基于卷积神经网络的船舶复合接头焊接损伤识别

提出一种基于卷积神经网络（Convolutional Neural Network，CNN）的船舶复合接头焊接损伤识别方法。采用Ansys软件对焊接过程进行模拟，考虑5种不同损伤位置和损伤程度的模拟工况，采集不同工况条件下复合接头的应变响应数据。构建一维CNN，将数据分为训练集和测试集放入神经网络中进行训练和测试，验证该方法对复合接头焊接过程中不同损伤位置和损伤程度进行识别的适用性。结果表明，该方法在结构焊接损伤检测方面具有良好的检测性能。

Welding Damage Identification of Ship Composite Joint Based on Convolutional Neural Network

A welding damage identification method of ship composite joint based on Convolutional Neural Network (CNN) is proposed. The welding process is simulated with the Ansys software, 5 simulated operation conditions with different damage positions and damage degrees are considered, and the strain response data of composite joints under different operation conditions are collected. A one-dimensional CNN is constructed, and the data are divided into the training set and test set and are put into the neural network for training and testing, so as to verify the applicability of the method to identify different damage positions and damage degrees during the composite joint welding. The results show that the method is of good testing performance in the structural welding damage testing.