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1 | Faculty name | Faculty Member Name | Faculty contact number | Faculty Email ID | Faculty / Institute details | Department Name | Title of the project | Area of Research | Abstract (100 - 200 words) | Outside Co-mentor Details from industry or academia; Please provide the designation, affiliation, address and contact details etc. | ||||||||||||||||
2 | FOET | Dr. Prachi Vijaykumar Pandya | 9033964597 | pvp05@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Piezoelectric roads in smart cities: Innovation for Clean Energy and Sustainability | Environment and Sustainability | Piezoelectric roads represent a cutting-edge approach to integrating renewable energy generation into the infrastructure of smart cities. By embedding piezoelectric materials beneath road surfaces, mechanical stress generated by moving vehicles can be converted into electrical energy. This technology offers a dual benefit: harnessing clean energy from daily urban traffic and contributing to sustainable power solutions for city operations such as street lighting, electric vehicle charging stations, and sensor networks. The adoption of piezoelectric road systems aligns with global efforts to reduce carbon emissions and promote energy efficiency. However, challenges remain in terms of cost, material durability, and large-scale implementation. This paper explores the principles of piezoelectric energy conversion, evaluates current prototype installations, and discusses the potential socio-economic and environmental impacts of integrating piezoelectric roads within the smart city paradigm. Through a comprehensive review of technological advances and policy considerations, we argue that piezoelectric roads could play a meaningful role in the transition toward more resilient, green urban infrastructures. | na | ||||||||||||||||
3 | FOET | Jay D. Gohel | 7984382162 | 011151 | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Seismic Fragility Assessment of Asymmetric RC Buildings | Earthquake Engineering | This research will investigate the seismic vulnerability of asymmetric structures, a critical concern in structural engineering where plan irregularity leads to coupled torsional-translational responses that significantly increase damage potential. The primary objective of this study will be to perform a comprehensive Seismic Fragility Assessment of Asymmetric Reinforced Concrete (RC) Buildings to quantify the probability of structural damage under varying levels of ground motion intensity. The study will focus on multi-story RC frames with varying degrees of plan asymmetry, ranging from moderate to extreme eccentricity between the centre of mass (CM) and the centre of rigidity (CR). To capture the complex nonlinear behaviour of these systems, three-dimensional numerical models will be developed using advanced simulation platforms. A suite of recorded ground motions, representative of high-seismicity regions, will be selected and scaled to perform Incremental Dynamic Analysis (IDA). This process will allow for the tracking of structural response—specifically Maximum Inter-story Drift (MIDR) and Torsional Rotation—from the elastic range through to global collapse. Fragility curves will subsequently be derived for four distinct damage states: Slight, Moderate, Extensive, and Global Collapse, as defined by established performance-based design codes. It is hypothesized that asymmetric configurations will exhibit a significantly higher probability of exceeding the "Extensive" damage state compared to their symmetric counterparts, primarily due to the concentration of demand on "flexible-side" columns. Furthermore, the research will investigate the influence of bi-directional ground motion interaction, which is often underestimated in traditional planar assessments. The findings of this assessment will provide essential data for Probabilistic Seismic Hazard Analysis (PSHA) and offer structural engineers a robust framework for evaluating the risk-to-benefit ratio of implementing specific lateral force-resisting systems in irregular buildings. By pinpointing the specific intensity measures that trigger torsional instability, this work will contribute to the development of more resilient urban infrastructure. | |||||||||||||||||
4 | FOET | Jay D. Gohel | 7984382162 | jdg01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Spatio-Temporal b-Value Mapping and Stress Accumulation along the India-Burma Convergent Margin | Seismology | The India-Burma convergent margin is a seismically volatile tectonic environment defined by the highly oblique subduction of the Indian Plate beneath the Burma Microplate. Understanding the spatial and temporal distribution of tectonic stress is paramount for accurate seismic hazard assessment, particularly near major systems like the Dauki, Kabaw, and Sagaing faults. This research utilizes the Gutenberg-Richter b-value as a critical physical proxy for differential stress accumulation, where low values identify potential asperities and high-risk zones. To ensure data reliability, a unified earthquake catalogue will be compiled and homogenized to a consistent moment magnitude (Mw) scale. The Maximum Likelihood Estimation (MLE) method will be applied to calculate b-values within uniform spatial grids and depth sections, thereby revealing anomalies associated with "locked" fault segments. By pinpointing areas where low b-values indicate high stress buildup, this study prioritizes specific zones for future earthquake forecasting and infrastructure resilience in a region historically prone to great earthquakes exceeding Mw 8.0. | |||||||||||||||||
5 | FOET | BHAVESH CHAVDA | 9727741742 | visiting_bhavesh"ict.gnu.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI in music classification | AI/ML | Artificial Intelligence (AI) has become a powerful tool in music classification, enabling automatic identification and organization of music based on genre, mood, instruments, and other characteristics. By using machine learning and deep learning techniques, AI systems analyze audio features such as tempo, pitch, rhythm, and spectral patterns to categorize songs accurately. These models are trained on large music datasets to learn patterns and improve classification performance over time. AI-based music classification supports applications in music streaming platforms, recommendation systems, digital libraries, and music information retrieval. Overall, AI enhances the efficiency, scalability, and accuracy of managing and discovering large collections of digital music. | |||||||||||||||||
6 | FOET | Mayurbhai Mistry | 8347520940 | msm01@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI in Music Information Retrieval (MIR) | Music | Music Information Retrieval (MIR) focuses on extracting meaningful information from music data such as audio signals, metadata, and lyrics. With the rapid advancement of AI, MIR systems have significantly improved in tasks such as genre classification, mood detection, music recommendation, and automatic transcription. This two-month summer research project aims to explore the application of AI techniques—particularly machine learning and deep learning—in analyzing and organizing musical data. The study will investigate how neural networks and feature extraction methods can be used to identify patterns in audio signals and improve the accuracy of music classification and retrieval systems. The research will entail an examination of current MIR models, experimentation with publicly accessible music datasets, and the application of AI-driven methodologies to assess their efficacy in tasks such as genre classification and similarity identification. The expected outcome is a deeper understanding of how AI can enhance automated music analysis and retrieval efficiency. This project will contribute to ongoing research in intelligent music systems and demonstrate the potential of AI-driven approaches to improve user interaction with large-scale music databases and streaming platforms. | |||||||||||||||||
7 | FOET | Mayurbhai Mistry | 8347520940 | msm01@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI for Music Recommendation Systems | Music | With the rapid growth of digital music platforms, personalized music recommendation has become essential for improving user experience. AI plays a significant role in analyzing large volumes of music data and understanding user listening behavior. The goal is to look into AI-based methods used in music recommendation systems, with a focus on how machine learning algorithms can guess what songs a user will like and suggest songs that fit those tastes. The project will review common recommendation approaches such as collaborative filtering, content-based filtering, and hybrid models. It will also examine how deep learning methods can enhance recommendation accuracy by analyzing audio features, user interaction data, and listening patterns. During the research period, publicly accessible music datasets will be examined, and basic AI models will be employed to assess their efficacy in suggesting music based on user profiles and listening history. The expected outcome is to gain insights into the effectiveness of AI techniques in developing personalized music recommendation systems and to identify ways to improve recommendation accuracy and user satisfaction. This research will help us learn how intelligent systems enhance music discovery on modern streaming platforms. | |||||||||||||||||
8 | FOET | Kunal Garud | 8866244116 | kdg01@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI-Powered Real-Time Weather Monitoring and Personalized Alert System Using Satellite Data from MOSDAC | Artifical Intelligence | Weather-related disasters such as heavy rainfall, cyclones, thunderstorms, floods, and heatwaves cause significant damage to human life, agriculture, and infrastructure each year. Early detection and timely warning systems are essential to reduce the impact of such extreme weather events. However, traditional weather forecasting systems usually provide regional predictions that are updated periodically and often lack real-time insights or personalized alerts for specific locations. Moreover, many existing weather applications do not effectively integrate real-time satellite observations with artificial intelligence techniques to provide accurate early warnings. This research proposes an AI-powered real-time weather monitoring and personalized alert system using satellite data from the Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), operated by the Indian Space Research Organisation. The system continuously collects near real-time meteorological data such as cloud movement, rainfall intensity, wind speed, humidity, atmospheric temperature, and cyclone tracking. These datasets are analyzed using machine learning and deep learning models. Convolutional Neural Networks (CNN) analyze satellite imagery, while Long Short-Term Memory (LSTM) networks predict weather conditions for the next 24–48 hours. The system generates location-based alerts by combining prediction results with user geographic information, enabling timely warnings and improving disaster preparedness and public safety. | |||||||||||||||||
9 | FOET | Prof. Dhaval Sathawara | 7984424598 | 010558 | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI-Based Detection of Epileptic Seizures and Mental Health Patterns (Stress, Anxiety, Depression, Sleep Disorders) Using EEG Brain Signal Analysis | Artificial Intelligence | Epilepsy and mental health conditions such as stress, anxiety, depression, and sleep disorders significantly affect brain activity and overall human well‑being. Early detection of these conditions is essential for effective treatment and improved quality of life. This research proposes an artificial intelligence (AI)-based system for analyzing electroencephalogram (EEG) brain signals to detect epileptic seizures and identify patterns related to mental health conditions. The system utilizes machine learning techniques to classify EEG signals stored in a neural data repository. Signal preprocessing and feature extraction methods are applied to improve classification accuracy. The proposed approach aims to support medical professionals by providing an intelligent decision-support tool for early diagnosis and monitoring of neurological and mental health conditions. This research highlights the potential of AI-driven brain signal analysis in modern healthcare and neurological disorder detection | NA | ||||||||||||||||
10 | FOET | Patel Ravindra Laljibhai | 9925504525 | rlp01@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Enhancing Cybersecurity Using Blockchain Technology for Secure Data Management | Cybersecurity | Cybersecurity has become a major concern due to increasing cyber threats such as data breaches, unauthorized access, and data tampering. Traditional centralized security systems often suffer from single points of failure, making them vulnerable to attacks. Blockchain technology offers a decentralized, transparent, and tamper-resistant approach to securing digital information. This research focuses on analyzing how blockchain technology can improve cybersecurity by ensuring secure data storage, authentication, and integrity verification. The study examines blockchain architecture, cryptographic techniques, and smart contracts to understand their role in protecting sensitive information. A prototype or conceptual framework will be proposed to demonstrate how blockchain can prevent data manipulation and unauthorized access. The results of this research highlight the advantages of blockchain such as decentralization, immutability, and enhanced trust in cybersecurity applications. The study also discusses challenges like scalability and implementation complexity while proposing future improvements for secure digital systems. | NA | ||||||||||||||||
11 | FOET | Dr. Aniket Patel | 9429062411 | arp02@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Al Algorithms and Security Techniques in Modern Video Games: A Study of Black Myth: Wukong | Al Algorithms and Security Techniques in Modern Video Games: A Study of Black Myth: Wukong | The rapid advancement of artificial intelligence (AI) has significantly transformed the design, functionality, and security of modern video games. This research paper explores the integration of AI algorithms and security techniques in contemporary gaming environments, with a focused case study on Black Myth: Wukong. The study examines how AI-driven systems enhance non-player character (NPC) behavior, adaptive gameplay, environment interaction, and real-time decision-making to create immersive gaming experiences. In addition, the paper analyzes the cybersecurity mechanisms implemented to protect games from threats such as cheating, hacking, data manipulation, and unauthorized access. Techniques including anti-cheat systems, encryption, behavior analysis, and secure network protocols are discussed in relation to maintaining fair gameplay and protecting user data. By evaluating the technological framework behind Black Myth: Wukong, this research highlights the role of AI in improving both gameplay intelligence and game security. The findings emphasize that the combination of advanced AI algorithms and robust security strategies is essential for the development of reliable, engaging, and secure modern video games. | |||||||||||||||||
12 | FOET | Dr. Rohit B. Patel | 9909716318 | principle.ict@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AutoShield Your Vehicle Guardian | AI | AutoShield is an intelligent vehicle protection system designed to improve the safety and reliability of parked vehicles. In many situations, vehicles remain unattended for long periods and become vulnerable to threats such as unauthorized access, electrical damage, and unnecessary battery drain. Conventional protection systems mainly rely on basic alarms and manual monitoring, which often fail to provide continuous and energy-efficient protection. The proposed AutoShield system integrates sensors, a control unit, and automated response mechanisms to monitor vehicle conditions in real time. When abnormal activity or potential risks are detected, the system activates protective measures to prevent damage and ensure vehicle security. The design prioritizes low power consumption, reliable operation, and minimal human intervention. By combining intelligent monitoring with automated protection, AutoShield offers a practical and sustainable solution for modern vehicles and can be further extended for use in smart and electric vehicle systems. | |||||||||||||||||
13 | FOET | Digant Shah | 9727585866 | digant@ict.gnu.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Smart Traffic Management System Using IoT | AI, IoT | IoT-based smart traffic management systems are the ideal solution to address current needs as the population and traffic are growing. The technology can easily control all the traffic on the roadways and provide dedicated lanes for emergency vehicles like the fire department or ambulances. | |||||||||||||||||
14 | FOET | Digant Shah | 9727585866 | digant@ict.gnu.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Air Pollution Monitoring System Using IoT | Ai, IoT | Air pollution has worsened almost all major cities' air quality. As a result, many diseases have emerged, and human health is deteriorating. Internet of Things (IoT)-based air pollution monitoring systems can measure air quality. In this IoT mini project, the air quality will be continuously monitored in PPM while important logs are saved for later use. The system activates an alarm with MQ135 and MQ6 sensors if the air quality drops below a baseline. These sensors can identify dangerous gasses in the air and determine their precise concentration in real-time. | |||||||||||||||||
15 | FOET | Dr. Sheetal Pandya | 9924013902 | ssp08@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI-Driven Multispectral Analysis for Early-Stage Crop Disease Detection using Drone System | IoT, ML, Drone | This project focuses on the development of an autonomous drone system designed to identify crop stress before it becomes visible to the human eye. By utilizing multispectral sensors, the drone captures data across various light bands, including near-infrared, to calculate the Normalized Difference Vegetation Index (NDVI). The core of the project is a machine learning algorithm that processes these images to differentiate between nutrient deficiency, pest infestation, and fungal infections. By providing farmers with precise "heat maps" of field health, the system enables localized intervention rather than blanket pesticide application. This targeted approach significantly reduces chemical costs, minimizes environmental impact, and improves overall crop yield through early-stage diagnostics and data-driven decision-making. | NO | ||||||||||||||||
