Abstract: The automatic classification and recognition algorithm of mechanical parts has broad application prospects
in the fields of intelligent industry and automatic processing. In the automatic classification of automobile engine
main bearing cap parts, there are difficult problems such as multi-surface distribution of features and light sensitivity.
A multi-branch feature fusion convolutional neural network (MFF-CNN) was designed. The two sub-network
branches of the MFF-CNN can extract the features of the two surfaces of the main bearing cap respectively, and form
the final part classification feature after feature fusion. In terms of network structure design, the MFF-CNN was based
on a densely-connected convolutional neural network design. By enhancing the feature reuse between network layers,
the parameter amount of the model was effectively reduced, and the problems of overfitting and gradient
disappearance of deep networks can be alleviated under the condition of small sample size. The experimental results
show that the MFF-CNN can attain the recognition rate of 91.6% on the image data set of the main bearing cap
collected in practice, and it displays good robustness in terms of the problem of uneven illumination of the parts’
images in actual production.

Key words: mechanical part recognition, convolutional neural network, fine-grained image classification, feature
fusion