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Safety continues to be one of the most important factors in motor vehicle design, manufacturing, and marketing.  This course provides a comprehensive overview of these critical automotive safety considerations: injury and anatomy; human tolerance and biomechanics; occupant protection; testing; and federal legislation. The knowledge shared at this course enables participants to be more aware of safety considerations and to better understand and interact with safety experts. This course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 18 Continuing Education Units (CEUs).

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

This title includes the technical papers developed for the 2023 Stapp Car Crash Conference, the premier forum for the presentation of research in impact biomechanics, human injury tolerance, and related fields, advancing the knowledge of land-vehicle crash injury protection. The conference provides an opportunity to participate in open discussion about the causes and mechanisms of injury, experimental methods and tools for use in impact biomechanics research, and the development of new concepts for reducing injuries and fatalities in automobile crashes.

This research aims to develop an inverse control method capable of adaptively simulating dynamic models of car subsystems in the rig-test condition. Accurate simulation of the actual vibration conditions is one of the most crucial factors in realizing reliable rig-test platforms. However, most typical rig tests are conducted under simple random or harmonic sweep conditions. Moreover, the conventional test methods are hard to directly adapt to the actual vibration conditions when switching the dynamic characteristics of the subsystem in the rig test. In the present work, we developed an inverse controller to adaptively control the vibration exciter referring to the target vibration signal. An adaptive LMS filter, employed for the control algorithm, updated the filter weights in real time by referring to the target and the measured acceleration signals.

Design and Manufacturing of an Inclinometer sensing element for launch vehicle applications Tony M Shaju, Nirmal Krishna, G Nagamalleswara Rao, Pradeep K Scientist/Engineer, ISRO Inertial Systems Unit, Vattiyoorkavu, Trivandrum, India - 695013 Indian Space Research Organisation (ISRO) uses indigenously developed launch vehicles like PSLV, GSLV, LVM3 and SSLV for placing remote sensing and communication satellites along with spacecrafts for other important scientific applications into earth bound orbits. Navigation systems present in the launch vehicle play a pivotal role in achieving the intended orbits for these spacecrafts. During the assembly of these navigation packages on the launch vehicle, it is required to measure the initial tilt of the navigation sensors for any misalignment corrections, which is given as input to the navigation software. A high precision inclinometer is required to measure these tilts with a resolution of 1 arc-second.

The descent phase of GAGANYAAN (Indian Manned Space Mission) culminates with a crew module impacting at a predetermined site in Indian waters. During water impact, huge amount of loads are experienced by the astronauts. This demands an impact attenuation system which can attenuate the impact loads and reduce the acceleration experienced by astronauts to safe levels. Current state of the art impact attenuation systems use honeycomb core, which is passive, expendable, can only be used once (at touchdown impact) during the entire mission and does not account off-nominal impact loads. Active and reusable attenuation systems for crew module is still an unexplored territory. Three configurations of impact attenuators were selected for this study for the current GAGANYAAN crew module configuration, namely, hydraulic damper, hydro-pneumatic damper and airbag systems.

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

Evolving to MedDev provides a new opportunity for executives in aerospace, automotive and medical devices companies to connect and develop long-term growth strategies and find ways to meet the increased short-term demand for medical supplies

The paramount importance of titanium alloy in implant materials stems from its exceptional qualities, yet the optimization of bone integration and mitigation of wear and corrosion necessitate advanced technologies. Consequently, there has been a surge in research efforts focusing on surface modification of biomaterials to meet these challenges. This project is dedicated to enhancing the surface of titanium alloys by employing shot peening and powder coatings of titanium oxide and zinc oxide. Comparative analyses were meticulously conducted on the mechanical and wear properties of both treated and untreated specimens, ensuring uniformity in pressure, distance, and time parameters across all experiments. The outcomes underscore the efficacy of both methods in modifying the surface of the titanium alloy, leading to substantial alterations in surface properties.

This document covers the recommended practice for determining the acceptability of the dendrite arm spacing (DAS) of D357-T6 aluminum alloy castings required to have tensile strength not lower than 50 ksi (345 MPa).

Abstract This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions.