Data-Enabled Tribological Engineering: Experiments to Predictive Models

Friction, wear, and lubrication dictate the lifespan and efficiency of mechanical systems, yet traditional engineering often relies on slow trial-and-error testing. Modern engineering solves this by combining physical experimentation with data science to predict material behavior accurately. This text-based course bridges the gap between physical tribology and predictive data modeling, preparing you to design smarter, longer-lasting mechanical systems. You will start with foundational concepts in surface contact, friction mechanisms, and wear theories before moving into modern data collection and analysis methodologies. Through clear, written explanations, you will discover how to translate experimental laboratory data into robust predictive models using modern statistical methods and machine learning frameworks. What you'll learn: - Understand the core principles of friction, wear, and lubrication regimes - Design robust tribological experiments to collect high-quality engineering data - Apply modern data cleaning and preprocessing techniques to noisy sensor outputs - Build predictive models using regression and basic machine learning algorithms - Analyze surface roughness and material degradation using digital data workflows - Interpret model predictions to optimize component life and prevent mechanical failure This course begins with basic definitions and fundamental tribological concepts, gradually introducing statistical tools, Python-based data modeling approaches, and predictive engineering workflows. It is designed for engineering students, maintenance professionals, and materials researchers who are new to data-driven engineering and want to modernize their analytical skills. No advanced programming or machine learning background is required to begin. Start exploring the intersection of data science and surface engineering today.