Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

Ge, Jianyue; Liu, Haoting; Shen, Yang; Lan, Jinhui

doi:10.3390/photonics9080510

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…With the exception of the spot irradiation and scanning speed, the surface laser cleaning method was similar to previous research [48,49], and the cleaning parameters were optimized and are provided in Table 3. To clean the material more rapidly, the scanning speed was set up higher than previous works [50][51][52]. After the experiment, the surface cleaning precision of the samples was observed and measured via SEM and an optical microscope.…”

Section: Test Materials Laser Cleaning Parameters and Methodsmentioning

confidence: 99%

Study on the Surface Morphology of Micro-Particles and the Oxide Layer on Silicon Carbide Crystal Using Nanosecond Green Laser Cleaning Assisted with Airflow

Xiao

Zhang

et al. 2022

Crystals

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With a focus on the particle pollutants on the surface of silicon carbide crystal materials, this paper establishes a laser cleaning model for the fine particles found in silicon carbide crystal materials and proposes a new nanosecond green laser cleaning method assisted by airflow, which can effectively remove microparticles and the oxide layer on the substrate surface. Abaqus software and ANSYS Fluent software were used to simulate changes in the cleaning temperature field and the distribution of particles and dust during cleaning simulation, respectively. Based on the experimental research, and by using a nanosecond green laser to produce a wavelength of 532 nm, the direct irradiation of a nanosecond green laser on the surface of the element, and the particle contaminants on the surface of the silicon carbide material, optimized the process parameters to achieve a better cleaning efficiency. A green laser was used as a light source to conduct experiments to control the wind force of the gas chamber. The influence of the laser energy, scanning speed, and other parameters on the final cleaning efficiency was studied. The parameters of the silicon carbide before and after cleaning were characterized. The research shows that laser cleaning assisted with airflow is an efficient cleaning method that can be used to clean microparticles without damaging silicon carbide crystal substrate and to reduce the surface roughness of silicon carbide material from 1.63 to 0.34 μm, with an airflow of 0.2 Mpa.

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Section: Test Materials Laser Cleaning Parameters and Methodsmentioning

confidence: 99%

Study on the Surface Morphology of Micro-Particles and the Oxide Layer on Silicon Carbide Crystal Using Nanosecond Green Laser Cleaning Assisted with Airflow

Xiao

Zhang

et al. 2022

Crystals

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“…The section presents a modern methodology for MRI image diagnosis of AD dataset by FFNN based on extracting features from GoogLeNet and Dense-121 models separately and then combining them with features of DWT, LBP and GLCM methods (handcrafted features) [44]. This technique consists of two systems shown in Figure 6.…”

Section: Ffnn Network According To Fusing the Features Of Cnn Models ...mentioning

confidence: 99%

Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features

et al. 2023

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Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer’s is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer’s and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer’s, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. This study contributed to the development of three methodologies, each with two systems, with all systems aimed at achieving satisfactory accuracy for the detection of AD and predicting the stages of its progression. The first methodology is by Feed Forward Neural Network (FFNN) with the features of GoogLeNet and DenseNet-121 models separately. The second methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models before and after high-dimensionality reduction of features using the Principal Component Analysis (PCA) algorithm. The third methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models separately and features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods called handcrafted features. All systems yielded super results in detecting AD and predicting the stages of its progression. With the combined features of the DenseNet-121 and handcrafted, the FFNN achieved an accuracy of 99.7%, sensitivity of 99.64%, AUC of 99.56%, precision of 99.63%, and a specificity of 99.67%.

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“…Proc. 2023, 59, 92 3 of 7 determining surface texture [8,14]. Thus, the study aims to explore the applicability of Tamura features in identifying surface texture.…”

Section: Feature Definitionmentioning

confidence: 99%

“…Various methods have been used for surface texture identification by earlier researchers [11][12][13]. However, the Tamura features are considered one of the essential features in identifying patterns, which is not studied in determining surface texture [8,14]. Thus, the study aims to explore the applicability of Tamura features in identifying surface texture.…”

Section: Feature Definitionmentioning

confidence: 99%

Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques

Kamath,

Vijay,

Prasad

et al. 2023

RAiSE-2023

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Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

Cited by 8 publications

References 49 publications

Study on the Surface Morphology of Micro-Particles and the Oxide Layer on Silicon Carbide Crystal Using Nanosecond Green Laser Cleaning Assisted with Airflow

Study on the Surface Morphology of Micro-Particles and the Oxide Layer on Silicon Carbide Crystal Using Nanosecond Green Laser Cleaning Assisted with Airflow

Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features

Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques

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