| description abstract | Abstract. Laser shock peening (LSP) can significantly enhance material properties, but fluctuations in laser power, changes in the confinement and absorbing layers during the process may compromise quality. Conventional detection methods often lack the capability to identify such issues in real time. This study introduces an acoustic emission (AE)-based online monitoring approach to detect laser power anomalies, variations in the confinement medium, and ruptures in the absorbing layer during LSP. The particle swarm optimization algorithm was applied to quickly and accurately locate AE sources from multiple sensors, enabling efficient real-time analysis of AE signals, which makes real-time monitoring possible. Variations in laser power, confinement medium, and absorbing layer integrity were reflected in noticeable changes in the AE signals. Additionally, both time-domain and frequency-domain characteristics of the AE signals were analyzed, revealing correlations with surface hardness and residual stress of the target material. This method provides a new approach to quality control in LSP, enabling real-time monitoring that ensures consistency and reliability in processing quality. | |
