Comprehensive Course Structure
The Mechanical Engineering program at Maulana Azad University Jodhpur is structured over eight semesters, with each semester designed to progressively build upon the previous one. The curriculum balances theoretical knowledge with practical application through a combination of core courses, departmental electives, science electives, and hands-on laboratory experiences.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | ME101 | Engineering Mathematics I | 4-0-0-4 | None |
| I | ME102 | Physics for Engineers | 3-0-0-3 | None |
| I | ME103 | Chemistry for Engineers | 3-0-0-3 | None |
| I | ME104 | Basic Electrical & Electronics | 3-0-0-3 | None |
| I | ME105 | Introduction to Engineering Design | 2-0-2-4 | None |
| I | ME106 | Computer Programming | 3-0-0-3 | None |
| I | ME107 | Workshop Practice | 2-0-4-6 | None |
| II | ME201 | Engineering Mathematics II | 4-0-0-4 | ME101 |
| II | ME202 | Strength of Materials | 3-0-0-3 | ME102 |
| II | ME203 | Thermodynamics | 3-0-0-3 | ME102 |
| II | ME204 | Fluid Mechanics | 3-0-0-3 | ME102 |
| II | ME205 | Machine Design I | 3-0-0-3 | ME202 |
| II | ME206 | Manufacturing Processes | 3-0-0-3 | ME104 |
| III | ME301 | Heat Transfer | 3-0-0-3 | ME203 |
| III | ME302 | Dynamics of Machines | 3-0-0-3 | ME205 |
| III | ME303 | Control Systems | 3-0-0-3 | ME201 |
| III | ME304 | Materials Science | 3-0-0-3 | ME103 |
| III | ME305 | Machine Design II | 3-0-0-3 | ME205 |
| III | ME306 | Industrial Engineering & Management | 3-0-0-3 | ME104 |
| IV | ME401 | Advanced Thermodynamics | 3-0-0-3 | ME203 |
| IV | ME402 | Fluid Machinery | 3-0-0-3 | ME204 |
| IV | ME403 | Numerical Methods & Computer Applications | 3-0-0-3 | ME201 |
| IV | ME404 | Design of Experiments | 3-0-0-3 | ME201 |
| IV | ME405 | Energy Systems & Sustainability | 3-0-0-3 | ME301 |
| IV | ME406 | Project Management | 3-0-0-3 | ME306 |
| V | ME501 | Advanced Manufacturing Processes | 3-0-0-3 | ME206 |
| V | ME502 | Robotics & Automation | 3-0-0-3 | ME303 |
| V | ME503 | Computational Fluid Dynamics | 3-0-0-3 | ME204 |
| V | ME504 | Composite Materials | 3-0-0-3 | ME304 |
| V | ME505 | Renewable Energy Systems | 3-0-0-3 | ME301 |
| V | ME506 | Biomedical Engineering | 3-0-0-3 | ME304 |
| VI | ME601 | Advanced Control Systems | 3-0-0-3 | ME303 |
| VI | ME602 | Finite Element Analysis | 3-0-0-3 | ME302 |
| VI | ME603 | Machine Learning in Engineering | 3-0-0-3 | ME403 |
| VI | ME604 | Smart Grid Technologies | 3-0-0-3 | ME201 |
| VI | ME605 | Energy Storage Systems | 3-0-0-3 | ME401 |
| VI | ME606 | Industry 4.0 | 3-0-0-3 | ME502 |
| VII | ME701 | Capstone Project I | 6-0-0-6 | ME405, ME502, ME601 |
| VII | ME702 | Research Methodology | 3-0-0-3 | ME403 |
| VIII | ME801 | Capstone Project II | 6-0-0-6 | ME701 |
| VIII | ME802 | Thesis | 6-0-0-6 | ME702 |
Detailed Departmental Elective Courses
The department offers a rich selection of advanced elective courses designed to enhance students' expertise in specialized areas. These courses are taught by internationally recognized faculty members and often involve collaboration with industry partners.
