Surface Roughness Measurement Method Research Articles

Titaniumdioxide Reinforced Glass-Ionomers with Different Surface Roughness Measurement Methods

Titaniumdioxide Reinforced Glass-Ionomers with Different Surface Roughness Measurement Methods

A visual measurement method for grinding surface roughness combining filter and branch convolution network

ABSTRACT The visual measurement method of grinding surface roughness is mainly predicted by designing image feature indicators. In contrast, the self-extraction method of grinding surface features based on deep learning has problems such as noise, low resolution and poor perception of details. This paper proposes a visual measurement method for grinding surface roughness to address these problems by combining filters and branching convolutional networks. The method adopts a two-branch convolutional neural network, inputs the images using filter denoising and the original grinding workpiece surface images simultaneously, self-extracts roughness correlation features and fuses them to achieve feature enhancement, effectively improves the recognition accuracy and generalisation ability of the model, and verifies the robustness of the model in the case of rusting on the workpiece surface. The experimental results show that the method can effectively solve the problem of the grinding process’s weak and difficult recognition of surface roughness feature information and provide a basis for automated visual online measurement of grinding surface roughness.

A new surface roughness measurement method based on QR-SVM

A new surface roughness measurement method based on QR-SVM

A visual measurement method of grinding surface roughness based on aliasing region index and neural network

Most existing vision-based roughness measurements primarily rely on statistical information from grayscale images or intensity information from color images. However, the structural information of images has not been fully and effectively utilized. To more accurately measure the roughness of grinding surfaces, a visual measurement method of grinding surface roughness based on aliasing region index and neural network is proposed. Firstly, color images of grinding surface are obtained under red and green illumination. Secondly, aliasing regions of red and green images are extracted through fuzzy clustering segmentation and morphological processing. Then the aliasing width and the aliasing dispersion of aliasing region can be calculated as indices for roughness measurement. Thirdly, the relationship model between aliasing region index and grinding surface roughness is constructed using the back propagation (BP) neural network. The results demonstrate that the aliasing dispersion index has a better correlation with grinding surface roughness than the aliasing width index. The method based on the aliasing dispersion index and BP neural network is feasible and accurate for grinding surface roughness measurement.

Measurement methods and fractal dimensions of blast-cleaned steel substrates

ABSTRACT Morphological parameters of steel substrates determine the adhesion of coatings and adhesives. Three different assessment methods (cross section image, contact stylus instrument, and stripe light projection) were applied to blast-cleaned substrates. A total of 12 surface configurations were generated with different abrasive materials. The fractal dimensions were estimated by means of the box-counting method. Design of experiment and ANOVA were applied to statistically analyze the relationships. Factors included abrasive type, surface preparation grade, surface roughness, and surface profile measurement method. Similar relative trends for the fractal dimensions resulted for the three measurement methods. The normalized values of the fractal dimensions were approximately twice as high for the cross section profiles compared to the ones from contact stylus and stripe light projection. Fractal values derived from contact stylus measurements showed the highest repeatability, whereas those derived from cross section images showed the lowest repeatability.

Concrete surface roughness measurement method based on edge detection

Concrete surface roughness measurement method based on edge detection

Research on the Detection and Measurement of Roughness of Dam Concrete Layers Using 3D Laser Scanning Technology

The roughness of the interlayer concrete, which is an important index for the construction of dams, affects the mechanical properties of the interface between new and old concrete. The traditional monitoring method cannot satisfy field measurements. Therefore, this paper proposes a concrete surface roughness measurement method based on 3D laser scanning technology and implements 3D point cloud calculations. Two layers of specimens with same concrete mix proportion were poured 28 days apart, and a splitting tensile strength test of the superimposed specimen was carried out. Four groups of experiments (MR-S, MR-N, MR-15, and MR-35) tested the interlaminar tensile splitting strength of nine 150 mm cubic specimens on the 3rd, 7th, 14th, and 28th days, respectively. Filling volume, mean amplitude, surface area expansion rate, normal angle std. dev., and curvature std. dev. were measured in the study. The results showed that as the surface roughness of the concrete increased, as did the flushing pressure. The splitting strength of the concrete interface after it solidified increased with age. At 28 days, the splitting strength of MR-15 was higher than that of MR-35. The mean amplitude and the normal angle std. dev. of the rough surface could replace the traditional filling volume index. The new 3D point cloud calculation method for calculating concrete surface roughness was proven by experiments to have good performance.

A new grinding surface roughness measurement method based on image quality algorithm and BP neural network

Due to the problem of single factor and no benchmark in the design of image characteristic index, the measurement results of machine vision-based grinding surface roughness measurement are greatly influenced by the light source brightness, and the accuracy is limited. To address this problem, a method based on the combination of a full-reference (FR) image quality algorithm visual saliency-induced index (VSI) and a backward propagation (BP) neural network for grinding surface roughness measurement is proposed. First, the VSI is applied to characterize grinding surface roughness. Its performance is then compared with the mainstream color image-based indices. Meanwhile, the ability of anti-interference to light source is analyzed for the VSI. Furthermore, in order to improve the prediction accuracy, the BP neural network is selected and applied to construct the roughness prediction model. The experimental results show that the VSI has significant advantages in the abilities of measurement accuracy and anti-interference to light source brightness levels. BP can significantly improve the prediction accuracy for VSI.

Change pattern of geomembrane surface roughness for geotextile/textured geomembrane interfaces

Applying textured geomembrane improves the frictional performance of geotextile and geomembrane interfaces. However, very limited research has been conducted to analyze the variation in textured geomembrane roughness during geotextile/geomembrane interface shear processes. In this study, a geomembrane surface roughness measurement method for measuring asperity height data with fixed intervals was presented. Normalized profile length and fractal dimension were used to quantitatively describe the geomembrane surface deformation during the geotextile/textured geomembrane interface shear process. It was found that applying normal stress led to a reduction of the roughness parameters. After the mobilization of the peak shear stress during the shear process, the chosen roughness parameters decreased with the shear displacement. And, increasing the normal stress made the shear-induced reduction of roughness parameters more obvious. The hyperbolic model can be used to describe the quantitative relationship between the geomembrane roughness parameters and the shear displacement. This study can help explain the displacement-softening post-peak behavior of the geotextile/textured geomembrane interfaces.

Evaluation of grinding surface roughness based on gradient similarity and color similarity

Existing machine vision detection methods for surface roughness based on design indices are mainly divided into two categories: grayscale information-based and color information-based. The former loses a large amount of image information when converting the image’s three-dimensional color space to one-dimensional grayscale space. The latter does not fully consider the color information and structural detail changes in images of different rough surfaces. To address the above problems, a visual measurement method of grinding surface roughness based on gradient similarity and color similarity is proposed in this study. This method purposefully uses gradient similarity and color similarity to evaluate the structural differences and color differences between different roughness images, respectively. The comparison experiments with CD, F2, and F5 indices show that the GC index we proposed has a strong correlation with the grinding surface roughness, and its regression fitting prediction model has a high prediction accuracy. In addition, we have discussed the effect of light intensity on the GC index. The experimental result indicates that the correlation between the GC index and roughness is relatively stable under different light intensities.

Research on the applicability of color vision-based roughness inspection method

This study was designed to address the issue that the machine vision-based measurement method of surface roughness based on color information cannot be universally applicable to different machining processes and materials. To this end, the performance of the average color difference (CD) and five typical spectrum indices in the characterization of the surface roughness of two representative texture processes, namely milling and grinding, were explored in the present study. The research results proved that the CD had a stronger correlation with surface roughness and a better robustness to the incident angle than spectrum indices, and the cause for the correlation of index with milling sample being weaker than that with grinding sample was studied from the mechanism. Besides, the correlation between the CD and the surface roughness of different materials was investigated. The results showed that the correlation between roughness and the CD of different materials was stable under different incident angles and ambient light noise. However, the differences in the mathematical relationship models between the indices and the roughness of different materials proved that the materials had a mathematical nature effect on color information detection roughness. In conclusion, this experiment validates the effectiveness of machine vision-based roughness inspection method based on color information and common light sources.