16 | FOET | Dr. Sheetal Pandya | 9924013902 | ssp08@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Autonomous Variable-Rate Spraying and Seed Sowing Drone System | IOT, Drone | Traditional broadcasting methods for seeds and fertilizers often lead to significant waste and uneven growth. This project proposes an intelligent UAV (Unmanned Aerial Vehicle) equipped with a variable-rate application (VRA) system. Integrating GPS and ultrasonic sensors for altitude hold, the drone adjusts its discharge rate based on real-time field requirements or pre-programmed soil maps. The system is designed to navigate complex terrains where heavy machinery cannot reach, ensuring uniform distribution of resources. Key innovations include a custom-designed centrifugal spreader mechanism and a flight controller optimized for heavy-payload stability. The result is a highly efficient, labor-saving solution that optimizes resource utilization, reduces the physical strain on farmers, and ensures sustainable agricultural practices in diverse topographical conditions. | no | ||||||||||||||||
17 | FOET | Dr. Sheetal Pandya | 9924013902 | ssp08@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Integrated Thermal Imaging for Livestock Tracking and Irrigation Leak Detection | ML,IOT, Drone | Managing large-scale livestock and sprawling irrigation networks presents significant logistical challenges. This project introduces a dual-purpose drone system utilizing thermal infrared (TIR) sensors to automate ranch management. The drone performs two primary functions: automated counting and health monitoring of livestock through heat signature tracking, and the identification of underground irrigation leaks evidenced by localized soil cooling. By implementing an autonomous flight path algorithm, the drone can cover large areas with minimal human intervention. The data is relayed to a mobile dashboard, providing real-time alerts for stray animals or water wastage. This integration of thermal technology enhances operational efficiency, improves animal welfare, and promotes water conservation in arid and semi-arid farming environments. | NO | ||||||||||||||||
18 | FOET | Ms. Sanika Soni | 9328537652 | visiting_shanika@ict.gnu.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Explainable AI for Time Series Anomaly Detection | AI, XAI | This project aims to develop an Explainable Artificial Intelligence (XAI) based system for detecting anomalies in time-series data. Machine learning and deep learning models such as Isolation Forest and LSTM will be used to identify abnormal patterns in datasets like server monitoring logs, stock market data, or energy consumption records. To improve transparency, explainability techniques such as SHAP or LIME will be applied to interpret the model predictions and highlight the key factors responsible for anomalies. The proposed system will help users not only detect unusual behavior in time-series data but also understand the reasons behind it, making the model more reliable for applications such as financial fraud detection, industrial monitoring, and smart grid systems. | no | ||||||||||||||||
19 | FOET | Ms. Sanika Soni | 9328537652 | visiting_shanika@ict.gnu.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Commodity Price Prediction in Indian Agriculture using Data Science | Data Science | This project aims to predict agricultural commodity prices in India using data science and machine learning techniques. Historical price data from sources such as the Agmarknet database, along with weather and seasonal datasets, will be analyzed to identify market patterns. Time series forecasting models such as ARIMA, Prophet, and machine learning models like LSTM or XGBoost will be implemented to predict future commodity prices. The system will provide insights into price trends that can help farmers, traders, and policymakers make informed decisions. The proposed solution supports the development of data-driven decision systems for smart agriculture and agricultural market forecasting. | no | ||||||||||||||||
20 | FOET | Prof. Umesh Lakhtariya | 9426636393 | ul01@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI-Based Multi-Layer Vehicle Accident Detection and Emergency Response System Using Voice Interaction and Smartwatch Health Monitoring. | Artificial intelligence | Road traffic accidents are one of the leading causes of injury and death worldwide, and delays in emergency response often increase the severity of casualties. To address this problem, this paper proposes an Artificial Intelligence (AI)-based vehicle accident detection and emergency notification system that improves the reliability and speed of accident response. The proposed system automatically detects accidents using vehicle sensor data and then performs a multi-layer verification process to minimize false alarms. After detecting a potential accident, the system generates a voice-based query to confirm the safety status of the driver or passengers. If a positive response is received, the system assumes that the occupants are safe and no emergency notification is sent. However, if the response is negative or no response is received within a predefined time, the system proceeds to the next verification stage by analyzing the driver’s pulse rate obtained from a connected smartwatch or wearable device. This health-aware mechanism helps determine whether the driver is unconscious or in a critical condition. Based on the analysis, the system automatically sends an emergency alert along with the vehicle’s GPS location to the nearest police station and hospital to enable quick ambulance dispatch. Additionally, a manual emergency button is provided to allow the driver or passengers to request assistance in case the automated detection fails. The proposed framework integrates AI, IoT devices, wearable health monitoring, and voice interaction to create a more intelligent and reliable accident response system. This approach aims to reduce emergency response time, improve survival rates, and enhance road safety through intelligent real-time monitoring and automated assistance. | |||||||||||||||||
21 | FOET | Prof. Tejas R. Kadiya | 9427680977 | trk01@ganpatuniversity.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | AI Based Fake News & Misinformation Detection System | AI/ML | The rapid growth of social media platforms has led to a significant increase in the spread of fake news and misinformation. Unverified information shared through social networks and messaging applications often causes public panic, social conflicts, and incorrect decision-making. Therefore, detecting and controlling the spread of misleading information has become an important challenge in today’s digital society. This research proposes an AI-based Fake News and Misinformation Detection System that can automatically analyze news content, messages, or web links to determine their authenticity. The system will use Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning techniques to examine textual patterns and evaluate the credibility of information sources. Based on the analysis, the system will classify the input as real or fake and provide additional indicators such as trusted source references and a risk level. A web-based application will be developed to allow users to easily submit news text or links for verification. The system will process the data using trained machine learning models to provide quick and reliable results. The main objective of this research is to develop a simple and automated tool that helps users verify information before sharing it further. | |||||||||||||||||
22 | FOET | Prof. Tejas R. Kadiya | 9427680977 | trk01@ganpatuniversitya.ac.in | Institute of Computer Technologies (ICT) | Computer Science and Engineering | Federated Learning-Based Framework for Autonomous Satellite Image Processing in Distributed Earth Observation Systems | AI/ML and Image Processing | Satellite imagery plays a vital role in monitoring environmental changes, disaster management, urban planning, and agricultural development. However, processing the large volume of satellite data generated by multiple Earth observation systems presents significant challenges, particularly in terms of data sharing, privacy, and computational efficiency. Traditional centralized machine learning approaches require transferring large datasets to a central server, which can lead to high communication costs, latency issues, and potential data privacy concerns. This research proposes a Federated Learning-based framework for autonomous satellite image processing within distributed Earth observation systems. The framework enables multiple satellite data sources or ground stations to collaboratively train machine learning models without sharing their raw data. Instead, only model updates are exchanged and aggregated to improve the global model. This approach reduces the need for extensive data transfer while maintaining data privacy and improving overall system efficiency. The proposed system leverages machine learning, distributed computing, and cloud-based infrastructure to automatically analyze satellite images for tasks such as land-use classification, environmental monitoring, and anomaly detection. By enabling decentralized learning, the framework supports scalable and efficient processing of large-scale satellite imagery. The research aims to develop a secure and efficient model training environment that enhances real-time Earth observation capabilities. In the future, the system can be expanded to support more advanced AI models and real-time satellite data streams for improved decision-making and environmental monitoring. | |||||||||||||||||