Advanced Manufacturing Processes (ME501): This course delves into modern manufacturing techniques such as 3D printing, laser cutting, CNC machining, and micro-manufacturing. Students learn about process optimization, quality control, and sustainability in manufacturing environments. The course includes laboratory sessions using industrial-grade equipment and guest lectures from leading manufacturers.
Robotics & Automation (ME502): This elective introduces students to robotics fundamentals, sensor integration, control algorithms, and machine learning applications. Students work on building autonomous robots, developing navigation systems, and implementing computer vision techniques. The course includes hands-on projects using ROS (Robot Operating System) and physical robots from industry partners.
Computational Fluid Dynamics (ME503): Focused on numerical methods for solving fluid flow problems, this course covers finite volume methods, turbulence modeling, and CFD software applications. Students use tools like ANSYS Fluent and OpenFOAM to simulate complex flow scenarios and analyze results. The course includes practical projects related to aerodynamics, heat transfer, and environmental flows.
Composite Materials (ME504): This course explores the structure, properties, design, and processing of composite materials. Students learn about polymer matrix composites, fiber-reinforced systems, and advanced manufacturing techniques. Laboratory experiments include material testing, mechanical characterization, and failure analysis.
Renewable Energy Systems (ME505): Designed to address global energy challenges, this course covers solar thermal collectors, wind energy systems, hydroelectric power generation, and energy storage technologies. Students study system design, efficiency optimization, and environmental impact assessment. Projects include designing small-scale renewable energy installations.
Biomedical Engineering (ME506): This interdisciplinary course bridges mechanical engineering with healthcare technologies. Topics include medical device design, biomechanics, prosthetics, and tissue engineering. Students engage in research projects involving clinical data analysis, prototype development, and regulatory compliance.
Advanced Control Systems (ME601): Building upon fundamental control theory, this course covers advanced topics such as optimal control, robust control, and adaptive control systems. Students implement control algorithms on real-world systems and develop simulation models using MATLAB/Simulink.
Finite Element Analysis (ME602): This course teaches numerical methods for solving engineering problems using finite element techniques. Students learn to model structures, analyze stress distributions, and predict behavior under various loads. The course includes practical applications in civil, mechanical, and aerospace engineering.
Machine Learning in Engineering (ME603): Integrating artificial intelligence with engineering principles, this course covers supervised and unsupervised learning algorithms, neural networks, and deep learning frameworks. Students apply machine learning techniques to solve real-world engineering problems such as predictive maintenance, optimization, and data-driven design.
Smart Grid Technologies (ME604): Focused on modern power systems, this course explores smart grid components, energy management systems, and integration of renewable sources. Students study grid stability, demand response strategies, and communication protocols used in intelligent power networks.
Energy Storage Systems (ME605): This course examines various energy storage technologies including batteries, supercapacitors, and compressed air systems. Students analyze performance characteristics, design storage solutions, and assess economic viability. Projects include designing battery management systems for electric vehicles.
Industry 4.0 (ME606): Covering the fourth industrial revolution, this course explores IoT technologies, digital twins, cyber-physical systems, and automation in manufacturing. Students learn to design smart factory layouts and implement Industry 4.0 solutions using cloud computing platforms and edge devices.
Project-Based Learning Approach
The department strongly emphasizes project-based learning as a core component of the educational experience. This approach ensures that students develop both technical skills and practical problem-solving abilities while working collaboratively in teams.
Mini-projects are introduced in the third year, with each student selecting a topic aligned with their interests and career goals. These projects typically last 10-12 weeks and involve extensive research, experimentation, and documentation. Students receive guidance from faculty mentors who help them navigate challenges and refine their approaches.
The final-year thesis/capstone project represents the culmination of students' academic journey. It requires them to propose a significant engineering solution or research study that addresses a real-world problem. Projects are supervised by faculty members with expertise in relevant domains, ensuring high-quality outcomes and meaningful contributions to the field.
Students select their projects based on interest areas, mentor availability, and resource constraints. The selection process includes an initial proposal submission followed by peer review and faculty feedback. Successful candidates proceed to implementation phase where they utilize advanced tools, software, and laboratory facilities provided by the university.