Comparative study of linear and quadratic model equations for prediction and evaluation of surface roughness of a plain-woven fabric

Purpose Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to estimate and evaluate without the complexity and time-consuming experimental procedures. The purpose of this study is to develop and select the best regression model equations for the prediction and evaluation of surface roughness of plain-woven fabrics. Design/methodology/approach In this study, a linear and quadratic regression model was developed for the prediction and evaluation of surface roughness of plain-woven fabrics, and the capability in accuracy and reliability of the two-model equation was determined by the root mean square error (RMSE). The Design-Expert AE11 software was used for developing the two model equations and analysis of variance “ANOVA.” The count and density were used for developing linear model equation one “SMD1” as well as for quadratic model equation two “SMD2.” Findings From results and findings, the effects of count and density and their interactions on the roughness of plain-woven fabric were found statistically significant for both linear and quadratic models at a confidence interval of 95%. The count has a positive correlation with surface roughness, while density has a negative correlation. The correlations revealed that models were strongly correlated at a confidence interval of 95% with adjusted R² of 0.8483 and R² of 0.9079, respectively. The RMSE values of the quadratic model equation and linear model equation were 0.1596 and 0.0747, respectively. Originality/value Thus, the quadratic model equation has better capability accuracy and reliability in predictions and evaluations of surface roughness than a linear model. These models can be used to select a suitable fabric for various end applications, and it was also used for tests and predicts surface roughness of plain-woven fabrics. The regression model helps to reduce the gap between the subjective and objective surface roughness measurement methods.

Surveying the topography and examining the quality of the machined surface of selected hardened steels in the milling process

The article deals with the surface roughness examination of three selected hard materials, that is, K100, 100CrMnMo7, and 16MnCr5, all with a defined hardness of HRC 60. The individual materials were machined using sintered carbide and cermet cutting materials with TiCN and TiN + TiCN + Al2O3 coatings. The research resided in the realization of three experiments. These experiments were carried out by three alternatives differing in the setting of basic cutting parameters. The experimental procedure was realized by the simulation method and direct contact measurement method of surface roughness. From the overall technical evaluation described in the practical part of the article, for the primarily observed parameter, the surface roughness, the least suitable values were obtained in the evaluation of the material from experiment No. 1-K100 with hardness HRC60. The experiments realized and the results achieved show that the machining of tool steels and other superhard materials requires special cutting tools, machines, and the use of progressive technologies, to ensure the production of a dimensionally and qualitatively shaped functional component.

Knowledge-based transfer fuzzy clustering with non-local spatial information for surface roughness measurement

Surface roughness is an important quality indicator of machining processing, which is often needed as a feedback. To meet the requirement of on-line roughness measurement, the machine vision-based measurement method has emerged in recent decades. In this paper, a new surface roughness measurement method is developed. It measures the surface roughness by evaluating the aliasing degree of the images reflected by the machined surface. To analyze the aliasing degree more accurately, a knowledge-based transfer fuzzy clustering with non-local spatial information (KTFCM_NLS) is proposed. The KTFCM_NLS incorporates both the auxiliary knowledge and non-local spatial information into the fuzzy c-means (FCM) algorithm. It can preserve more image details, and is robust to image noise. The results show that the proposed algorithm performs better than other FCM algorithms, and the corresponding index is strongly related to surface roughness. The average relative error of the proposed method is only 5.95% on the testing samples.

Towards intelligent CFRP composite machining: Surface analysis methods and statistical data analysis of machined fibre laminate surfaces

Many carbon fibre reinforced polymer composite parts need to be edged trimmed before use to ensure both geometry and mechanical performance of the part edge matches the design intent. Measurement and control of machining induced surface damage of composite material is key to ensuring the part retains its strength and fatigue properties. Typically, the overall surface roughness of the machined face is taken to be an indicator of the amount of damage to the surface, and it is important that the measurement and prediction of surface roughness is completed reliably. It is known that the surface damage is heavily dependent on the fibre orientation of the composite and cutting tool edge condition. This research has developed a new ply-by-ply surface roughness measurement methods using optical focus variation surface analysis and image segmentation for calculating areal surface roughness parameters of a machined carbon fibre composite laminate. Machining experiments have been completed using a polycrystalline diamond edge trimming tool at increasing levels of cutting edge radius. Optical surface measurement and µ-CT scanning have been used to assess machining induced surface and sub-surface defects on individual fibre orientations. Statistical analysis has been used to assess the significance of machining parameters on Sa (arithmetic mean height of area) and Sv (areal magnitude of maximum valley depth) areal roughness parameters, on both overall roughness and ply-by-ply fibre orientations. Empirical models have been developed to predict surface roughness parameters using statistical methods. It has been shown that cutting edge degradation, fibre orientation and feed rate will significantly affect the cutting mechanism, machining induced surface defects and surface roughness parameters.

