An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

Souza, Amanda Campos; Silva, Gulliver Catão; Caldeira, Lecino; Nogueira, Fernando Marques de Almeida; Lagares, Moisés Luiz; Aguiar, Eduardo Pestana de

doi:10.1177/0954406220942268

Cited by 5 publications

(3 citation statements)

References 53 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The acquired images are a useful resource for computational intelligence research teams, e.g. [2] , by offering images for handling as filtering, feature extraction, training, validation and testing in pattern recognition and machine learning techniques.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Fusion zone microstructure image dataset of the flux-cored and shielded metal arc welding processes

2020

Self Cite

View full text Add to dashboard Cite

This paper presents high quality (2048 × 1532 pixels) Light Microscope steel images sampled from the welding fusion zone. The microstructure images were acquired from the Design of Experiments (2 2 full factorial design) planned to compare two different arc welding processes at two different arc welding energies [1]. The 400 raw images appear as they were captured by the microscope and they are categorized into four groups: that acquired from the Flux Cored Arc Welding process and that acquired from the Shielded Metal Arc Welding process; both of them run for high and low levels of arc energy. For the Flux Cored Arc Welding process, ASME SFA 5.20 E71T-5C(M) tubular wire was used, with a nominal diameter of 1.2 mm. For the Shielded Metal Arc Welding process, AWS E7018 coated electrodes were used, with nominal diameters of 3.25 mm (for the low energy level) and 5.00 mm (for the high energy level). The deposition of the beads was run on AISI 1010 steel plates in the flat position (bead-on-plate). Different proportions of primary grain boundary ferrite; polygonal ferrite; acicular ferrite; nonaligned side-plate ferrite and aligned side-plate ferrite can be observed in each image. This image dataset is ready to visual and automatic microstructure recognition and quantification. It can be a useful resource for computational intelligence research teams, e.g. [2], by offering images for handling as filtering, feature extraction, training, validation and testing in pattern recognition and machine learning techniques.

show abstract

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Fusion zone microstructure image dataset of the flux-cored and shielded metal arc welding processes

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…With this method, GLCM has been used in various studies in recent years. The idea of GLCM is to calculate the phantom θ of two pixels (P i , P j ) separated by a distance d and characterized by a direction (Strzelecki, 1996;Dong et al, 2019;Souza et al, 2020). The matrix C d,θ (i, j) is used for evaluating the joint probability of every two pixels.…”

Section: Feature Extraction (Glcm)mentioning

confidence: 99%

Detection of oil spills based on gray level co-occurrence matrix and support vector machine

Yu²,

Xu³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Accurate reconnaissance of Marine oil spill is very important for emergency management of Marine oil spill accidents. Unmanned aerial vehicles (UAV) is a suitable carrier for offshore oil spill reconnaissance because of its fast deployment speed and low cost. Aiming at the identification accuracy of small oil spill accident in offshore port area and the problem of day and night reconnaissance, this study takes thermal infrared remote sensing images of oil leakage captured by UAV as the research object and proposes an oil spill detection method based on a Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) method. Firstly, the extraction steps of image GLCM feature and the basic principle of SVM classification are studied. Then, the thermal infrared image data collected by UAV is preprocessed, including image filtering, clipping and rotation, and the sample database is generated. Subsequently, GLCM features of the samples were extracted, and the energy and correlation in GLCM were selected as classification features and sent to the SVM classifier to complete the oil spill detection of real-time thermal infrared images. The experimental results show that, compared with Classification and Regression Tree algorithm (CART) and Random Forests of Decision Trees (RF) algorithm, the detection accuracy of the method proposed in this paper reaches 95%, which were 10 and 2 percentage points higher than them respectively. The proposed method in this paper has fast recognition speed and high accuracy, and can provide all-weather recognition of oil spills for the detection of small oil spills in the offshore port area.

show abstract

“…In the wavelet analysis-based ceramic surface image texture feature analysis method, the image quality is critical to the impact of the detection results, but the actual detection process is inevitable noise interference, including material texture noise interference and various types of pseudo-crack noise interference on the surface of the ring, where the material texture brings random noise interference to the detection process, and various types of pseudo-crack features are very similar to the crack features on the image, which is very easy to cause misidentification of crack detection. To cope with the material texture noise interference, the material texture noise is first analyzed, and the filtering algorithm is studied based on the texture noise analysis, to remove the material texture noise interference while preserving the crack features [7]. Chapter 1 is the introduction, which mainly discusses the background and significance of this research and also explains the research framework of this paper.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Analysis-Based Texture Analysis of Ceramic Surface Images

Liu

Zhou

Yang

2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

This paper is conducted to explore a new characterization method as a supplement to the traditional roughness characterization. The main research includes the extraction and evaluation of damage features of ceramic surface morphology by applying wavelet methods, the extraction of damage features in surface contours by using wavelet analysis, and the quantitative evaluation of damage degree by using damage rate and damage mean spacing. By comparing various fractal dimension calculation methods, a fractal dimension method suitable for calculating the ceramic surface was selected, and the fractal method was used to describe the ceramic surface topography as a whole. By comparing different methods of calculating the fractal dimension and further verifying them with the measured three-dimensional morphology, it is found that the vibrational method is more suitable for calculating the fractal dimension of ceramic surface, and its calculation accuracy is investigated, and the results show that the method is a reliable one. Based on the fractal theory, a mathematical model of surface wear and surface sealing was established. Further study of the model shows that the surface with a large fractal dimension has a good sealing effect; the surface corresponding to the best fractal dimension is the most resistant to wear. The fractal method can characterize the complexity of the surface profile as a whole. The wavelet method can describe the ceramic surface profile from a local perspective, and the combination of the two methods can characterize the ceramic surface well. Finally, the experimental device of the ceramic surface defect detection system is constructed, and the joint debugging of hardware and software is completed. Under different light source intensities, ceramic image samples are collected, and the accuracy detection experiments of sample defective edges are conducted, and the results show that the light source has a small impact on the accuracy of ceramic defective edge detection. The results show that the light source has more influence on the accuracy of scratch detection. The results show that the system constructed in this thesis has good applicability for different ceramic sample detection.

show abstract

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

Cited by 5 publications

References 53 publications

Fusion zone microstructure image dataset of the flux-cored and shielded metal arc welding processes

Fusion zone microstructure image dataset of the flux-cored and shielded metal arc welding processes

Detection of oil spills based on gray level co-occurrence matrix and support vector machine

Wavelet Analysis-Based Texture Analysis of Ceramic Surface Images

Contact Info

Product

Resources

About