Calculating Surface Roughness for a Large Scale SEM Images by Mean of Image Processing

Hameed, Noor; Ali, Inbethaq M.; Hassun, Hanan K.

doi:10.1016/j.egypro.2018.11.167

Cited by 19 publications

(8 citation statements)

References 6 publications

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“…Hameed et al [249] proposed that the findings show that employing AFM measurements, the SEM images quantify the ruggedness parameters that are accurate on a wide area and provide an acceptable computational ruggedness parameter compared with AFM measurements. The shift of the SEM image acquisition scale affects little of the contrast surface heights (Sp, Spm, Sv, Svm, St, and Sk), and the samples may relate to the homogeneity of the surface.…”

Section: Surface Characteristics Measurement Using Atomic Force Micro...mentioning

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: Surface Characteristics Measurement Using Atomic Force Micro...mentioning

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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“…As for the cleaning effects in rank 2 and rank 3, the 3-order polynomial function can get the best prediction result. Equations (17) and (18) show the computational methods of the linear function and the 3-order polynomial function. Other prediction methods, such as the Support Vector Regression (SVR) [36] and the n-order (n =3) polynomial functions etc., are also considered here.…”

Section: Elaborated Esimation Of Srmentioning

confidence: 99%

“…In [16], the RGBD camera was used for materials recognition and SR estimation. After the survey of lots of related works, it can be found the current researches can realize the SR estimation by using the observed image directly; however, it will be great useful if the SR can be controlled and estimated [17] before laser cleaning. That means even a corrosion layer covers the substrate we hope to control and estimate SR of substrate before the exfoliation of corrosion layer.…”

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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“…Tool wear measurement, surface quality control, workpiece surface texture measurements, and other machining processes can all benefit from digital image processing (DIP) with machine vision. Image processing techniques were used to determine the surface roughness of large-scale SEM images [5,6].…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

Kumar¹,

Thenmozhi²,

Bommi

et al. 2022

Journal of Nanomaterials

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Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.

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Calculating Surface Roughness for a Large Scale SEM Images by Mean of Image Processing

Cited by 19 publications

References 6 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

Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

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