1 Introduction
Si3N4 ceramic materials have high
mechanical properties such as low coefficient of thermal expansion, heat
resistance, corrosion resistance and wear resistance[1]. Ceramic materials also have thermal,
electrical and chemical properties [2]. As a
result, ceramic bearings are widely used in aerospace engines,
biomedical devices and transportation. Since the process of ceramic
products is susceptible to factors such as mixing and sintering, they
are prone to external defects [3]. The main types
of defects are pit, crack, wear and snowflake [4],
which affect the performance of ceramic materials. The defects of
ceramic products are relatively small and difficult to be recognized by
the human eye. At present, the inspection methods for surface defects of
ceramic materials at home and abroad are mainly divided into two
methods: manual inspection and nondestructive testing. Manual inspection
method test results have the disadvantages of low detection efficiency,
high false detection rate, randomness, and poor stability.
Non-destructive testing (NDT) [5-7] is a method
for detecting the type, number, shape, location and size of surface
defects in ceramic materials without changing the surface or internal
structure of the ceramic.
With the rapid development of artificial intelligence (AI) and machine
learning, convolutional neural networks(CNNs) have been widely used in
computer vision. Such as object detection, student teaching[8], health analysis, etc. Aiming at the vehicle
navigation light guide plate (LGP) image characteristics, Li et al[9] proposed proposed a visual detection method
based on improved RetinaNet. They proposed and used an improved feature
pyramid network module to improve the feature fusion network in the
retinal network. Experimental results show that the method is effective.
In the vehicle LGP data set, the average detection rate is 98.6%.
Mingming Zhu [10] et al. proposed a
RetinaNet-based method for detecting arbitrarily oriented ships. By a
rotated RetinaNet, a refined network, a feature alignment module, and an
improved loss function, rotated detection, achieving better detection
progress and solving boundary discontinuity are achieved to locate ships
with high accuracy. Yantong Chen [11] et al.
proposed a fly species recognition method based on the refined RetinaNet
and the convolutional block attention module (CBAM). A multi-scale
feature pyramid was constructed. Kullback-Leibler (KL) loss superseded
smoothed L1 loss for simultaneously learning bounding box regression and
localization uncertainty. The method achieves an average accuracy (mAP)
of 90.38%, which is better than existing methods. The average time to
identify a single image is 0.131s.
Combining NDT techniques with deep learning methods to process
Si3N4 ceramic bearing inner ring surface
defect images, online data augmentation is used to expand the images and
form the dataset. Attention mechanisms SE, CBAM and NAM are added to the
tail of the Resnet-50 feature extraction network, respectively. It is
found that the NAM attention mechanism improves the model recognition
and classification accuracy most significantly by comparison. Improved
loss function replaces Smooth L1 loss for learning bounding box
regression and uncertain localization. The
Si3N4 ceramic bearing inner ring surface
defect recognition and classification model is obtained by training the
network for testing surface defects. The main contributions of this
paper are as follows: ⑴the platform for surface defects of
Si3N4 ceramic bearing inner ring is
built independently to collect surface defects images, ⑵the online data
augmentation is used to expand the surface defect dataset, ⑶adding NAM
to obtain a more complete attention map and better global information of
the image. The results show that the proposed method has better
performance compared with the Faster RCNN and RetinaNet.
The rest of this paper is organized as follows. Chapter 2 introduces the
platform for surface defects of Si3N4ceramic bearing inner ring. Chapter 3 presents the
Si3N4 ceramic bearing inner ring surface
defect analysis and dataset fabrication. Chapter 4 introduces the
Si3N4 ceramic bearing inner ring surface
defect identification and classification model. Chapter 5 outlines the
experimental results and analyzes the feasibility of the proposed
method. Chapter 6 presents the conclusion.