Research on classification and identification method of installation position deviation of spiral bevel gears based on improved ResNet-18 model
Aiming at the problems of low efficiency in identification of spiral bevel gear installation position deviation,the improved ResNet-18 neural network was used to build a light-weight spiral bevel gear installation position deviation classification model.The improved network modified the first convolutional layer of the ResNet-18 network,and introduced a channel and spatial attention mechanism.The experimental results showed that,for gear contact pattern identification,the accuracy of the improved ResNet-18 was 3.23%higher than that of the original network,and the loss value was reduced by 0.04.The proposed method offers a new path for gear installation deviation identification,and provides guidance in assembling the spiral bevel gear.
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