Online monitoring of local defects in robotic laser additive manufacturing process based on a dynamic mapping strategy and multibranch fusion convolutional neural network

Online monitoring is essential for laser additive manufacturing (AM) to improve in-process quality control. Currently, accurate monitoring of local defects in the laser AM process remains a challenge. This paper proposes a method for predicting local defects in the laser AM process based on a dynamic mapping strategy and the multibranch fusion convolutional neural network (MBFCNN). In-situ sensing of the laser-material interaction zone is achieved using a camera integrated coaxially with the printing system. Experiment-based datasets are constructed, in which the in-process images were sampled and matched to the extracted local defect information based on their temporal-spatial correspondence. A dynamic mapping strategy using a sliding sampling window is introduced to achieve continuous monitoring. Considering the cyclic and layer-by-layer processing principle of laser AM, we propose MBFCNN to map the in-process images to local defect information. A multibranch feature extraction module is designed based on the deposited layers of the target region to be monitored, in which each branch extracts high-dimensional representations from in-process images corresponding to a certain layer. We further introduce an attention mechanism to distinguish the importance of each branch and a feature fusion module to fuse the high-level information. Experimental results and comparison with traditional convolutional neural networks demonstrate the effectiveness of our method.