scholarly journals Analysis of Surface Defect Generation Behavior of Wire Rolling Defects: Establishment of the analysis technique to determine the occurrence of surface defects

2014 ◽  
Vol 100 (5) ◽  
pp. 625-631 ◽  
Author(s):  
Hitoshi Kushida ◽  
Yasushi Maeda ◽  
Takashi Ishikawa ◽  
Shoji Sugyo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Defect Generation ◽  
Analysis Technique

scholarly journals Numerical and Experimental Investigation on the Surface Defect Generation during the Hot Extrusion of Al6063 Alloy

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6768
Author(s):  
Namsu Park ◽  
Yeonghwan Song ◽  
Seon-Ho Jung ◽  
Junghan Song ◽  
Jongsup Lee ◽  
...  
Keyword(s):  
Surface Quality ◽  
Manufacturing Industry ◽  
Surface Defect ◽  
Surface Defects ◽  
Scale Up ◽  
Laboratory Scale ◽  
Line Defect ◽  
White Line ◽  
Production Scale ◽  
Defect Generation

The surface quality control of extruded products is a critical concern in the home appliance manufacturing industry owing to the increasing need for products with a high surface quality, in addition to the essential mechanical properties of the final product. The underlying issue with achieving high-quality extrusion products is that surface defects, especially those resulting in surface gloss differences, called white line defects, are only observed after surface treatment. In this study, we aim to investigate the cause of white line defect generation on the surface of an extruded product. Accordingly, an experimental extrusion program is established using an L-shaped die that has a noticeable change in its bearing length along the inner corner of its cross-sectional profile. Laboratory-scale experiments were performed for the L-shaped extrusion of homogenized Al 6063 alloy at various ram speeds, in order to induce surface defects, considering the production yield rate required for mass production. Subsequently, the microstructural changes near the surface failure region were investigated using an arbitrary Lagrangian–Eulerian (ALE) technique-based thermomechanical finite element (FE) analysis. To scale-up the defect observation method from laboratory-scale to production-scale manufacturing and confirm the reproducibility of the surface defect, scaled-up L-shaped extrusions were performed in an actual industrial production line. Finally, the potential cause of white line defect generation is discussed by comparing the numerical and metallurgical analyses, including the scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) observations.

scholarly journals X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

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.

Surface defect chemistry of Y-substituted and hydrated BaZrO3 with subsurface space-charge regions

2016 ◽  
Vol 4 (19) ◽  
pp. 7437-7444 ◽  
Author(s):  
Jonathan M. Polfus ◽  
Tor S. Bjørheim ◽  
Truls Norby ◽  
Rune Bredesen
Keyword(s):  
Surface Layer ◽  
Space Charge ◽  
Space Charge Region ◽  
First Principles ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Chemistry ◽  
First Principles Calculations ◽  
Proton Conducting ◽  
Charged Species

First-principles calculations were utilized to elucidate the complete defect equilibria of surfaces of proton conducting BaZrO3, encompassing charged species adsorbed to the surface, defects in the surface layer as well as in the subsurface space-charge region and bulk.

scholarly journals Dynamics of Assembled Structures: Taking Into Account the Surface Defects in Interfaces

2013 ◽  
Author(s):  
Nicolas Peyret ◽  
Gaël Chevallier ◽  
Jean-Luc Dion
Keyword(s):  
Loss Factor ◽  
Surface Defect ◽  
Test Bench ◽  
Surface Defects ◽  
Dissipated Energy ◽  
Energy Losses ◽  
Proposed Model ◽  
Nonlinear Loss ◽  
Energy Dissipated ◽  
Experimental Bench

In structural dynamics, the prediction of damping remains the biggest challenge. This paper deals with the energy losses caused by micro-slip in a nominally planar interface of a structure. This paper proposes an analytical and experimental study of flexural vibrations of a clamped-clamped beam with innovative position of the interfaces. The objective of this test bench is to characterize the global rheology of the interface. The proposed model aims to characterize this rheology based on local settings of the interface. First, the test bench is described and the choice of the position of the interface is justified. The experimental bench and the dynamic behavior of this structure are presented. We propose to illustrate the mechanism of energy losses by micro-slip by making a comparison between the behavior of a “monolithic” beam and a sectioned beam. Secondly, a modeling of the interface taking into account the surface defect is presented. The energy dissipated by friction in the interface is calculated during a loading cycle. This leads to a computation of the dissipated energy and thus to a nonlinear loss factor. Finally, we confront the loss factor calculated analytically and the measured one.

scholarly journals An improved Vision Transformer-based method for classifying surface defects in hot-rolled strip steel

2021 ◽  
Vol 2082 (1) ◽  
pp. 012016
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Learning Ability ◽  
Rolled Strip ◽  
Strip Surface ◽  
Strip Steel ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Improved Algorithm

Abstract A new Vision Transformer(ViT) model is proposed for the classification of surface defects in hot rolled strip, optimizing the poor learning ability of the original Vision Transformer model on smaller datasets. Firstly, each module of ViT and its characteristics are analyzed; Secondly, inspired by the deep learning model VGGNet, the multilayer fully connected layer in VGGNet is introduced into the ViT model to increase its learning capability; Finally, by performing on the X-SDD hot-rolled steel strip surface defect dataset. The effect of the improved algorithm is verified by comparison experiments on the X-SDD hot-rolled strip steel surface defect dataset. The test results show that the improved algorithm achieves better results than the original model in terms of accuracy, recall, F1 score, etc. Among them, the accuracy of the improved algorithm on the test set is 5.64% higher than ViT-Base and 2.64% higher than ViT-Huge; the accuracy is 4.68% and 1.36% higher than both of them, respectively.

Surface Defects in Drawn Aluminum Magnet Wire

1975 ◽  
Vol 97 (1) ◽  
pp. 134-141 ◽  
Author(s):  
R. N. Wright ◽  
A. T. Male
Keyword(s):  
Defect Structure ◽  
Surface Characterization ◽  
Surface Defect ◽  
Potential Role ◽  
Surface Defects ◽  
Repair Process ◽  
Fine Surface ◽  
Magnet Wire ◽  
Characterization Studies

The fine surface defect structure of commercial EC grade aluminum magnet wire has been characterized and four basic component types have been identified. A grading system has been established for each of the component defects. Intermediate process surface characterization studies and laboratory drawing experiments have been performed to clarify the origin of the defects. The potential role of drawing lubrication in repairing or compounding the defect structure has been demonstrated and the mechanics of a drawing related repair process have been clarified through study of the effects of rod drawing on hardness indentations.

Facet-dependent evolution of surface defects in anatase TiO2 by thermal treatment: implications for environmental applications of photocatalysis

2019 ◽  
Vol 6 (6) ◽  
pp. 1740-1753 ◽  
Author(s):  
Tong Li ◽  
Zelin Shen ◽  
Yiling Shu ◽  
Xuguang Li ◽  
Chuanjia Jiang ◽  
...  
Keyword(s):  
Thermal Treatment ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Formation ◽  
Anatase Tio2 ◽  
Environmental Applications

Exposed crystal facets of TiO2 nanomaterials significantly affect the surface defect formation of the materials during thermal treatment.

scholarly journals SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4356 ◽  
Author(s):  
Chi-Yi Tsai ◽  
Hao-Wei Chen
Keyword(s):  
Real Time ◽  
Calcium Silicate ◽  
Surface Defect ◽  
Surface Defects ◽  
Visual Image ◽  
Training Dataset ◽  
Image Information ◽  
Training Stage ◽  
High Recognition Accuracy ◽  
Defect Recognition

This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.

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