Laser cladding technology uses a high-power laser beam to melt the substrate and metal powder at high temperature to form a molten pool. Relying on the spontaneous cooling of the molten pool, a metal cladding coating is formed on the substrate to strengthen the surface properties of the substrate metal. However, the typical defects such as cracks are easy to occur during the cladding process, which greatly affects the performance and quality of the cladded layer. This paper proposes a method for the state identification of cladding and the crack detection in the laser cladding process. By monitoring the acoustic emission signal during the laser cladding process, the current cladding state such as the status of laser power, scanning speed, and powder feed rate, and the occurrence of cracks are identified. By collecting the acoustic emission signal, the method first performs the data preprocessing for signal feature components according to the characteristic parameters of the signal maximum peak value and the energy of the emission signal samples, and then a deep learning neural network is applied to extract the feature vectors based on the two major characteristics of the signal. Finally, the current cladding states are recognized and the generation of cracks are detected based on the extracted feature vector and the identification through the neural network.

激光熔覆技术使用大功率激光束在高温下熔化基板和金属粉末，形成熔池。依靠熔池的自发冷却，在基底上形成金属覆层以增强基底金属的表面性能。然而，典型的缺陷例如裂纹在覆层过程中容易发生，这极大地影响了覆层的性能和质量。本文提出了一种用于激光熔覆过程中熔覆层状态识别和裂纹检测的方法。通过监视激光熔覆过程中的声发射信号，可以识别当前熔覆状态，例如激光功率，扫描速度和粉末进给速度的状态以及裂纹的发生。通过收集声发射信号，该方法首先根据信号最大峰值的特征参数和发射信号样本的能量对信号特征分量进行数据预处理，然后应用深度学习神经网络基于两个主要特征提取特征向量。信号的特性。最后，基于提取的特征向量和通过神经网络的识别，识别当前的熔覆状态并检测裂纹的产生。