature of the noise and the dominant path through which the transient road forces are transferring inside the body. Based on the outcome of TPA, various countermeasures like reduction of dynamic stiffness of suspension bushes, TMDs on the path are proposed to reduce the structure borne noise. Criteria which need to be considered for reduction of cabin noise due to transient road inputs is also discussed.