Slm printing defect detection and repair method and system based on deep learning network

Abstract

The present invention discloses to a selective-laser-melting (SLM) printing defect detection and repair method and system based on a deep learning network and belongs to the technical field of additive manufacturing. The method includes: training a first neural network model through a defect dataset to obtain a defect recognition model; in a printing process of a part to be detected, performing online defect recognition through the defect recognition model; if a current layer has no defects, continuing to perform powder spreading and printing on a next layer; if a defect is recognized in the current layer, selecting whether to repair the defect according to a defect type; if defect repair is needed, after the current layer is printed, suspending powder spreading once, predicting laser remelting parameters by adopting a pre-trained second neural network model, and performing laser remelting repairing until the current layer has no defects; and repeating the processes of online defect recognition and laser remelting repair until the part to be detected is printed. In the present invention, the online defect recognition and repair are realized, and the real-time performance of the defect repair is improved.