The Case for AI Powered Automated Tribometry in Industrial Reliability

Description

Expert lecture on AI-driven automated tribometry that closes the loop between plant data, adaptive experiments, physics-informed modeling, and design. Covers system architecture, robotics, experimental design, uncertainty, MLOps, and CMMS integration. Includes case studies on friction–wear mapping and rapid lubricant screening, highlighting accelerated insights, reliability gains, and cost savings. Emphasizes data quality, safety, and governance.