Change of a State of the Optical Parts Surfaces made of K8 Glass after Polishing Completion

The work is devoted to studying the polished surface state of K8 optical glass by ellipsometry, IR spectroscopy and surface roughness measurement methods. It is shown that for chemically stable K8 glass the change of the surface state is insignificant, that is caused by its composition.

A novel surface roughness measurement method based on the red and green aliasing effect

Abstract A common requirement in many machine learning algorithms is that the training data is sufficient. However, in the visual measurement of surface roughness, this requirement often can't be meet due to the reason that it is time-consuming and expensive to process and label the training samples. To address this issue, this paper proposes a novel method to establish an advanced roughness predictive model with less standard training samples based on inductive transfer learning. The experimental results show that the proposed method has superior measurement performance, and can maintain the average relative error of 12.57% even when the training data is insufficient. This indicates that the proposed method can provide a new strategy for improving the visual roughness measurement performance.

Surface roughness measurement method based on multi-parameter modeling learning

To improve the accuracy and efficiency of the existing roughness measurement methods, we propose a new surface roughness measurement technique based on multi-parameter modelling learning. First, multi-feature descriptor is constructed through speckle feature, grey feature and Tamura texture feature. Then, an identification reasoning method based on ACC-random forest was proposed to determine the work-piece classification. Finally, to realize surface roughness measurement efficiently, a multi-parameter learning model is established. Through establishment and optimization of multi-parameter surface roughness modeling, the value of surface roughness can be measured accurately. Thus, not only the class of work-piece be classified, also the value of surface roughness can be measured. Our proposed method breaks through the limitations of existing methods, which are based on several roughness measurement models for different classes of work-pieces. The experimental results indicate that our proposed method significantly outperform the state-of-the-art methods in terms of classification accuracy and measurement error rate.

Nondestructive, fast, and cost-effective image processing method for roughness measurement of randomly rough metallic surfaces.

In recent years, various surface roughness measurement methods have been proposed as alternatives to the commonly used stylus profilometry, which is a low-speed, destructive, expensive but precise method. In this study, a novel method, called "image profilometry," has been introduced for nondestructive, fast, and low-cost surface roughness measurement of randomly rough metallic samples based on image processing and machine vision. The impacts of influential parameters such as image resolution and filtering approach for elimination of the long wavelength surface undulations on the accuracy of the image profilometry results have been comprehensively investigated. Ten surface roughness parameters were measured for the samples using both the stylus and image profilometry. Based on the results, the best image resolution was 800dpi, and the most practical filtering method was Gaussian convolution+cutoff. In these conditions, the best and worst correlation coefficients (R2) between the stylus and image profilometry results were 0.9892 and 0.9313, respectively. Our results indicated that the image profilometry predicted the stylus profilometry results with high accuracy. Consequently, it could be a viable alternative to the stylus profilometry, particularly in online applications.

Vision system in quality control automation

Measurement of surface roughness is one of the quality control processes, usually carried out off line. Contact type surface roughness measurement method is commonly used in quality control. The processes consume lot of time with human interaction. In order to reduce or to eliminate non value added time, effective quality inspection tool and automation of the processes has to be utilized. An attempt has been made to automate the process with integration of vision camera in capturing the image of the component surface. The image process technique has the advantage of analyzing the single captured image for multiple area measurement. Hence, the in-line quality control of each component surface roughness measurement is ensured. The automation process involves component movement, image capturing, image processing, and decision making, using sensors, actuators and microcontroller. The proposed in-line quality control of surface roughness with vision system has been successfully developed. The designed automated system has fulfilled the objectives in respect of the scope of the present work.