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Browse AllTraditional vehicle diagnostics often rely on manual inspections and diagnostic tools, which can be time-consuming, inconsistent, and prone to human error. As vehicle technology evolves, there is a growing need for more efficient and reliable diagnostic methods. This paper introduces an innovative AI-based diagnostic system utilizing Artificial Intelligence (AI) to provide expert-level analysis and solutions for automotive issues. By inputting various details such as the vehicle’s make, model, year, mileage, problem description, and symptoms, the AI system generates comprehensive diagnostics, identifies potential causes, suggests step-by-step repair solutions, and offers maintenance tips. The proposed system aims to enhance diagnostic accuracy and efficiency, ultimately benefiting mechanics and vehicle owners. The system’s effectiveness is evaluated through various experiments and case studies, showcasing its potential to revolutionize vehicle diagnostics
In highly populated countries two-wheelers are the most convenient mode of transportation. But at the same time, these vehicles consume more fuel and produces emissions in urban driving. This work is aimed at developing a hybrid two-wheeler for reducing fuel consumption and emissions by incorporating electric vehicle technology in a conventional two-wheeler. The hybrid electric scooter (HES) made consisted of an electric hub motor in the front wheel as the prime mover for the electrical system. The powertrain of the HES was built using a parallel hybrid structure. The electric system is engaged during startup, low speeds, and idling, with a simple switch facilitating the transition between electric and fuel systems. The HES was fabricated and tested through trial runs in various operating modes. Before conversion to a hybrid system, the two-wheeler achieved a mileage of 34 km/liter. After conversion, the combined power sources resulted in an overall mileage of 55 km. It was observed
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
Radiation has garnered the most attention in the research that has been conducted on polyethylene sheets. According to the calculations, there were 145892.35 kGy in total radiation doses administered. An ultraviolet visible spectrophotometer was used to examine the impact that electron beam irradiation had on the optical constants. Two of the most crucial variables taken into account when calculating the optical constants and the absorption coefficient are the reflectance and transmittance of polyurethane sheets. Reduced light transmission through the sheet achieves these characteristics, which are related to the transmittance and reflectance of the Fresnel interface. Cross linking makes it more challenging for the polyurethane molecular chains to become fixed. Both the refractive index and the dispersion properties have been altered as a direct result of this. Despite the fact that the doses of electron irradiation were getting lower, it eventually rose to 105 kGy. Contrary to the
This project presents the development of an advanced Autonomous Mobile Robot (AMR) designed to autonomously lift and maneuver four-wheel drive vehicles into parking spaces without human intervention. By leveraging cutting-edge camera and sensor technologies, the AMR integrates LIDAR for precise distance measurements and obstacle detection, high-resolution cameras for capturing detailed images of the parking environment, and object recognition algorithms for accurately identifying and selecting available parking spaces. These integrated technologies enable the AMR to navigate complex parking lots, optimize space utilization, and provide seamless automated parking. The AMR autonomously detects free parking spaces, lifts the vehicle, and parks it with high precision, making the entire parking process autonomous and highly efficient. This project pushes the boundaries of autonomous vehicle technology, aiming to contribute significantly to smarter and more efficient urban mobility systems
The properties of organic nitrate ester that inhibit scale formation were investigated in order to acquire a better understanding of ferrous carbide precipitation from supersaturated solutions. When the scale inhibitor was present, precipitation rates were much lower than when it was missing, even at very low concentrations. When the temperature and time are increased simultaneously, more scale is deposited. The effect of nitrate ester on scale deposition demonstrates that the inhibitory dosage is relatively low at low temperatures but rapidly increases when exposed to high temperatures. The inhibitor is thought to alter the shape of the first crystals by binding to dynamic growth sites and inhibiting the threshold level of development