基于深度学习激光熔覆层树枝晶的形貌识别

在增材制造技术中，树枝晶的表征对于分析激光熔覆层的机械性能至关重要，但目前树枝晶的标记主要由人工完成，耗时长且容易引入人为误差，而深度学习可提高目标识别准确度。基于U-net网络提出了适于识别分割树枝晶的BNC-Unet网络，将串行注意力机制和Batch Normalization层有效地部署在上采样和下采样区域，调整图像特征的权重信息。选取交并比作为分割结果的评价指标，并对比了原Unet以及不同的改进方法在该指标下的结果。在测试集中，BNC-Unet网络分割树枝晶准确率指标为84.2%，比原U-net网络结果提升了8.97%。该指标表明网络能准确地从激光熔覆层金相图中识别出树枝晶形貌，且识别树枝晶的准确率显著提高，便于在激光熔覆试验后评估熔覆层性能。

Morphology identification of dendrites of laser cladding layer based on deep learning

In the manufacturing technology of additive materials, the characterization of dendrites is crucial for analyzing the mechanical properties of laser cladding layer. However, the labeling of the dendrites is mainly completed manually at present, which is time-consuming and easy to introduce artificial errors, while the deep learning can improve the accuracy of target recognition. Based on the U-net network, the BNC-Unet network which was suitable for the identification and segmentation of dendrites was proposed. The serial attention mechanism and the Batch Normalization layer were effectively deployed in the upsampling and downsampling regions to adjust the weight information of image features. The intersection over union (IoU) was selected as the evaluation index of the segmentation results, and the results of original U-net network and different improved methods under this index were compared. In the test set, the segmentation accuracy index of BNC-Unet network for dendrites is 84.2%, which is 8.97% higher than the results of original U-net network. The index shows that the BNC-Unet network can accurately identify the morphology of dendrites from metallographic diagrams of laser cladding layer, and the accuracy of dendrites identification is significantly improved, which is convenient for evaluating the properties of cladding layer after the laser cladding test.