Daily Archives: April 3, 2025

Abstract-Internal combustion engines produce exhaust gases at high temperature and pressures. As these hot gases passes through the exhaust valve ,temperatures of the valve ,valve seat, and the stem increase .To avoid any damage to the exhaust valve assembly , heat is transferred from the exhaust valve from different parts ,Especially the valve seat insert during the opening and closing cycle as they come into contact each other . In this article, A Finite-Element method is used for modeling the transient thermal anaiysis of an exhaust valve .The temperature distribution and resultant thermal stresses at each opening and closing time are obtained. Detailed analyses are performed to estimate the boundary conditions of an internal combustion engine. The modeling includes exhaust valve, seat, guide, and spring. The analysis continues until a steady state condition is obtained. In this study ANSYS is employed for modeling and analysis of the exhaust valve. A Methodology is developed for transient thermal analysis of the exhaust valve.

Abstract-A secure bootloader plays a critical role in ensuring the integrity and authenticity of firmware during system startup. Traditional bootloaders lack robust security mechanisms, making them vulnerable to tampering and malware injections. Public Key Cryptography (PKC) provides a strong foundation for secure bootloaders by enabling digital signatures and authentication mechanisms to verify firmware integrity before execution. This research explores the design and implementation of a secure bootloader using Public Key Cryptography to prevent unauthorized modifications and ensure a trusted execution environment. The proposed bootloader employs asymmetric encryption to verify signed firmware images using RSA or ECC (Elliptic Curve Cryptography). Additionally, hash-based verification techniques such as SHA-256 are used to ensure firmware integrity. Experimental evaluations demonstrate that the proposed bootloader effectively prevents unauthorized code execution, ensuring a secure boot process with minimal performance overhead. This paper discusses the system architecture, cryptographic implementation, evaluation metrics, and future research directions for enhancing bootloader security.

Abstract-Micro turbines are becoming widely used for combined power generation and heat applications. Their size varies from small scale units like models crafts to heavy supply like power supply to hundreds of households. Micro turbines have many advantages over piston generators such as low emissions less moving parts, accepts commercial fuels. Gas turbine cycle and operation of micro turbine was studied and reported. Brief description on CAD software and CATIA studied and reported. Different parts (Inlet. Storage, Nozzle, Rotor, coupling, outlet, clip, housing) of turbine are designed with the help of CATIA (Computer Aided Three Dimensional Interactive Analysis) software. Then they were assembled to a single unit and coupled to a generator to produce power. The turbine is of Axial input and axial output type.

Abstract-The wheel hub and spindle are critical components in the suspension and steering system of a Formula Student car, directly influencing its vehicle dynamics, handling, and safety. This project focuses on the design, analysis, and optimization of the wheel hub and spindle assembly, aiming to ensure structural integrity, reduce weight, and improve overall performance under dynamic loading conditions. Using SolidWorks, the wheel hub and spindle were meticulously designed to meet the requirements of a Formula Student car, emphasizing lightweight construction while maintaining sufficient strength. The design process involved careful consideration of materials, geometry, and manufacturing feasibility to create a durable and efficient assembly. The geometry was optimized to reduce unsprung mass, which is crucial for enhancing vehicle stability and handling. The designed components were analyzed using ANSYS Workbench to simulate real-world conditions and assess their structural performance. Static and dynamic load analyses were performed to evaluate stress distribution, deformation, and factor of safety under various scenarios, such as cornering, braking, and acceleration. The results of the analysis demonstrate that the design meets the functional and safety criteria for a Formula Student car, ensuring reliability during competitive racing conditions. This study provides valuable insights into the integration of design and analysis tools for optimizing vehicle dynamics, contributing to the advancement of motorsport engineering.

Abstract-Optimizing the orientation of a part in the Material Extrusion (MEX) process is crucial for reducing print time, energy consumption, and material waste while maintaining part quality. This study focuses on optimizing the printing orientation and layer height using the Taguchi method in Minitab to achieve an efficient and sustainable additive manufacturing process. The research employs the Taguchi Design of Experiments (DOE) approach to systematically evaluate the effects of different orientation angles and layer heights on print time and energy consumption. Experiments were conducted by printing samples at various orientations and layer thicknesses, and the response variables—total printing time and energy usage—were recorded. Signal-to-noise (S/N) ratios were analyzed in Minitab to determine the optimal parameter settings that minimize both print time and energy usage. The results indicate that the print orientation significantly affects deposition path efficiency, while layer height plays a key role in determining the number of layers and total energy required. The optimized orientation and layer height configuration led to a substantial reduction in energy consumption without compromising part accuracy and mechanical integrity. This study demonstrates that using the Taguchi method in Minitab for orientation optimization provides a structured and statistical approach to improving additive manufacturing efficiency. The findings can be applied to enhance the sustainability of FDM-based 3D printing, reducing material wastage and operational costs while improving process efficiency.

Abstract-This research delves into an innovative app that seamlessly integrates AI-driven traffic management with emergency ambulance services, presenting users with a holistic solution to address both transportation efficiency and urgent medical needs. By harnessing advanced algorithms and real-time data analysis, the app optimizes traffic flow, reduces congestion, and delivers personalized travel routes tailored to the prevailing conditions. Moreover, it offers a dedicated emergency ambulance service equipped with priority response times and specialized care for its subscribers. The subscription-based model of the app affords users a plethora of benefits, including access to enhanced features, exclusive discounts on additional healthcare services, and a flexible payment structure catering to diverse user preferences. In .essence, the app epitomizes the convergence of technology and human-centric design, empowering users to navigate urban landscapes safely and efficiently while ensuring immediate access to critical medical assistance when circumstances demand. Through its innovative approach and commitment to user-centricity, the app stands as a beacon of progress in shaping the future of mobility and healthcare accessibility in the communities.

