Review on the Recent Welding Research with Application of CNN-Based Deep Learning Part II: Model Evaluation and Visualizations

During machine learning algorithms, deep learning refers to a neural network containing multiple hidden layers. Welding research based upon deep learning has been increasing due to advances in algorithms and computer hardwares. Among the deep learning algorithms, the convolutional neural network (CNN) has recently received the spotlight for performing classification or regression based on image input. CNNs enables end-to-end learning without feature extraction and in-situ estimation of the process outputs. In this paper, 18 recent papers were reviewed to investigate how to apply CNN models to welding. The papers was classified into 5 groups: four for supervised learning models and one for unsupervised learning models. The classification of supervised learning groups was based on the application of transfer learning and data augmentation. For each paper, the structure and performance of its CNN model were described, and also its application in welding was explained.

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