23 | FOET | Dr. V M Patel | 9974094051 | dean.foet2@ganpatUniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | Comparative Analysis of Indian State-Level EV Policies and Their Impact on Adoption | Electric Vehicle Technology & Policy | This study evaluates the effectiveness of diverse state-level Electric Vehicle (EV) policies in India, focusing on their role in achieving the national 30% penetration target by 2030. With over 25 states and Union Territories now implementing unique frameworks, the research compares fiscal demand-side incentives—such as road tax waivers and purchase subsidies—against supply-side supports like land concessions and manufacturing clusters. By analyzing adoption rates in leading states like Uttar Pradesh, Maharashtra, and Karnataka, the study unearths how specific institutional mechanisms and infrastructure mandates drive market growth. The findings will highlight "best practice" models to inform standardized, high-impact regional policy development. | |||||||||||||||||
24 | FOET | Dr. V M Patel | 9974094051 | dean.foet2@guni.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | Evaluating the Viability and Challenges of "Battery-as-a-Service" (BaaS) and Swapping Models | Electric Vehicle Technologies & Policies | This study investigates the technical and economic feasibility of Battery-as-a-Service (BaaS) and swapping models within the Indian EV ecosystem. By decoupling battery ownership, these models can reduce initial vehicle costs by up to 40%, transforming high capital expenditure into manageable operating costs. While swapping offers rapid "refueling" for commercial fleets, significant challenges remain, including a critical lack of battery standardization, interoperability hurdles, and a high GST on separate batteries. This research evaluates these barriers alongside emerging policy frameworks and the potential for "Battery Passports" to improve lifecycle transparency and market trust. | |||||||||||||||||
25 | FOET | Dr. V M Patel | 9974094051 | dean.foet2@guni.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | Critical Mineral Supply Chain Vulnerabilities and Their Impact on India's EV Manufacturing and Adoption Goals | Electric Vehicle Technologies & Policies | India’s ambitious 2030 EV targets face substantial risks due to a near 100% import dependency on critical minerals like lithium, cobalt, and nickel,,. This study analyzes supply chain vulnerabilities resulting from concentrated sourcing, highlighting India's heavy reliance on China for battery-grade graphite and lithium-ion cells,,. Such dependencies expose domestic manufacturing to severe price volatility and geopolitical disruptions,. To safeguard adoption goals, the research evaluates the effectiveness of the PLI-ACC scheme, strategic overseas mineral acquisitions via KABIL, and the role of "urban mining" (battery recycling) in securing secondary material supplies. | |||||||||||||||||
26 | FOET | Dr.V.M.Patel | 9974094051 | dean.foet2@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Assessment of Advanced Formwork Techniques for Improving Construction Productivity | Construction Technology | Advanced formwork systems play a vital role in improving the efficiency and productivity of modern construction projects. This study focuses on the assessment of advanced formwork techniques such as slipform, climbing formwork, tunnel formwork, and aluminium formwork used in reinforced concrete construction. The objective is to evaluate their effectiveness in terms of construction speed, cost efficiency, labour requirement, and quality of finished structures compared with conventional formwork methods. The study also examines the suitability of these systems for high-rise buildings and large-scale infrastructure projects. The findings highlight that advanced formwork techniques significantly enhance construction productivity, reduce project duration, and improve structural accuracy and safety in contemporary construction practices. | |||||||||||||||||
27 | FOET | Dr. Nikunj Patel | 9825788250 | nrp05@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | Modeling and Simulation of EV Motors Using ANSYS Tools | Advancement of Electrical Motor for Eclectic Vehicle | This training program focuses on the modelling and simulation of electric vehicle (EV) motors using ANSYS tools. It covers key motor technologies such as Permanent Magnet Synchronous Motors (PMSM), Induction Motors, and Switched Reluctance Motors. Participants will learn fundamental design principles, material selection, and performance optimization techniques. The training emphasizes electromagnetic, thermal, and mechanical analysis through simulation to evaluate motor efficiency, power density, and reliability. Hands-on sessions using ANSYS will enable accurate modelling and real-time performance assessment under various operating conditions. This program aims to develop practical skills required for designing efficient and sustainable motor systems for modern electric mobility applications. | NA | ||||||||||||||||
28 | FOET | Prof. Apurvkumar Prajapati | 8000030770 | arp12@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Enhancing Pavement Sub-Base Layers with Solid Waste: A Sustainable Approach | Transportation Geotechnics | Improving pavement sub-base layers is essential for enhancing roadway durability, load-bearing capacity, and overall service life. Conventional materials such as crushed stone and gravel are widely used; however, their extraction and processing involve high costs and significant environmental impacts, including resource depletion, habitat degradation, and increased carbon emissions. In response to these challenges, this study investigates the use of solid waste as a sustainable alternative for enhancing sub-base layer performance. Laboratory evaluations, including compaction, California Bearing Ratio (CBR), and resilient modulus (MR) tests, were conducted to assess the engineering properties of modified sub-base materials. The results demonstrate that the incorporation of solid waste can improve key performance characteristics such as strength, stiffness, and compaction behavior. The findings suggest that solid waste has strong potential as a cost-effective and environmentally friendly substitute for conventional materials in pavement construction. This approach supports sustainable infrastructure development while promoting efficient waste utilization and reducing environmental impact within the civil engineering sector. | |||||||||||||||||
29 | FOET | Sumit Sharma | 8866505344 | sms04@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Seismic Microzonation of an Urban Region in India for Localized Earthquake Hazard Assessment | Earthquake Engineering | Rapid urbanization in many Indian cities has increased the vulnerability of infrastructure and populations to earthquake hazards. While national seismic zoning maps provide a broad classification of seismic risk, they are insufficient for evaluating localized site-specific effects that significantly influence earthquake damage patterns. Seismic microzonation provides a detailed assessment of spatial variations in ground motion characteristics by incorporating geological, geotechnical, and seismological parameters at the city scale. The present study proposes a framework for conducting seismic microzonation of an urban region in India to evaluate localized seismic hazard and identify areas susceptible to higher earthquake risk. The methodology integrates multi-disciplinary datasets including geological formations, soil properties, shear wave velocity (Vs30), ground response characteristics, liquefaction potential, and peak ground acceleration (PGA). Field investigations such as microtremor measurements, borehole data interpretation, and geophysical surveys are combined with Geographic Information System (GIS) techniques to develop thematic hazard maps. These maps classify the study region into different seismic hazard zones based on site amplification effects and soil behavior during seismic events. The expected outcome of the study is the generation of a comprehensive seismic microzonation map that highlights variations in earthquake hazard across the selected urban region. Such information can support urban planners, engineers, and policymakers in implementing earthquake-resistant design practices, improving land- use planning, and enhancing disaster preparedness strategies in rapidly developing Indian cities. | NA | ||||||||||||||||
