Application of Lightweight Convolutional Neural Network Method Based on SERepVGG-A2 in Melt Pool State Recognition

doi:10.3901/JME.2025.03.440

Abstract

Abstract: A Quasi-Aiming at the problem that the traditional convolutional neural network is applied to the state recognition of the molten pool, the shallow convolutional neural network cannot effectively extract the characteristics of the molten pool, and the deep convolutional neural network has the problems of large number of parameters and high redundancy. A lightweight convolutional neural network (SERepVGG-A2) method is quantified. Firstly, in order to solve the problem of a large number of useless background areas in the original melt pool image, the region of interest (ROI) extraction of the melt pool image is performed to optimize the data set; then, in order to ensure the model feature extraction ability and reduce the complexity of the model, a lightweight convolutional neural network model combining residual structure, attention mechanism and structural parameter reorganization is proposed to identify the molten pool state. The proposed method is verified and illustrated with the online molten pool data of Ti-10Mo titanium alloy in the directed energy deposition process. The results show that the proposed lightweight network model can not only effectively identify the molten pool characteristics according to the molten pool signal, but also it has higher melting pool identification efficiency than traditional CNN methods.

Key words: melting pool state recognition, convolutional neural network, deep learning, attention mechanism, structural parameter reorganization

CLC Number:

TG156

LIU Hanru, CHEN Guiying, XU Zelin, PENG Shitong, GUO Jianan, LIU Weiwei, WANG Fengtao. Application of Lightweight Convolutional Neural Network Method Based on SERepVGG-A2 in Melt Pool State Recognition[J]. Journal of Mechanical Engineering, 2025, 61(3): 440-448.

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