Laser cladding state recognition and crack defect diagnosis by acoustic emission signal and neural network

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.