30 | FOET | Dr. V M Patel | 9974094051 | dean.foet2@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Civil Engineering | Rapid Visual Survey and Maintenance Condition Assessment of Ganpat University Buildings | Visual Rapid Survey | This internship focuses on conducting a Rapid Visual Survey of approximately 50 buildings across the Ganpat University campus, emphasizing non-structural and service-related issues rather than major structural distress. Students will document defects such as water leakage, dampness, open or exposed wiring, damaged finishes, and minor deterioration of building components. Using a structured data collection format and photographic evidence, the survey will generate a prioritized maintenance and repair schedule for the campus. The internship aims to enhance students’ practical skills in building condition assessment, facilities management, and preventive maintenance planning within a real institutional environment. | Dr Satyajeet Nanda, Professor, Jain University | ||||||||||||||||
31 | FOET | Dr. Anvita Sharma | 8511103370 | aas03@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Chemical Engineering | Analyze mixing time and uniformity with different impeller designs using CFD | Computational fluid dynamics | This study investigates the influence of impeller design on mixing time and uniformity in a stirred tank reactor using computational fluid dynamics (CFD) simulations in ANSYS Fluent. Two commonly used impellers, the Rushton turbine and pitched blade impeller, are analyzed under varying rotational speeds and flow regimes. The simulations solve the Navier–Stokes equations to capture detailed flow patterns, turbulence characteristics, and species distribution within the reactor. Mixing performance is evaluated through velocity vectors, concentration contours, and mixing time calculations based on tracer dispersion. Results indicate that impeller geometry significantly affects flow circulation, shear rate distribution, and homogeneity. The pitched blade impeller demonstrates superior axial flow and faster mixing, while the Rushton turbine provides higher shear but slower homogenization. This work highlights the importance of impeller selection in optimizing reactor performance and provides insights for scale-up and design of efficient mixing systems in chemical engineering applications. | |||||||||||||||||
32 | FOET | Dr. Anvita Sharma | 8511103370 | aas03@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Chemical Engineering | Simulation of Biodiesel Production Process using DWSim | Simulation and Optimization | This study presents the simulation of a biodiesel production process using DWSIM to evaluate process performance and optimize operating conditions. The production is based on the transesterification of vegetable oil with methanol in the presence of a catalyst, yielding biodiesel and glycerol as the main products. A steady-state flowsheet was developed incorporating key unit operations such as a reactor, separator, and distillation column for methanol recovery. Appropriate thermodynamic models were selected to accurately represent phase equilibria and reaction behavior. The simulation investigates the effects of parameters such as temperature, methanol-to-oil ratio, and conversion efficiency on product yield and purity. Results demonstrate that process optimization can significantly enhance biodiesel yield while reducing energy consumption. The study highlights the applicability of process simulation tools in designing sustainable biofuel production systems and provides insights for scaling up and industrial implementation of biodiesel manufacturing processes. | |||||||||||||||||
33 | FOET | Yug Chandra Saraswat | 7043338707 | ycs01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Chemical Engineering | Investigating resuspendability in nanosilica gels as model pharmaceutical adjuvants | Basic sciences/nanotechnology | Aluminum phosphate (Adju-Phos) is a widely used vaccine adjuvant whose sedimentation behavior, microstructure, and resuspendability critically influence formulation stability and performance. However, the underlying mechanisms linking inter-particle interactions, protein adsorption, and sediment structure remain fragmented across the literature. In this work, we present a systematic analysis of existing experimental studies on aluminum-based adjuvants to develop a unified framework connecting microscopic interactions to macroscopic behavior. Drawing on reported measurements of particle size, zeta potential, protein adsorption, and rheological response, we identify key physicochemical parameters governing aggregation, sediment packing, and redispersion. Particular emphasis is placed on the role of protein-mediated interactions and ionic strength in modulating network formation and mechanical properties. To generalize these insights, we further examine analogous colloidal systems, including silica suspensions, where interaction potentials can be independently tuned, enabling clearer interpretation of aggregation and gelation mechanisms. By synthesizing insights across these systems, we propose a mechanistic map that relates interaction regimes to sediment structure and resuspendability. This work provides a conceptual framework for interpreting formulation behavior and offers design guidelines for improving the stability and performance of adjuvanted vaccine systems, while also identifying key gaps for future experimental investigation. | |||||||||||||||||
34 | FOET | DR. Deoashish Panjiara | 7070485221 | dp01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Petrochemical Engineering | DEVELOPMENT OF PAPER BASED MICROFLUIDIC FUEL CELL | FUEL CELL TECHNOLOGY | The paper-based microfluidic fuel cells (pMFCs) have emerged as a promising low-cost and portable energy technology for next-generation micro-power applications. The recent research highlights that these devices utilize the natural capillary action of paper to transport fuel and oxidant without external pumps, making them simple, lightweight, and environmentally friendly. Advances in design, such as improved electrode materials and optimized channel geometry have significantly enhanced power density and operational stability. Furthermore, flexible and paper-based architectures enable applications in wearable devices, sensors, and educational kits. This project aims to develop a simple and reproducible pMFC using readily available materials, allowing students to fabricate, test, and analyze performance parameters such as voltage, current, and fuel utilization. The experimental setup is safe, low-cost, and easy to assemble, making it highly suitable for summer internships. Students will gain hands-on experience in microfluidics, electrochemistry, and sustainable energy systems. Keywords: pMFC, capillary flow, electrochemistry, portable power, microfluidics | NA | ||||||||||||||||
35 | FOET | Nikunj Patel | 9586795841 | nnp01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | Experimental Investigation of Partial Shading Effects on PV Micro-Inverter Systems for Residential Applications | Renewable Energy | Partial shading in photovoltaic (PV) systems causes significant power losses due to current mismatches and multiple maximum power points (MPPs) in series-connected strings, often reducing output by 70-80% in residential setups. This experimental study investigates these effects using a low-cost hardware prototype suitable for B.Tech students, comparing traditional string inverters against micro-inverters under controlled shading conditions (200-1000 W/m² irradiance). The setup employs two 20W polycrystalline PV panels, Hoymiles HMS-200 micro-inverter, MPPT buck converter, Arduino-based data logging, and variable shading with mesh cloth under halogen illumination. Results show micro-inverters recover 75-90% more power from unshaded modules via per-panel MPPT, eliminating global MPP tracking errors seen in strings. I-V and P-V characteristics confirm superior performance for shaded rooftops, validated against PSIM simulations. Findings provide practical insights for renewable energy education and Gujarat's urban PV deployments. | |||||||||||||||||
36 | FOET | HIMANSHU AMARAM PATEL | 8238059731 | himanshu.patel@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Biomedical Engineering | Development of Smart Emotion Recognition Models using EEG, AI, and Brain Signal Processing | Medical Signal Processing & AI | The proposed title provides an opportunity to work on smart emotion recognition systems based on combining EEG brain signals processing concept with AI models. It includes signal processing methods such as how to clean EEG signals, extract meaningful features using spectral and connectivity analysis spectral feature extraction, and implement different AI models for emotion recognition tasks. For proposed project work we will use standard datasets like DEAP and GAMMA to build and test emotion recognition frameworks. Participants will gain practical exposure to real-world biomedical data challenges and will be guided to develop new applications like mental health monitoring and human–computer interaction. The internship will provide hands-on experience in Python, data analysis, and model evaluation, and students can propose innovative approaches for improving system accuracy. Also we encourage for creative thinking, allowing students to propose and implement new AI-based solutions for improving emotion detection. | |||||||||||||||||
