Steel surface defect classification using multiple hyper-spheres support vector machine with additional information

2018 ◽  
Vol 172 ◽  
pp. 109-117 ◽  
Author(s):  
Rongfen Gong ◽  
Chengdong Wu ◽  
Maoxiang Chu
Keyword(s):  
Support Vector Machine ◽  
Steel Surface ◽  
Surface Defect ◽  
Support Vector ◽  
Defect Classification ◽  
Additional Information

scholarly journals Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 639
Author(s):  
Chen Ma ◽  
Haifei Dang ◽  
Jun Du ◽  
Pengfei He ◽  
Minbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Support Vector ◽  
Defect Classification ◽  
Droplet Deposition ◽  
Network Support ◽  
Visual Sensing ◽  
Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

scholarly journals Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

2019 ◽  
Vol 68 (3) ◽  
pp. 667-679 ◽  
Author(s):  
Qiwu Luo ◽  
Yichuang Sun ◽  
Pengcheng Li ◽  
Oluyomi Simpson ◽  
Lu Tian ◽  
...  
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Local Binary Patterns ◽  
Defect Classification

scholarly journals Steel Surface Defect Classification Based on Small Sample Learning

2021 ◽  
Vol 11 (23) ◽  
pp. 11459
Author(s):  
Shiqing Wu ◽  
Shiyu Zhao ◽  
Qianqian Zhang ◽  
Long Chen ◽  
Chenrui Wu
Keyword(s):  
Feature Extraction ◽  
Steel Surface ◽  
Surface Defect ◽  
Nearest Neighbor ◽  
Surface Defects ◽  
Feature Space ◽  
Small Sample ◽  
Defect Classification ◽  
Sample Number ◽  
Degradation Problem

The classification of steel surface defects plays a very important role in analyzing their causes to improve manufacturing process and eliminate defects. However, defective samples are very scarce in actual production, so using very few samples to construct a good classifier is a challenge to be addressed. If the layer number of the model with proper depth is increased, the model accuracy will decrease (not caused by overfit), and the training error as well as the test error will be very high. This is called the degradation problem. In this paper, we propose to use feature extraction + feature transformation + nearest neighbors to classify steel surface defects. In order to solve the degradation problem caused by network deepening, the three feature extraction networks of Residual Net, Mobile Net and Dense Net are designed and analyzed. Experiment results show that in the case of a small sample number, Dense block can better solve the degradation problem caused by network deepening than Residual block. Moreover, if Dense Net is used as the feature extraction network, and the nearest neighbor classification algorithm based on Euclidean metric is used in the new feature space, the defect classification accuracy can reach 92.33% when only five labeled images of each category are used as the training set. This paper is of some guiding significance for surface defect classification when the sample number is small.

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

scholarly journals Photovoltaic cell defect classification using convolutional neural network and support vector machine

2020 ◽  
Vol 14 (14) ◽  
pp. 2693-2702
Author(s):  
Ashfaq Ahmad ◽  
Yi Jin ◽  
Changan Zhu ◽  
Iqra Javed ◽  
Asim Maqsood ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Photovoltaic Cell ◽  
Support Vector ◽  
Defect Classification ◽  
Cell Defect
Sign in / Sign up

Export Citation Format

Share Document