Application of image edge detection methods for precise estimation of the standard surface roughness parameters: Polypropylene/ethylene-propylene-diene-monomer blend as a case study

Ghodrati, Sajjad; Mohseni, Mohsen; Kandi, Saeideh Gorji

doi:10.1016/j.measurement.2019.02.033

Cited by 25 publications

(15 citation statements)

References 44 publications

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“…9), a greater amount of energy was dissipated in their bulks. Thus, greater force was needed to separate the coating from the substrate (Ghodrati et al , 2015). In contrast, in the EP-OMMT3 sample, due to its high level of stiffness ( i.e.…”

Section: Resultsmentioning

confidence: 99%

“…The samples were scratched according to ASTM D1654 and then placed in the salt fog chamber for about 168 h. After removing the samples from the salt spray test, their images were recorded with a digital scanner (Epson V550, Japan) at 600 dots per inch (dpi) resolution. Because in recent years image-processing methods have shown promising potential in quantitative characterization of the materials and their behaviour in various media (Ghodrati et al, 2017, 2018), an image-processing method was employed for non-subjective evaluation of the salt spray results. Using the Canny edge detection method as an image processing tool, the corroded area in the samples was evaluated quantitatively.…”

Section: Methodsmentioning

confidence: 99%

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Modification and characterization of an Iranian montmorillonite as a corrosion/mechanical promoter for epoxy coatings

2018

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Iranian montmorillonite (MMT) was modified with hexadecyltrimethylammonium bromide (HDTB) and used as a nano-additive for the enhancement of the mechanical and anticorrosion properties of an epoxy coating. Infrared (IR) spectroscopy, thermo-gravimetric analysis (TGA), transmission electron microscopy (TEM) and X-ray diffraction (XRD) analysis indicated that HDTB reacted with MMT (with 97% yield) and increased the d001 spacing of MMT from 1.27 to 3.92 nm. Some 1, 3 and 5 wt.% of organically modified montmorillonite (OMMT) were added to the epoxy coating formulation. XRD and TEM/image processing analysis showed that the extent of intercalation/exfoliation of OMMT was substantially greater than that of MMT. Dynamic mechanical analysis showed that the glass transition temperature and storage modulus increased significantly with the addition of OMMT. Electrochemical impedance spectroscopy (EIS) and salt spray/image processing results revealed that addition of OMMT to the epoxy coating significantly increased the corrosion resistance of the coating compared to neat and MMT-containing epoxy coatings. The optimum concentration of OMMT was 3 wt.%. At this concentration, the mechanical and anticorrosion properties were greatly superior to those of neat and MMT-containing epoxy coatings.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modification and characterization of an Iranian montmorillonite as a corrosion/mechanical promoter for epoxy coatings

2018

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“…Edges consist of meaningful features and contain a significant information. To differentiate between significant and insignificant information, help from edge detectors is very important as it can aid in reducing and filtering out the features that may be regarded as less relevant [14]. In edge detection approach, the method of edge detection is classified into two groups which are gradientbased and Laplacian-based.…”

Section: Implementation Of Gradient-based Edge Detectionmentioning

confidence: 99%

Application of Gradient-based Edge Detection on Defined Surface Feature Boundary

Razali¹,

Ismail²

2021

AETiC

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In surface topography, the detection of edges or surface feature boundary should be made prior to the characterization process to ensure precise measurement of the surface feature. However, problem arises when the existing edge detection from the image processing area is being directly adopted on the surface without firstly defining the exact location of edges. Thus, an approach has been carried out with the aim of developing the definition of edges to identify its exact location. The process of developing the definition of edges is conducted based on theoretical understanding and demonstration on the simulated surface topography data with the help of MATLAB software. Gradient-based edge detection operators are applied to see their performance on the defined surface feature boundary. Overall findings of this study showed that there are slight discrepancies of the edge position signified by the gradient-based operators to manually-defined edges. Despite of such outcome, it is then recommended to use either the Sobel operator or the Prewitt operator as a tool to signify the edge before proceeding with characterization process in future study due to their stability and clarity in producing the surface gradient function.

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“…First, regarding the cleaning effect in rank 1, both the Tamura coarseness and the convex region feature are used. The computational method of Tamura coarseness is shown in (14) and (15). Second, as for the cleaning effect in rank 2, the average of the GLCM correlation M GLCM _COR is computed.…”

Section: Elaborated Esimation Of Srmentioning

confidence: 99%

“…In [14], the Gray-Level Co-occurrence Matrix (GLCM) was employed to estimate the SR of brittle graphite. In [15], five image edge detection methods were considered to evaluate SR. The Laplacian of Gaussian method was found to be the best solution.…”

Section: Introductionmentioning

confidence: 99%

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Liu

Yang

et al. 2020

IEEE Access

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An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user's demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

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Application of image edge detection methods for precise estimation of the standard surface roughness parameters: Polypropylene/ethylene-propylene-diene-monomer blend as a case study

Cited by 25 publications

References 44 publications

Modification and characterization of an Iranian montmorillonite as a corrosion/mechanical promoter for epoxy coatings

Modification and characterization of an Iranian montmorillonite as a corrosion/mechanical promoter for epoxy coatings

Application of Gradient-based Edge Detection on Defined Surface Feature Boundary

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

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