37 | FOET | Dr.Vishnu Patel | 9825298492 | vishanu.patel@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | IoT BASED INTELLIGENT FAULT DISTANCE LOCATOR FOR UNDERGROUND POWER CABLES USING MICROCONTROLLER | Power system Protection | The rapid expansion of underground power distribution networks requires accurate and intelligent fault detection systems to maintain reliable and uninterrupted electricity supply. Conventional fault location methods are often slow, labor-intensive, and economically inefficient. This summer internship project presents the design and development of an IoT-enabled intelligent fault distance locator using a microcontroller integrated with advanced sensing and diagnostic techniques for effective underground cable monitoring. The proposed system utilizes a hybrid fault detection approach combining voltage drop measurement and basic Time Domain Reflectometry concepts to improve fault localization accuracy. Precision current and voltage sensors, along with signal conditioning circuits, continuously monitor cable parameters and transmit real-time data to the embedded controller. Based on impedance variation, the controller estimates the distance of the fault from the supply end and displays the information on a digital interface. An IoT communication module further enables remote transmission of fault data to a cloud platform or mobile device for faster maintenance response and operational planning.The prototype demonstrates improved fault detection speed, reduced outage duration, enhanced monitoring capability, and suitability for smart grid applications, highlighting the practical implementation of embedded systems and IoT in modern underground cable fault management. | Mr. A.G. Balar , Deputy Engineer, GETCO | ||||||||||||||||
38 | FOET | Vishnu Patel | 9825298492 | vishanu.patel@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Electrical Engineering | Dynamic Torque–Speed Optimization and Performance Mapping of Electric Vehicles under Real-Time Load and Road Gradient Variations | Electric vehicle | This project presents a comprehensive design and performance analysis of an electric vehicle (EV) focusing on the variation of speed and torque characteristics under diverse load and road conditions. The study aims to develop a dynamic vehicle model incorporating real-time road load components such as rolling resistance, aerodynamic drag, gradient force, and acceleration demand. An electric traction motor drive system is analysed to investigate its torque–speed behaviour in constant torque and constant power operating regions. The proposed work evaluates EV performance during critical operating scenarios including vehicle starting, hill climbing, cruising on flat terrain, regenerative braking during downhill motion, and operation under varying passenger or payload conditions. Mathematical modelling of tractive effort and vehicle dynamics is carried out to determine motor rating, gear ratio selection, and power requirement for efficient propulsion. Simulation-based analysis is performed to generate performance curves illustrating speed variation, torque demand, and energy consumption under different driving cycles. The study also emphasizes energy-efficient torque management strategies to enhance vehicle range and operational reliability. The outcomes of this project provide valuable design insights for optimized EV powertrain development, improved traction control, and sustainable electric mobility solutions. | Mr. Noel Mcwan | ||||||||||||||||
39 | FODE (Diploma Engineering) | Prof.Vidhi Chaudhari | 7600561528 | vbc01@ganpatuniversity.ac.in | Institute of Technology (IOT) | Computer Engineering | Intelligent System for Detection and Classification of Oral Diseases | AI, ML,DL | Early detection of oral lesions plays a critical role in preventing the progression of oral cancer and improving patient survival rates. Traditional clinical examination and biopsy procedures are often time-consuming and dependent on specialist expertise. This study proposes an artificial intelligence-based automated system for the detection and classification of oral lesions from clinical images. The proposed framework integrates image preprocessing techniques, including resizing, normalization, and data augmentation, to enhance image quality and increase dataset diversity. Deep learning models based on convolutional neural networks are utilized to extract discriminative features and classify oral lesions into premalignant, malignant, and normal categories. To improve classification performance, ensemble learning techniques are employed by combining predictions from multiple trained models. The experimental results demonstrate that the proposed approach achieves high accuracy and robustness in identifying different oral lesion types. The system can support clinicians in early diagnosis, reduce diagnostic errors, and facilitate efficient screening in healthcare environments. | |||||||||||||||||
40 | FODE (Diploma Engineering) | Prof. Kiran Kamlesh Panchal | 9724315631 | kkp05@ganpatuniversity.ac.in | Institute of Technology (IOT) | Information Technology | AI-Based Exam Proctoring System | AI & ML | The AI-Based Exam Proctoring System is designed to ensure the integrity, security, and fairness of online examinations through automated monitoring and analysis. The system leverages advanced technologies such as computer vision, facial recognition, and machine learning to detect suspicious activities including identity impersonation, multiple face detection, eye movement tracking, and unauthorized object usage. Real-time alerts and post-exam analytics help educators identify potential malpractice efficiently. The system reduces the need for human invigilators, making it cost-effective and scalable for large-scale assessments. It also maintains detailed logs and reports for audit purposes. By combining accuracy with automation, the proposed solution enhances trust in remote examinations and supports the growing demand for digital education platforms while ensuring a secure and transparent evaluation environment. | |||||||||||||||||
41 | FODE (Diploma Engineering) | Prof. Kiran kamlesh panchal | 9724315631 | kkp05@ganpatuniversity.ac.in | Institute of Technology (IOT) | Information Technology | AI Personalized Learning System | AI & ML | The AI Personalized Learning System is designed to enhance student learning outcomes by delivering customized educational experiences based on individual needs, preferences, and performance. It utilizes machine learning algorithms to analyze student behavior, learning pace, and knowledge gaps, enabling real-time content adaptation and intelligent recommendations. Adaptive education platform that changes difficulty based on student performance is a key feature of the system, ensuring that learners remain engaged and challenged at an appropriate level. The platform supports continuous assessment, feedback, and progress tracking, helping educators make data-driven decisions. By promoting self-paced and targeted learning, the system improves knowledge retention and academic performance. This solution is scalable, efficient, and aligned with the evolving demands of digital education, making learning more interactive, personalized, and effective for diverse student populations. | |||||||||||||||||
42 | FODE (Diploma Engineering) | Peof. Kiran kamlesh panchal | 9724315631 | 170178 | B. S. Patel Polytechnic (BSPP) | Computer Engineering | Offline AI Wearable for Seizure Detection & Health Monitoring | AI , ML and IOT | This project proposes a low-cost, wearable device that uses Offline Artificial Intelligence (TinyML) to detect and monitor epileptic seizures in real time without requiring internet connectivity. The system integrates physiological sensors such as heart rate, motion (accelerometer), and skin temperature to continuously track the user’s condition. A lightweight machine learning model is deployed on an embedded device (e.g., ESP32) to analyze patterns and identify abnormal activities associated with seizures. Upon detection, the device triggers instant alerts to caregivers via Bluetooth or SMS, along with location information. The offline capability ensures fast response, improved privacy, and usability in low-network areas. Additionally, the system can log health data for long-term analysis. This solution aims to provide an affordable, reliable, and accessible healthcare tool, especially beneficial for rural and resource-limited environments. | |||||||||||||||||
43 | FODE (Diploma Engineering) | Yash Shah | 8866838622 | yjs01@ganpatuniversity.ac.in | Institute of Technology (IOT) | Civil Engineering | Analysis and Design of a Steel Roof Truss using STAAD Pro | Structure Design | Steel roof trusses are commonly used in industrial and commercial buildings because of their strength, light weight, and ability to cover large spans economically. This project involves the analysis and design of a steel roof truss using STAAD.Pro software. The truss structure is modeled by defining geometry, material properties, and support conditions. Dead load, live load, and wind load are applied as per relevant Indian Standard provisions. Structural analysis is carried out to determine axial forces, stresses, and displacements in truss members. Based on the analysis results, steel design is performed according to IS 800:2007 to check the safety and adequacy of the members. The project helps in understanding basic structural modeling, load application, analysis procedures, and design verification using modern structural engineering software. | |||||||||||||||||
