Design of Defect Classification on Clay Tiles using Support Vector Machine (SVM)

2020 ◽  
Author(s):  
Murman Dwi Prasetio ◽  
Mohammad Husain Rifai ◽  
Rais Yufli Xavierullah
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Defect Classification ◽  
Clay Tiles

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 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

Intelligent Detection and Defect Classification of Infusion Bags Based on Support Vector Machine

2019 ◽  
Vol 56 (13) ◽  
pp. 131502
Author(s):  
李丹 Dan Li ◽  
金媛媛 Yuanyuan Jin ◽  
童艳 Yan Tong ◽  
白国君 Guojun Bai ◽  
杨明 Ming Yang
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Defect Classification ◽  
Intelligent Detection

Wood Defect Classification Based On Support Vector Machine

2016 ◽  
Vol 9 (11) ◽  
pp. 179-190 ◽  
Author(s):  
Hongbo Mu ◽  
Yang Yang ◽  
Haiming Ni ◽  
Dawei Qi
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Defect Classification

scholarly journals Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

2021 ◽  
Vol 2 (2) ◽  
pp. 141
Author(s):  
Murman Dwi Prasetio ◽  
Rais Yufli Xavier ◽  
Haris Rachmat ◽  
Wiyono Wiyono ◽  
Denny Sukma Eka Atmaja
Keyword(s):  
Support Vector Machine ◽  
Industrial Development ◽  
Rapid Development ◽  
Raspberry Pi ◽  
Support Vector ◽  
Linear Kernel ◽  
Svm Algorithm ◽  
Supervised Learning Algorithms ◽  
Clay Tiles

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards.  One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight.  It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods.  Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.

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