A new surface roughness measurement method based on a color distribution statistical matrix

Liu, Jian; Lu, Enhui; Yi, Huaian; Wang, Menghui; Peng, Ao

doi:10.1016/j.measurement.2017.02.036

Cited by 80 publications

(39 citation statements)

References 25 publications

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“…Based on this theory, the feature index correlated with roughness can be designed by evaluating the aliasing effect. However, in the previous work, researches only count the number of pixels with the same red and green brightness values [21], or calculate the absolute difference between the brightness values of red and green components [7] to characterize the aliasing images. These approaches are obviously not comprehensive and reasonable, since the correlation between pixels and the spatial information of pixels are ignored.…”

Section: Fuzzy C-means Clustering Algorithm With Non-neighborhood mentioning

confidence: 99%

“…However, the sensitivity of these features to the roughness parameter may be affected, because the gray scale image lost the color information and the features mentioned above are all extracted from these degraded images. To address this problem, the color information has been studied to analyze the machined surface images [7,17,21]. Liu et al proposed a color difference index to measure the roughness of grinding surface.…”

Section: Introductionmentioning

confidence: 99%

“…As color images have more detail information, more accurate features can be extracted from these images to identify targets [22,23], but in previous works [7,17,21], only simple color indexes are proposed to characterize the surface images, which ignore the correlation between pixels and the spatial information of pixels. Moreover, appropriate image processing algorithm can be applied to fully characterizing the surface images and accurately measuring the surface roughness [11,24,25].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Zhang

Liu

Chen

et al. 2019

Sensors

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Due to the limitation of the fixed structures of neighborhood windows, the quality of spatial information obtained from the neighborhood pixels may be affected by noise. In order to compensate this drawback, a robust fuzzy c-means clustering with non-neighborhood spatial information (FCM_NNS) is presented. Through incorporating non-neighborhood spatial information, the robustness performance of the proposed FCM_NNS with respect to the noise can be significantly improved. The results indicate that FCM_NNS is very effective and robust to noisy aliasing images. Moreover, the comparison of other seven roughness indexes indicates that the proposed FCM_NNS-based F index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (R2 = 0.9327 for thirty grinding samples).

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Section: Fuzzy C-means Clustering Algorithm With Non-neighborhood mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Zhang

Liu

Chen

et al. 2019

Sensors

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“…It also significantly impacts the life, reliability and the operation of the technical equipment [16,17]. Liu, Lu, Yi, Wang and Ao [18] proposed a similar surface roughness measurement method to prove that the measurement process is largely influenced by the source of the laser light containing two colors, red and green, creating a relationship model between the overlapping index and roughness.…”

Section: Introductionmentioning

confidence: 99%

Application of Laser Profilometry to Evaluation of the Surface of the Workpiece Machined by Abrasive Waterjet Technology

et al. 2019

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The paper is an evaluation of the surface roughness of various materials produced by water jet cutting (AWJ, abrasive water jet). A 3D laser profilometer developed at the Department of Design and Technical Systems Monitoring at our University was used in roughness measurement. To verify the values measured by the laser profilometer, another measurement was performed using a 2D contact roughness meter. The tests were done on aluminum and stainless-steel materials, respectively. Six samples were produced; three made of stainless steel and three made of aluminum. All samples were produced at a different feed rate of the cutting head. This was adapted to the different roughness required, per the manufacturer’s material data sheets. Varying rates of separation translated into different qualities of the surfaces under evaluation. The evaluated roughness parameters were Ra and Rz. Dependencies were plotted in the chart based on the values measured, which were then compared and evaluated.

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“…Krolczyk et al researched the surface morphology of duplex stainless steel using the method Power Spectral Density for dry and MQL/MQCL (Minimum Quantity Lubrication) turning process [15], Krolczyk concluded in the following study that the application of the MQCL method can lead to the reduction of 3D surface roughness parameters compared to values reached after dry machining, and surfaces machined with the use of the MQCL technique can be characterized by a high wear resistance [16]. Liu et al proposed a ground surface roughness measurement method to address current problems in the use of machine vision technology to measure roughness, and the results demonstrated that the surface roughness measurement method had relatively high accuracy and a relatively wide measurement range [17]. Chen et al demonstrated a Nested-ANN (Artificial Neural Network) model predicting surface roughness, and pointed out that the nested-ANN uses less input variables to obtain superior prediction accuracy than other models [18].…”

Section: Introductionmentioning

confidence: 99%

Study on Surface Roughness of Gcr15 Machined by Micro-Texture PCBN Tools

Pan

et al. 2018

Machines

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This paper applies micro textures to the rake face of PCBN (Polycrystalline Cubic Boron Nitride) tools, including three types of micro textures that are microgroove textures vertical to the cutting edge, microgroove textures parallel to the cutting edge, and microhole textures. In this paper, the effects of different cutting speeds on the surface quality of hardened bearing steel GCr15 by dry turning with non-texture PCBN tools and micro-texture PCBN tools are studied, and the surface roughness values obtained by different micro textures were compared and analyzed. The results showed that, compared to that of non-texture tools, the influence degree of the micro-texture tools on the machined surface roughness was different. The microhole texture and vertical microgroove texture were able to effectively reduce the surface roughness of the workpiece, and microhole texture had the best effective influence on surface roughness, but the parallel microgroove texture increased surface roughness. The influence of cutting speeds on surface roughness was different due to different types of micro textures. The influence of micro textures on surface roughness has huge potential for tool applications.

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A new surface roughness measurement method based on a color distribution statistical matrix

Cited by 80 publications

References 25 publications

Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Application of Laser Profilometry to Evaluation of the Surface of the Workpiece Machined by Abrasive Waterjet Technology

Study on Surface Roughness of Gcr15 Machined by Micro-Texture PCBN Tools

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