Data-Enabled Tribological Engineering Course | IIT Delhi | Predictive Models
Course Details
| Exam Registration | 70 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 12 weeks |
| Categories | Mechanical Engineering |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 24 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Data-Enabled Tribological Engineering: From Experiments to Predictive Models
Engineering systems across industries face persistent challenges from friction and wear, leading to energy losses, surface degradation, and soaring maintenance costs. Traditional tribological problem-solving is often slow, costly, and struggles to capture the full complexity of surface interactions. A paradigm shift is here. Welcome to the era of Data-Enabled Tribological Engineering, a revolutionary approach that leverages sensor technology, vast datasets, and computational power to build predictive models, transforming how we design and maintain mechanical systems.
About the Course
This comprehensive 12-week course, designed for undergraduate and postgraduate students, bridges the gap between foundational tribology and cutting-edge data science. It empowers you to move beyond traditional trial-and-error methods. You will learn to harness experimental data, simulations, and historical records to gain profound insights into tribological phenomena. The core objective is to equip you with the skills to develop accurate, reliable predictive models that can optimize designs, forecast system behavior, reduce experimental overhead, and ultimately enhance the performance, reliability, and longevity of machinery.
Course Instructor: Prof. (HAG) Harish Hirani, IIT Delhi
The course is led by the distinguished Prof. Harish Hirani, a leading authority in the field. With a prolific career mentoring 9 PhDs and 30 M.Tech students at IITs, along with scientists at CSIR-CMERI, Prof. Hirani is dedicated to aligning academic knowledge with evolving industrial needs. His expertise is documented in seminal books like “Fundamentals of Engineering Tribology with Applications” and contributions to prestigious ASM Handbooks on Friction, Lubrication, and Wear Technology. His extensive experience in publishing research and conducting industry education programs ensures the course content is both rigorous and highly relevant.
Who Should Enroll?
Intended Audience: UG & PG students in Mechanical Engineering, Materials Science, Chemical Engineering, and related fields.
Prerequisites: Basic knowledge of instrumentation, material science, physics, chemistry, and mathematics.
Industry Support: This course is highly relevant for professionals and is recognized by major industries including ONGC, Hero MotoCorp, Gear Manufacturers, and Power plants.
Detailed 12-Week Course Layout
Weeks 1-2: Tribology and its Challenges
Build a solid foundation in classical tribology.
- Introduction to Tribology & Tribological Interfaces
- Fundamentals of Friction and Wear Mechanisms
- Wear Measurement Techniques
- Principles of Lubrication and Lubricant Properties
Weeks 3-5: Data-Enabled Tribological Approaches
Dive into advanced lubrication theories and surface engineering.
- Lubrication Regimes: Hydrodynamic, Elastohydrodynamic (EHL), Mixed
- Solid Lubrication and Surface Modification Techniques
- Thin Film Coatings and Nanotribology
Weeks 6-8: The Role of Experiments in Data-Enabled Engineering
Learn to generate and prepare data for analysis.
- Tribocorrosion and Wear Testing Standards
- Experimental Design & Statistical Analysis
- Data Collection, Preprocessing, and Feature Extraction
- Introduction to Machine Learning Algorithms
Weeks 9-12: Predictive Models: The Future of Tribological Engineering
Core module on building and applying data-driven models.
- Regression & Classification for Tribological Modeling
- Deep Learning Applications in Tribology
- Data-Driven Models for Friction, Wear, Lubricant Optimization, and Tribofilm Formation
- Prediction of Coating Performance & Process Optimization
- Uncertainty Quantification, Data Management, Ethics, and Future Directions
Recommended Books and References
| Book Title | Author/Editor |
|---|---|
| Fundamentals of Engineering Tribology with Applications | Harish Hirani |
| Applied Tribology: Bearing Design and Lubrication | Michael M. Khonsari |
| Tribology Data Handbook | E.R. Booser |
| Encyclopedia of Tribology | Q. Jane Wang, Yip-Wah Chung |
| ASM Handbook, Vol. 18: Friction, Lubrication, and Wear Technology | ASM International |
Why This Course is Essential for the Future Engineer
The integration of data science with tribology is no longer a niche—it's the frontier of mechanical engineering innovation. This course offers a unique blend of theoretical depth and practical, data-driven application. You will graduate not only with an understanding of why components fail but with the predictive tools to prevent failure before it happens. By mastering data-enabled tribological engineering, you position yourself at the forefront of designing more efficient, reliable, and sustainable mechanical systems for industries ranging from automotive and aerospace to energy and manufacturing. Enroll today and transition from observing tribological challenges to predicting and solving them.
Enroll Now →