Abstract-Brake pedals are critical components in automotive applications, requiring a balance between high stiffness, low weight, and manufacturability. Traditional design approaches often result in suboptimal structures with excessive material usage. This study explores the generative design optimization of a brake pedal using Fusion 360, targeting maximum stiffness and minimum mass while considering different manufacturing constraints for milling and additive manufacturing (AM). Generative design algorithms were employed to generate multiple optimized pedal designs by defining material properties, boundary conditions, and load cases. The milling-based design focused on constraints like tool access, machining orientations, and material removal feasibility, whereas the AM-based design leveraged organic lattice structures and topology optimization to achieve minimal material usage while maintaining structural integrity. The optimized models were analyzed using finite element analysis (FEA) to compare stress distribution, deformation, and weight reduction for both manufacturing methods. Results indicate that additive manufacturing allows for a more complex, lightweight design with internal lattice structures, resulting in a higher stiffness-to-weight ratio compared to the milling approach. However, the milled design exhibits superior fatigue resistance and is better suited for high-load conditions due to the absence of microstructural porosity. A comparative evaluation of material usage, manufacturing feasibility, and mechanical performance highlights the trade-offs between AM and milling-based designs. This research demonstrates how generative design tools can optimize brake pedal geometry for different manufacturing processes, leading to weight savings and enhanced performance while ensuring manufacturability. The findings provide valuable insights into process-dependent design optimizations and serve as a reference for future lightweight automotive component development.

Abstract-The Material Extrusion (MEX) process, commonly known as Fused Deposition Modeling (FDM), is widely used for manufacturing complex geometries with minimal material wastage. However, optimizing key printing parameters is crucial for improving efficiency while maintaining print quality. This study focuses on enhancing the MEX process using Cura 5.9.0 and the B to optimize layer height, line width, and wall count, aiming to reduce print time and material consumption. The Taguchi DOE method was employed to systematically analyze the effects of these parameters on printing performance. A set of experiments was conducted by varying layer height, line width, and wall count within practical limits. The primary objectives were to minimize printing time and optimize material usage while maintaining structural integrity. Print time and material consumption were recorded for each experiment, and statistical analysis was performed using Minitab to determine the optimal parameter combination. The results show that layer height significantly influences printing time, as higher layer heights reduce the number of layers but may impact surface quality. Line width affects material flow and print strength, while wall count directly impacts material consumption. The optimized parameter settings achieved a significant reduction in print time and material usage, ensuring an efficient balance between speed, material economy, and part durability. This study demonstrates that using Cura 5.9.0 in combination with DOE techniques provides a structured methodology for enhancing the efficiency of the MEX process. The findings are beneficial for industries and researchers seeking to optimize print settings, reduce operational costs, and improve sustainability in additive manufacturing.

Abstract-The integration of neural networks and machine learning technologies in allied healthcare has the potential to revolutionize diagnostic accuracy, treatment personalization, and patient care. This study focuses on practical applications and strategies for implementing these advanced technologies to optimize healthcare processes in real-world scenarios. By leveraging artificial intelligence, this study seeks to enhance diagnostic imaging, predictive analytics, personalized treatment plans, and remote patient monitoring. A key innovation explored is the Geo Health Sync ID, a centralized health record system designed to improve diagnosis accuracy, streamline medical histories, and enhance treatment outcomes by enabling global access to healthcare data. Additionally, this is a proposal of the development of an AI-powered chatbot and wearable device that utilizes neural networks to monitor patient vitals in real-time, detect anomalies, and provide early alerts to healthcare providers. Addressing challenges such as data privacy, AI model fairness, and seamless clinical integration, this study aims to bridge existing gaps and establish a more efficient, patient-centric healthcare ecosystem. This initiative holds the potential to transform allied healthcare by improving patient outcomes, reducing healthcare costs, and driving innovation through AI-driven decision-making and automation. With the rapid advancements in artificial intelligence (AI), neural networks and machine learning have become integral to allied healthcare. These technologies offer predictive analytics, disease diagnosis, treatment recommendations, and administrative efficiency. Machine learning algorithms, particularly deep learning models, process vast amounts of healthcare data, enhancing accuracy in medical imaging, patient monitoring, and personalized medicine. This paper explores the applications, benefits, challenges, and future scope of neural networks in allied healthcare, including real-world case studies and implementation strategies.

Abstract-The Automobile Industry has shown keen interest for replacement of steel leaf spring with that of glass fiber composite leaf spring, since the composite material has high strength to weight ratio, good corrosion resistance properties. The present study searches the new material for leaf spring. In present study the material selected was glass fiber reinforced plastic (GFRP) and the epoxy resin is used against conventional steel. A spring with constant width and thickness was fabricated by hand lay-up technique which was very simple and economical. The numerical analysis is carried via finite element analysis using ANSYS software. Stresses, deflection and strain energy results for both steel and composite leaf spring material were obtained. Result shows that, the composite spring has maximum strain energy than steel leaf spring and weight of composite spring was nearly reduced up to 85% compared with steel material. This paper describes design and FEA analysis of composite leaf spring made of glass fiber reinforced polymer. The dimensions of an existing conventional steel leaf spring of a light commercial vehicle are taken for evaluation of results.