44 | FODE (Diploma Engineering) | Prof. Kiran Kamlesh Panchal | 9724315631 | kkp05@ganpatuniversity.ac.in | Institute of Technology (IOT) | Information Technology | The Gatepass System with Hostel Attendance System | AI&ML | The Gatepass System with Hostel Attendance System is a digital solution designed to manage student movement and attendance in hostels efficiently. Traditionally, hostels use manual registers to record entry, exit, and attendance, which can be time-consuming and prone to errors. This system automates the entire process by allowing students to request gate passes online and enabling authorities such as wardens and guards to approve and monitor them in real time. It also records student attendance automatically when they enter or leave the hostel. The system improves security by tracking student movement, reduces paperwork, and ensures accurate record-keeping. It also provides quick access to data for hostel management, helping them make better decisions and respond to situations effectively. Overall, this system makes hostel management more organized, transparent, and efficient. | |||||||||||||||||
45 | FODE (Diploma Engineering) | Prof. Kiran Kamlesh Panchal | 9724315631 | kkp05@ganpatuniversity.ac.in | Institute of Technology (IOT) | Information Technology | Sentiment Analysis of Social Media Data for Public Opinion Tracking | AI&ML | "In today’s digital era, social media platforms such as Twitter, Facebook, and Instagram have become powerful mediums for expressing opinions on various social, political, and commercial topics. Analyzing these vast volumes of user-generated content can provide valuable insights into public sentiment and opinion trends. This project, “Sentiment Analysis of Social Media Data for Public Opinion Tracking,” aims to develop a system that automatically collects and analyzes social media data to determine the overall sentiment — positive, negative, or neutral — using Natural Language Processing (NLP) and Machine Learning techniques. The proposed system preprocesses the data by removing noise, hashtags, and special symbols, followed by text vectorization using TF-IDF or Word2Vec representations. Classification algorithms such as Naïve Bayes, Logistic Regression, or Support Vector Machine (SVM) are then applied to predict sentiment polarity. A visualization dashboard presents real-time public sentiment trends on specific topics, helping policymakers, businesses, and researchers gauge public reactions effectively. By leveraging social media analytics, this project contributes to understanding collective human emotions, tracking opinion shifts, and supporting data-driven decision-making. The system demonstrates how AI-powered sentiment analysis can transform unstructured text data into actionable insights for public opinion monitoring and trend forecasting Artificial Intelligence & Machine Learning" | |||||||||||||||||
46 | FOET | Prof. Harshil Modi | 6354031638 | hcm01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Design and Development of Lattice Structures for Ballistic Impact Absorption Using Additive Manufacturing | Additive Manufacturing | Ballistic protection systems used in defence applications require materials and structures capable of absorbing high-energy impacts while maintaining lightweight characteristics. Traditional Armor systems rely on dense materials, which increase the overall weight and reduce mobility. Additive Manufacturing offers the ability to create complex lattice structures that can enhance energy absorption and improve ballistic resistance. This project aims to design and develop lattice structures optimized for ballistic impact absorption using AM technologies. Different lattice configurations will be designed and analysed using computational tools to evaluate their energy absorption capabilities under high velocity impact conditions. Finite element simulations will be used to study stress distribution, deformation behaviour, and impact resistance.Selected lattice designs will be fabricated using AM processes and tested experimentally for impact resistance and energy absorption performance. The results will help identify optimized lattice architectures suitable for lightweight ballistic protection systems. | Mrs. Sriya jamubkar, Industry Mentor, sriya.jambukar@gmail.com | ||||||||||||||||
47 | FOET | Prof. Harshil Modi | 6354031638 | hcm01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Design and Development of Metamaterial Structures Using Additive Manufacturing for Superior Mechanical Performance | Additive Manufacturing | Metamaterials are engineered materials with unique mechanical properties that are not commonly found in natural materials, such as negative Poisson’s ratio, enhanced energy absorption, Hyperelastic behaviour in structure and tenable stiffness. The complex internal architectures required for metamaterials are difficult to fabricate using conventional manufacturing processes. Additive Manufacturing provides the capability to produce such intricate geometries with high precision. This project aims to design and develop mechanical metamaterial structures using AM technology. Different metamaterial architectures such as auxetic and hierarchical lattice structures will be designed and analysed through numerical simulations to evaluate their mechanical performance. The focus will be on improving properties such as energy absorption, stiffness, and structural stability. The optimized metamaterial structures will be fabricated using AM processes and subjected to mechanical testing to validate their performance. The findings of this research could contribute to the development of advanced materials for applications in aerospace, defence, protective equipment, and lightweight structural systems. | Mrs. Sriya jamubkar, Industry Mentor, sriya.jambukar@gmail.com | ||||||||||||||||
48 | FOET | Prof. Harshil Modi | 6354031638 | hcm01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Design and Development of Lightweight Aerospace Lattice Structures Using Additive Manufacturing | Additive Manufacturing | Weight reduction is a critical requirement in the aerospace industry to improve fuel efficiency, payload capacity, and overall system performance. Conventional manufacturing methods face limitations in producing complex lightweight geometries such as lattice structures. Additive Manufacturing (AM) enables the fabrication of intricate lattice architectures that offer high strength-to-weight ratios while maintaining structural integrity. This project focuses on the design, simulation, and fabrication of lightweight aerospace lattice structures using AM technologies such as Fused Deposition Modelling (FDM). Various lattice geometries, including octet, gyroid, and diamond structures, will be designed and analysed using finite element methods to evaluate their mechanical performance under aerospace loading conditions. The optimized designs will be fabricated using AM and experimentally tested for compressive strength, stiffness, and weight reduction. The outcome of this project will contribute to the development of efficient lightweight structural components for aerospace applications, demonstrating the advantages of AM in producing high-performance lattice structures that are difficult to manufacture using conventional methods. | Mrs. Sriya jamubkar, Industry Mentor, sriya.jambukar@gmail.com | ||||||||||||||||
49 | FOET | Dr. Achyut Trivedi | 9265018153 | avt01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Application of ARIMA Modelling in 3D Printing | Additive Manufacturing | Additive Manufacturing (AM), commonly known as 3D printing, has emerged as a transformative technology across industries such as aerospace, automotive, healthcare, and defense. However, variability in process parameters, material behaviour, and environmental conditions often leads to inconsistencies in print quality, production time, and resource utilization. To address these challenges, this study explores the application of the Autoregressive Integrated Moving Average (ARIMA) modeling technique for time-series analysis and forecasting in 3D printing processes. The proposed work focuses on collecting real-time and historical data from AM systems, including parameters such as temperature, layer deposition rate, print speed, and defect occurrence. ARIMA models are developed to analyse trends, seasonality, and stochastic variations in the data, enabling accurate prediction of key performance indicators such as build time, dimensional accuracy, and potential failure points. The model’s performance is evaluated using statistical metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). By integrating ARIMA-based predictive analytics into the AM workflow, the study aims to enhance process optimization, reduce material waste, and improve overall production reliability. The findings demonstrate that time-series forecasting can serve as a powerful decision-support tool for smart manufacturing and Industry 4.0 environments. This research contributes to the advancement of data-driven additive manufacturing by bridging the gap between statistical modeling and real-time process control. | |||||||||||||||||
