Surface morphology inspection for directed energy deposition using small dataset with transfer learning

Directed Energy Deposition (DED) is a branch of Additive Manufacturing (AM) that offers extraordinary controllability and the ability to rapidly shape materials directly into large parts with complex structures. Innovative applications of machine learning and neural network algorithms in additive manufacturing processes have recently been developed, but there are still several barriers to the development and practical application of this technology. The large amount of sample data required for visual inspection using a neural network framework leads to the problem of difficult and costly preparation of surface morphology data. In this study, a transfer learning approach based on a small dataset is proposed to detect surface morphology in the DED pro-cedure. The approach proposed effectively and innovatively addresses the sample scarcity issue that arises during neural network training and eliminates the issue of having to recreate a sizable quantity of the matching surface sample data. It also effectively accelerates the model convergence process through the transfer learning method, which indirectly improves detection accuracy. This work selected a parametric transfer learning approach to train YOLOv7, a self-supervised object detection model, for transfer learning. The experiments utilized an open -source defective dataset (NEU-DEF) for transfer learning pretraining. The training was carried out in combination with a home-made object dataset of 240 small samples after transfer of the model parameters. The input to the model is image data and the output of the training is the classification of surface morphology. The results show that the transfer training model 2 pretrained with the Stochastic Gradient Descent (SGD) algorithm has high accuracy and precision, achieving a Mean Average Precision (MAP) value of 0.62. Transfer learning speeds up the feature extraction process by transferring the parameters of a pretrained model for a similar task directly into the current task model, resulting in improved detection performance.