Deep Multi-Modal U-Net Fusion Methodology of Thermal and Ultrasonic Images for Porosity Detection in Additive Manufacturing
Christian Zamiela, Zhipeng Jiang, Ryan Stokes, Zhenhua Tian, Anton Netchaev, Charles Dickerson, Wenmeng Tian, Linkan Bian
Abstract
Abstract We developed a deep fusion methodology of nondestructive in-situ thermal and ex-situ ultrasonic images for porosity detection in laser-based additive manufacturing (LBAM). A core challenge with the LBAM is the lack of fusion between successive layers of printed metal. Ultrasonic imaging can capture structural abnormalities by passing waves through successive layers. Alternatively, in-situ thermal images track the thermal history during fabrication. The proposed sensor fusion U-Net methodology fills the gap in fusing in-situ images with ex-situ images by employing a two-branch convolutional neural network (CNN) for feature extraction and segmentation to produce a 2D image of porosity. We modify the U-Net framework with the inception and long short term memory (LSTM) blocks. We validate the models by comparing our single modality models and fusion models with ground truth X-ray computed tomography (XCT) images. The inception U-Net fusion model achieved the highest mean intersection over union score of 0.93.