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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 paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The path planning algorithm described here generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles. The primary objective is to ensure safe navigation in environments where static obstacles are prevalent. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller is utilized as lateral control to follow the generated path, ensuring
When the flow of fluid within a high-pressure line is abruptly halted, pressure pulsations are generated. This phenomenon is known as the water hammer effect. This may lead to significant stress and, in the worst-case scenario, results in various types of failures within the highly pressurized system. Similar issues are observed in diesel high pressure fuel line where pressure is well above 1600 bar. Due to multiple injections on-off events, pressure pulsation gets created inside high pressure fuel lines (HPFL) which leads to problems such as high strain on high pressure fuel lines, mechanical damage, uneven fuel injected quantity, vibration beyond specification limits for rail pressure sensors or in worst case extreme noise. This is due to high pressure pulsation which occurs when fluid/fuel natural frequency resonates with structural HPFL natural frequency. In this work, A comparative FEA analysis is conducted to evaluate strain in two distinct high-pressure fuel lines, with pressure
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis
Generally, in an electric sports utility vehicle with rear mounted powertrain the mass distribution is greater in the rear compared to front. This higher rear to front weight distribution results in oversteer behavior during high-speed cornering deteriorating vehicle handling & risking passenger safety. To compensate this inherent oversteer nature of such vehicles & produce understeer behavior, the steering rack is placed frontwards of the front wheel center for toe-out behavior due to lateral compliance during cornering. This compensation measure results in lower Ackermann percentage resulting in higher turning circle diameter deteriorating vehicle maneuverability. This paper proposes a design to obtain ideal understeer gradient with minimal turning circle diameter through utilization of split link technology with a McPherson Strut based suspension framework & frontwards placed steering rack. This suspension is utilized in our Mahindra Inglo platform. This paper elaborates on how
This paper elucidates the implementation of software-controlled synchronous rectification and dead time configuration for bi-directional controlled DC motors. These motors are extensively utilized in applications such as robotics and automotive systems to prolong their operational lifespan. Synchronous rectification mitigates large current spikes in the H-bridge, reducing conduction losses and improving efficiency [1]. Dead time configuration prevents shoot-through conditions, enhancing motor efficiency and longevity. Experimental results demonstrate significant improvements in motor performance, including reduced thermal stress, decreased power consumption, and increased reliability [2]. The reduction in power consumption helps to minimize thermal stress, thereby enhancing the overall efficiency and longevity of the motor.
Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with