Study on Brittle Graphite Surface Roughness Detection Based on Gray-Level Co-occurrence Matrix

Zhou, Li; Liu, Hanzhang; Zhuang, Xiaopeng; Liu, Dawei

doi:10.1109/icmcce.2018.00062

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…The spatial correlation among the pixels on the surface image is taken into account by this statistical technique. Surface roughness is collected by investigating the relationships between average surface roughness (Ra) and the GLCM features of the surface image [119,120]. The procedure of the computer vision system for measuring the surface roughness [121], is shown in Fig.…”

Section: Computer Vision Systemmentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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Section: Computer Vision Systemmentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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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%

“…Many studies have been done to solve the image analysis-based SR estimation. 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.…”

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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“…The co-occurrence matrix based features are employed in [5] for surface roughness detection in graphite. It uses the features from the co-occurrence matrix such as secondary moment and entropy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Quality Control System by Machine Learning for Surface Defects

Alam¹,

Mohanan²

2021

IJASIS

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Quality control plays a crucial role to meet the high and accurate quality of production in many manufacturing industries. The high quality products may become unreliable due to surface defects. Generally, quality control is more important in automotive industry such as in the field of carbody parts manufacturing. The exterior appearance of the car body should be smooth surfaces and edges with flawless nature. In order to build such flawless body parts, surface defect detection system is taken into account. In this paper, an Automated Defect Detection (ADD) system is presented. The design of the ADD system consists of two steps. The first step is considered as a classification system where the given image is classified into defected or nondefected using Gabor expansion with Principal Component Analysis (PCA). The next step is segmentation where the region of defect is identified using local thresholding. The evaluation is performed on raw alloy steel surface and machined surfaces of steel and cast iron. Results prove that the ADD system classify the input image into defect/no defect with 100% accuracy by a simple nearest neighbor classifier and with 94.5% detection accuracy for the segmentation system.

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Study on Brittle Graphite Surface Roughness Detection Based on Gray-Level Co-occurrence Matrix

Cited by 9 publications

References 4 publications

Surface Characteristics Measurement Using Computer Vision: A Review

Surface Characteristics Measurement Using Computer Vision: A Review

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

An Efficient Quality Control System by Machine Learning for Surface Defects

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