The paper presents an analytical tool for early detection and online monitoring of fatigue damage in polycrystalline alloys that are commonly used in mechanical structures of human-engineered complex systems. Real-time fatigue damage monitoring algorithms rely on time series analysis of ultrasonic signals that are sensitive to micro-structural changes occurring inside the material during the early stages of fatigue damage; the core concept of signal analysis is built upon the principles of Symbolic Dynamics, Statistical Pattern Recognition and Information Theory. The analytical tool of statistical pattern analysis has been experimentally validated on a special-purpose test apparatus that is equipped with ultrasonic flaw detection sensors and a travelling optical microscope. The paper reports fatigue damage monitoring of 7075-T6 alloy specimens, where the experiments have been conducted under load-controlled constant amplitude sinusoidal loadings for low-cycle and high-cycle fatigue.
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Materials Science(all)
- Mechanics of Materials
- Mechanical Engineering
- Industrial and Manufacturing Engineering