ABOUT THE COURSE:Engineering systems encounter numerous challenges related to energy losses, surface degradation, and increased maintenance costs caused by friction and wear. Traditionally, addressing these challenges has been both time-consuming and expensive, and conventional solutions often struggle to fully comprehend the intricate complexities of surface interactions. However, recent advancements in data-enabled engineering have revolutionized the field of tribology, empowering engineers with predictive models driven by data analysis. Data-enabled engineering approaches harness cutting-edge sensor technologies, data acquisition methods, and computational power to access vast amounts of tribological data, encompassing experimental measurements, simulations, and historical records. By tapping into this wealth of data, engineers can gain profound insights into tribological phenomena and devise more efficient and effective solutions. The integration of experimental measurements with data-driven techniques paves the way for the creation of highly accurate and reliable predictive models. These models not only optimize tribological designs but also facilitate the prediction of system behavior, empowering engineers to make well-informed decisions. Thanks to their ability to capture intricate interactions, dependencies, and trends, predictive models offer numerous advantages, including design optimization, reduced experimental costs, and enhanced system performance and reliability. In essence, data-enabled engineering methodologies are set to transform tribology by providing a new frontier of predictive models that deliver significant insights and solutions for enhancing the efficiency, dependability, and longevity of mechanical systems across various industrial applications.INTENDED AUDIENCE: Students of UG & PG.PREREQUISITES: Basic courses on instrumentation, material science, physics,chemistry, and mathematics.INDUSTRY SUPPORT: ONGC, Hero MotoCorp, Gear Manufacturers, Power plants.
Data-Enabled Tribological Engineering: From Experiments to Predictive Models
Indian Institute of Technology Delhi and NPTEL via Swayam
This course may be unavailable.
Overview
Syllabus
Week 1: Tribology andits ChallengesLecture -01 Introduction to TribologyLecture -02 Tribological InterfacesLecture -03 Fundamentals of friction and wear
Week 2:Tribology andits ChallengesLecture -04 Adhesion, abrasion, and surface fatigue mechanismsLecture -05 Wear measurement techniquesLecture -06 Principles of lubrication, types of lubricants and their properties
Week 3:Data-EnabledTribologicalapproachesLecture -07 Lubrication regimes and film thickness calculationsLecture -08 Mixed LubricationLecture -09 Hydrodynamic lubrication theory
Week 4: Data-EnabledTribologicalapproachesLecture -10 Design considerations for hydrodynamic lubrication systemsLecture -11 Elastohydrodynamic LubricationLecture -12 Solid Lubrication
Week 5:Data-EnabledTribologicalapproachesLecture -13 Surface modification techniques for tribological applicationsLecture -14 Thin film coatings and their tribological propertiesLecture -15 Nanotribology
Week 6:The Role of Experiments in Data-Enabled Tribological EngineeringLecture -16 TribocorrosionLecture -17 MWear testing techniques and standardsLecture -18 Measurement and analysis of wear debris
Week 7:The Role of Experiments in Data-Enabled Tribological EngineeringLecture -19 Experimental Design and Statistical AnalysisLecture -20 Introduction to Data-Enabled EngineeringLecture -21 Data Collection and Preprocessing
Week 8:The Role of Experiments in Data-Enabled Tribological EngineeringLecture -22 Feature Extraction and SelectionLecture -23 Introduction to machine learning algorithmsLecture -24 Regression and classification algorithms for tribological modeling.
Week 9:Predictive Models: The Future of Tribological EngineeringLecture -25 Deep Learning for Tribological EngineeringLecture -26 Data-Driven Models for Friction PredictionLecture -27 Data-Driven Models for Wear Prediction
Week 10:Predictive Models: The Future of Tribological EngineeringLecture -28 Data-Driven Models for Lubricant OptimizationLecture -29 Data-Driven Models for Tribofilm FormationLecture -30 Data-Driven Models for Tribocorrosion Prediction
Week 11:Predictive Models: The Future of Tribological EngineeringLecture-31 Prediction of coating and surface treatment performanceLecture -32 Optimization of surface engineering processes using machine learningLecture -33 Uncertainty Quantification and Sensitivity Analysis
Week 12:Predictive Models: The Future of Tribological EngineeringLecture -34 Data Management and Ethics in Data-Enabled EngineeringLecture -35 Case Studies in Data-Enabled Tribological EngineeringLecture -36 Future Directions in Data-Enabled Tribological Engineering
Week 2:Tribology andits ChallengesLecture -04 Adhesion, abrasion, and surface fatigue mechanismsLecture -05 Wear measurement techniquesLecture -06 Principles of lubrication, types of lubricants and their properties
Week 3:Data-EnabledTribologicalapproachesLecture -07 Lubrication regimes and film thickness calculationsLecture -08 Mixed LubricationLecture -09 Hydrodynamic lubrication theory
Week 4: Data-EnabledTribologicalapproachesLecture -10 Design considerations for hydrodynamic lubrication systemsLecture -11 Elastohydrodynamic LubricationLecture -12 Solid Lubrication
Week 5:Data-EnabledTribologicalapproachesLecture -13 Surface modification techniques for tribological applicationsLecture -14 Thin film coatings and their tribological propertiesLecture -15 Nanotribology
Week 6:The Role of Experiments in Data-Enabled Tribological EngineeringLecture -16 TribocorrosionLecture -17 MWear testing techniques and standardsLecture -18 Measurement and analysis of wear debris
Week 7:The Role of Experiments in Data-Enabled Tribological EngineeringLecture -19 Experimental Design and Statistical AnalysisLecture -20 Introduction to Data-Enabled EngineeringLecture -21 Data Collection and Preprocessing
Week 8:The Role of Experiments in Data-Enabled Tribological EngineeringLecture -22 Feature Extraction and SelectionLecture -23 Introduction to machine learning algorithmsLecture -24 Regression and classification algorithms for tribological modeling.
Week 9:Predictive Models: The Future of Tribological EngineeringLecture -25 Deep Learning for Tribological EngineeringLecture -26 Data-Driven Models for Friction PredictionLecture -27 Data-Driven Models for Wear Prediction
Week 10:Predictive Models: The Future of Tribological EngineeringLecture -28 Data-Driven Models for Lubricant OptimizationLecture -29 Data-Driven Models for Tribofilm FormationLecture -30 Data-Driven Models for Tribocorrosion Prediction
Week 11:Predictive Models: The Future of Tribological EngineeringLecture-31 Prediction of coating and surface treatment performanceLecture -32 Optimization of surface engineering processes using machine learningLecture -33 Uncertainty Quantification and Sensitivity Analysis
Week 12:Predictive Models: The Future of Tribological EngineeringLecture -34 Data Management and Ethics in Data-Enabled EngineeringLecture -35 Case Studies in Data-Enabled Tribological EngineeringLecture -36 Future Directions in Data-Enabled Tribological Engineering
Taught by
Prof. (HAG) Harish Hirani