50 | FOET | Dr. Achyut Trivedi | 9265018153 | avt01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Development of Digital Twin for Additive Manufacturing | Additive Manufacturing | Additive Manufacturing (AM), widely recognized as 3D printing, is revolutionizing modern manufacturing by enabling complex geometries, mass customization, and reduced material waste. However, challenges such as process variability, lack of real-time monitoring, and limited predictive capabilities hinder its widespread industrial adoption. To overcome these limitations, this project focuses on the development of a Digital Twin framework for additive manufacturing systems. A Digital Twin is a virtual replica of a physical system that continuously updates using real time data acquired from sensors and machine interfaces. In this study, a comprehensive digital twin model of a 3D printing process is developed by integrating physical parameters such as temperature, layer thickness, deposition rate, and machine dynamics with simulation and data analytics tools. The virtual model is designed to mirror the real-time behaviour of the AM process, enabling monitoring, simulation, and prediction of system performance. The developed digital twin facilitates process optimization by predicting defects, minimizing downtime, and improving product quality through closed-loop control strategies. Advanced data analytics and machine learning techniques are incorporated to enhance predictive accuracy and adaptability. The performance of the digital twin system is validated by comparing simulated results with experimental data obtained from actual AM operations. This research demonstrates that the implementation of digital twin technology can significantly improve process transparency, efficiency, and reliability in additive manufacturing. The proposed framework contributes to the advancement of smart manufacturing and aligns with the principles of Industry 4.0 by enabling intelligent, data-driven decision-making in AM environments. | |||||||||||||||||
51 | FOET | Prof. Harshil Modi | 6354031638 | hcm01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Study of the Dynamic behaviour and fatigue characteristics of sandwich composite panels under Transportation - induced Loads (Automobile application) | Additive Manufacturing | Sandwich composite panels are increasingly being adopted in automotive applications due to their superior strength-to-weight ratio, enhanced energy absorption capability, and excellent fatigue resistance. However, during service, these structures are subjected to complex transportation-induced loads such as vibrations, cyclic stresses, and impact forces, which can significantly influence their dynamic behaviour and long-term durability. This project focuses on the study of the dynamic response and fatigue characteristics of sandwich composite panels under realistic automotive loading conditions. The research involves the design and simulation of a lightweight sandwich composite panel consisting of suitable face sheet materials and core configurations tailored for automotive applications. Finite Element Analysis (FEA) is employed to evaluate modal characteristics, natural frequencies, damping behaviour, and vibration response under varying boundary conditions. Additionally, fatigue analysis is carried out to assess the life cycle performance of the panel under cyclic loading representative of road-induced excitations. Different material combinations and geometric configurations are investigated to optimize structural performance, weight reduction, and durability. The study also considers failure mechanisms such as core shear, face sheet delamination, and fatigue cracking. The simulation results are validated through analytical methods or available experimental data where feasible. The outcomes of this work provide insights into the design of robust and lightweight composite panels capable of withstanding dynamic automotive environments. This research contributesto the development of advanced materials for next-generation vehicles, supporting improved fuel efficiency, safety, and structural reliability. | Mrs. Sriya jamubkar, Industry Mentor, sriya.jambukar@gmail.com | ||||||||||||||||
52 | FOET | Prof. Harshil Modi | 6354031638 | hcm01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Study of Repair UAV’s integrated framework for Realtime impact Damage Detection and optimized repair of polymer composite Laminates for UAV Application panels under Transportation - induced Loads (Automobile application) | Additive Manufacturing | Unmanned Aerial Vehicles (UAVs) extensively utilize polymer composite laminates due to their high strength-to-weight ratio, corrosion resistance, and structural efficiency. However, these materials are highly susceptible to impact damage, which may not always be visible but can significantly compromise structural integrity and operational safety. This project presents a comprehensive study on the development of an integrated framework for real-time impact damage detection and optimized repair of polymer composite laminates for UAV applications. The proposed framework combines advanced sensing technologies, data acquisition systems, and intelligent algorithms to enable continuous structural health monitoring of UAV components. Techniques such as acoustic emission, vibration analysis, and embedded sensor networks are explored for real-time detection and localization of impact-induced damage. The collected data is processed using data-driven models and machine learning approaches to accurately assess damage severity and predict its progression. Furthermore, the study investigates optimized repair strategies tailored for composite laminates, including additive manufacturing-based patch repair, resin injection, and automated surface restoration techniques. The integration of detection and repair within a unified system allows for rapid decision-making and minimal operational downtime. Simulation and experimental validation are performed to evaluate the effectiveness of the framework in restoring mechanical performance and extending the service life of UAV structures. This research contributes to the advancement of intelligent maintenance systems for UAVs by enhancing safety, reliability, and cost-effectiveness. The proposed approach aligns with emerging trends in smart materials, autonomous systems, and Industry 4.0, offering a scalable solution for next-generation aerospace applications. | Mrs. Sriya jamubkar, Industry Mentor, sriya.jambukar@gmail.com | ||||||||||||||||
53 | FOET | Prof. Harshil Modi | 6354031638 | hcm01@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | COE -AM (3D Printing) | Additive Manufacturing of Bio-Inspired Lattice Structures for High Strength-to Weight Ratio | Additive Manufacturing | Nature provides numerous examples of highly efficient structural designs that combine strength, flexibility, and lightweight characteristics. Bio-inspired lattice structures, derived from natural systems such as bone, honeycomb, and cellular structures, offer exceptional mechanical properties that can be utilized in engineering applications. However, traditional manufacturing techniques face challenges in fabricating such complex geometries. This project focuses on the design and fabrication of bio-inspired lattice structures using Additive Manufacturing. Various bio-inspired architectures will be developed based on natural structural patterns and analysed using finite element simulations to evaluate their mechanical behaviour under compressive and tensile loading conditions. The optimized lattice designs will be fabricated using AM processes, and experimental tests will be conducted to evaluate their strength, stiffness, and weight efficiency. The research aims to demonstrate how bio-inspired design combined with AM can lead to innovative lightweight structures suitable for applications in aerospace, automotive, and biomedical engineering. | Mrs. Sriya jamubkar, Industry Mentor, sriya.jambukar@gmail.com | ||||||||||||||||
54 | FOET | Bharatkumar Doshi & Dr. Ritesh Tirole | 9924860257 | Brd02@ganpatuniversity.ac.in, ritesh.tirole@ganpatuniversity.ac.in | U.V. Patel College of Engineering (UVPCE) | Mechanical- Ganpat University | Design, manufacturing and testing of 2kWh battery using CYLINDRICAL Lithium Ion cells following series and parallel electrical connections and implementation of BMS. | EV battery | EV is the current trend in Automobile sector to control the carbon emissions. The demand for EV batteries will be increasing many folds by 2030. LIB is most promising and particularly NMC/LFP cells of prismatic format is a demand for energy storage solutions. In this research project it is expected to design the baterrey using readily available LIB cells and manufactured the baterrey with appropriate series and parallel connection and housed in a 3D printed battery housing. All the required testing needs to be performed. | |||||||||||||||||
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