scholarly journals Surface Defect Detection of Nonburr Cylinder Liner Based on Improved YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongbin Chen ◽  
Qinshen Fu ◽  
Guitang Wang
Keyword(s):  
Internal Combustion Engine ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defect ◽  
Combustion Engine ◽  
Cylinder Liner ◽  
Internal Combustion ◽  
Detection Algorithm ◽  
Model Framework ◽  
Surface Defect Detection

Cylinder liner plays an important role in the internal combustion engine. The surface defects of cylinder liner will directly affect the safety and service life of the internal combustion engine. At present, the surface defect detection of cylinder liner mainly relies on manual visual inspection, which is easily affected by subjective factors of inspectors. Aiming at the bottleneck of traditional visual inspection technology in appearance inspection, this paper proposes a surface defect detection algorithm based on deep learning to realize defect location and classification. Based on the characteristics of the research object in this paper, the surface defect detection algorithm based on the improved YOLOv4 model is proposed, the model framework is constructed, and the data enhancement method and verification method are proposed. Experiments show that the proposed method can improve the detection accuracy and speed and can meet the requirements of the nonburr cylinder surface defect detection. At the same time, the method can be extended to other surface defect detection applications.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

Influence of positive texturing on friction and wear properties of piston ring-cylinder liner tribo pair under lubricated conditions

2019 ◽  
Vol 71 (4) ◽  
pp. 515-524 ◽  
Author(s):  
Venkateswara Babu P. ◽  
Ismail Syed ◽  
Satish Ben Beera
Keyword(s):  
Wear Resistance ◽  
Internal Combustion Engine ◽  
Friction And Wear ◽  
Piston Ring ◽  
Combustion Engine ◽  
Cylinder Liner ◽  
Internal Combustion ◽  
Tribological Performance ◽  
Friction Reduction ◽  
Content Type

Purpose In an internal combustion engine, piston ring-cylinder liner tribo pair is one among the most critical rubbing pairs. Most of the energy produced by an internal combustion engine is dissipated as frictional losses of which major portion is contributed by the piston ring-cylinder liner tribo pair. Hence, proper design of tribological parameters of piston ring-cylinder liner pair is essential and can effectively reduce the friction and wear, thereby improving the tribological performance of the engine. This paper aims to use surface texturing, an effective and feasible method, to improve the tribological performance of piston ring-cylinder liner tribo pair. Design/methodology/approach In this paper, influence of positive texturing (protruding) on friction reduction and wear resistance of piston ring surfaces was studied. The square-shaped positive textures were fabricated on piston ring surface by chemical etching method, and the experiments were conducted with textured piston ring surfaces against un-textured cylinder liner surface on pin-on-disc apparatus by continuous supply of lubricant at the inlet of contact zone. The parameters varied in this study are area density and normal load at a constant sliding speed. A comparison was made between the tribological properties of textured and un-textured piston ring surfaces. Findings From the experimental results, the tribological performance of the textured piston ring-cylinder liner tribo pair was significantly improved over a un-textured tribo pair. A maximum friction reduction of 67.6 per cent and wear resistance of 81.6 per cent were observed with textured ring surfaces as compared to un-textured ring surfaces. Originality/value This experimental study is helpful for better understanding of the potency of positive texturing on friction reduction and wear resistance of piston ring-cylinder liner tribo pair under lubricated sliding conditions.

A Deep Ensemble Classifier for Surface Defect Detection in Aircraft Visual Inspection

2020 ◽  
Vol 4 (1) ◽  
pp. 20200031
Author(s):  
Ivan Ren ◽  
Feraidoon Zahiri ◽  
Gregory Sutton ◽  
Thomas Kurfess ◽  
Christopher Saldana
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defect ◽  
Ensemble Classifier ◽  
Surface Defect Detection

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

scholarly journals Effects of a Cylinder Liner Microstructure on Lubrication Condition of a Twin-Land Oil Control Ring and a Piston Skirt of an Internal Combustion Engine

2021 ◽  
Author(s):  
Koji Kikuhara ◽  
Philipp S Koeser ◽  
Tian Tian
Keyword(s):  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Cylinder Liner ◽  
Internal Combustion ◽  
Operating Conditions ◽  
Friction Reduction ◽  
Surface Structures ◽  
Sliding Surface ◽  
Piston Skirt ◽  
Lubrication Condition

Abstract It is hypothesized that the sliding surface structures improve the lubrication condition by forming an oil sump on the sliding surface, redistributing the oil, and trapping wear debris. For these reasons, the sliding surface structures have been used as a friction reduction method for a long time. However, how to optimize the sliding surface structure is still controversial. In this work, effects of microstructure laid on the cylinder liner of an internal combustion engine on twin-land oil control ring (TLOCR) and piston skirt lubrication condition were investigated by comparing friction between the conventional fine-honed liner (CFL) and the microstructured liner (MSL) which was made based on the CFL. As a result of the friction measurement using a floating liner engine, it was found that the microstructure improved lubrication condition by reducing hydrodynamic friction. On the other hand, the result showed there was a possibility that the microstructure deteriorated friction depending on the engine operating conditions.

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