scholarly journals Research on laser ultrasonic surface defect identification based on a support vector machine

2021 ◽  
Vol 104 (4) ◽  
pp. 003685042110590
Author(s):  
Chao Chen ◽  
Xingyuan Zhang
Keyword(s):  
Support Vector Machine ◽  
Surface Defect ◽  
Surface Defects ◽  
Support Vector ◽  
Laser Ultrasound ◽  
Defect Depth ◽  
Laser Ultrasonic ◽  
Defect Identification ◽  
Different Depths ◽  
Quantitative Identification

To solve the problem of difficult quantitative identification of surface defect depth during laser ultrasonic inspection, a support vector machine-based method for quantitative identification of surface rectangular defect depth is proposed. Based on the thermal-elastic mechanism, the finite element model for laser ultrasound inspection of aluminum materials containing surface defects was developed by using the finite element software COMSOL. The interaction process between laser ultrasound and rectangular defects was simulated, and the reflected wave signals corresponding to defects of different depths under pulsed laser irradiation were obtained. Laser ultrasonic detection experiments were conducted for surface defects of different depths, and multiple sets of ultrasonic signal waveform were collected, and several feature vectors such as time-domain peak, center frequency peak, waveform factor and peak factor were extracted by using MATLAB, the quantitative defect depth identification model based on support vector machine was established. The experimental results show that the laser ultrasonic surface defect identification model based on support vector machine can achieve high accuracy prediction of defect depth, the regression coefficient of determination is kept above 0.95, and the average relative error between the true value and the predicted value is kept below 10%, and the prediction accuracy is better than that of the reflection echo method and BP neural network model.

scholarly journals An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1102-1110 ◽  
Author(s):  
Yu Wu ◽  
Yanjie Lu
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Rapid Development ◽  
Training Model ◽  
Support Vector ◽  
Ideal Condition ◽  
Product Packaging

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

Big Data Analytics: Prediction of Surface Defects on Steel Slabs Based on One Class Support Vector Machine

2016 ◽  
Author(s):  
Chao-Yung Hsu ◽  
Li-Wei Kang ◽  
Ming-Fang Weng
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Production Line ◽  
Surface Defects ◽  
Steel Production ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Process Data ◽  
Steel Slabs

Visible surface defects are common in steel products, such as crack or scratch defects on steel slabs (a main product of the upstream production line in a steel production line). In order to prevent propagation of defects from the upstream to the downstream production lines, it is important to predict or detect the defects in earlier stage of a steel production line, especially for the defects on steel slabs. In this paper, we address the problem regarding the prediction of surface defects on continuous casting steel slabs. The main goal of this paper is to accurately predict the occurrence of surface defects on steel slabs based on the online collected data from the production line. Accurate prediction of surface defects would be helpful for online adjusting the process and environmental factors to promote producibility and reduce the occurrence of defects, which should be more useful than only inspection of defects. The major challenge here is that the amounts of samples for normal cases and defects are usually unbalanced, where the number of defective samples is usually much fewer than that of normal cases. To cope with the problem, we formulate the problem as a one class classification problem, where only normal training data are used. To solve the problem, we propose to learn a one-class SVM (support vector machine) classifier based on online collected process data and environmental factors for only normal cases to predict the occurrence of defects for steel slabs. Our experimental results have demonstrated that the learned one class SVM (OCSVM) classifier performs better prediction accuracy than the traditional two-class SVM classifier (relying on both positive and negative training samples) used for comparisons.

scholarly journals Surface Defect Identification and Grouping of Intermittent Leather Images using Linear Discriminant Model

2019 ◽  
Vol 8 (12) ◽  
pp. 2253-2259
Keyword(s):  
Support Vector Machine ◽  
Visual Analysis ◽  
Surface Defects ◽  
Texture Features ◽  
Support Vector ◽  
Natural Material ◽  
Linear Discriminant ◽  
Discriminant Model ◽  
Occurrence Matrix ◽  
Experienced Individual

Scrutiny of intermittent leather is accepted through visual analysis on the natural material by an experienced individual based on many parameters which includes surface defects as a parameter. Such results comprising of base color, other than base color, share of regions, share of cutting area, share of cutting value, position wise length and position wise breadth will determine the value of the leather and surprisingly the result will vary form one experienced person to another. Hence, a new method for grouping of intermittent leather is proposed for a better or suitable decision making. Feature extraction technique, Grey Level Co-occurrence Matrix (GLCM) has been implemented to understand the features of color and area by extracting the texture features like Entropy, Energy, Contrast, Variance, Mean, Dissimilarity, Correlation and Homogeneity. A total of 428 intermittent leather imagesare used to understand the classification. The classifiers, Linear Discriminant Analysis (LDA) model and Support Vector Machine (SVM) are used to find out the accuracy. Further, linear discriminant model confirms 92% of accuracy over the support vector machine which is confirms 89.65% of accuracy. The proposed LDA model clearly shows that the approach is successful in classifying the variations among the defects and non-defects in intermittent leather images.

Sign in / Sign up

Export Citation Format

Share Document