Any defects pressed into the surface will be revealed if the surface is electropolished. Generally, the higher the level of purity demanded in the product, the finer the surface finish that will be required in the manufacturing equipment.
As an example, 2B finish is used in baking equipment, food processing, tanks and vessels, pharmaceutical equipment and vacuum drum dryers.
It is considered as smooth or smoother than a polished 4 finish and both are acceptable for meeting USDA standards. The Ra for a 2B finish is typically 0. There are many other finishes available of course, but for bio-pharmaceutical use injectables, otic solutions 0. Powder and tablet manufacturers may be able to use a slightly rougher surface of around 0. The surface finish of a vessel, as well as its Ra, determine what product can be produced within it, and as stated above, increasing levels of purity require increasingly finer surface finishes with lower Ra numbers.
Each industry has specific finish standards that must be met. Sanitary food grade finishes generally fall in the 0. This range eliminates places where bacteria or other contaminates can gain a foothold. The Ra can be lowered by employing a combination of chemicals and electricity to carefully dissolve the surface of the steel. This process is known as electropolishing. The results makes the valleys much shallower by comparison. Electropolishing is not the correct solution for heavily damaged surfaces such as caused by physical impact, welds or chloride micropitting.
In those cases, mechanical polishing such as sanding or grinding may need to be employed to reduce the Ra to near the desired range. Once that is accomplished, then electropolishing is performed. While electropolishing delivers an overall smoother surface, it also removes any embedded debris such as abrasive dust or metal fines that may have been burnished into the surface.
Ultimately, the thickness of the stainless steel also plays a factor in both Ra and electropolishing as the thicker metal is capable of withstanding more processing to achieve better smoothness. The industry leader in passivation, high purity and precision chemical cleaning. Facebook Twitter LinkedIn. How Smooth Is Smooth? Why Does Ra Matter? ISO No. A factor of 1.
As surface roughness decreases from 3. This is reflected in the table above. Examples of Different Surface Finishes Generally, the higher the level of purity demanded in the product, the finer the surface finish that will be required in the manufacturing equipment.
Other finishes and their roughness averages for comparison: A 1 finish, sometimes called Hot Rolled, Annealed, and Pickled HRAP , is plate material as it emerges from the mill. For example, there are regions of a block that have greater roughness and others with lower roughness; it is possible to identify regions where the blocks could favor adherence.
In order to evaluate surface roughness coefficients, are proposed the analysis of both the detail levels of the quadtree divisions and the comparison between surfaces by analyzing the developed evaluation metrics signature, histogram, and roughness graph. The first form of evaluation proposed is the hierarchical and local analysis of the roughness coefficients.
In this evaluation method, it is possible to analyze and compare surface signatures at different levels of detail. The higher the level of division of the quadtree evaluated, the greater the level of precision of this evaluation, precisely because the previous levels are of global or average values in relation to a region.
It should also be considered that in the initial level of the quadtree root level the calculated coefficient is the global value of the surface, that is, the same type of result as the works presented in 6 , Figure 11 presents a comparison at the initial levels of the quadtrees of two surfaces sampled for the tests performed.
In the example, the coefficients in the lower level of detail of the code levels 0, 1, and 2 present very similar results, owing to the average values of R a. This is noticeable because both the R a in the first column and the values R a min and R a max in the second and third columns of the table are very similar even the images are very similar.
However, in the third level of division level 3 , the difference between the surfaces is better perceived. The images have a larger difference and the R a min and R a max have a greater difference compared to the results of previous levels.
The engineering professional, however, can use the level of subdivision that best fits his purpose, because it is possible to search for a pattern of similarity among blocks or to analyze their differences in more detail.
The comparison of results obtained at different temperatures indicates to the engineering professional a metric to determine which process to adopt, according to the desired roughness level. Table 1 presents the results R a avg , R a min , R a max e R a adv , obtained by comparing coefficients on all surfaces of each block and also on all surfaces of all.
The other form of analysis used in this work is the comparison of data through the newly proposed analysis tools. Combining the use of the three tools, several behaviors, patterns, and analyses of the sampled surfaces can be inferred.
Figure 12 presents the results obtained in the tests performed for the model validation. For computing the signatures, the minimum R a min , maximum R a max , and average R a avg values were computed on all the sampled surfaces. The roughness signature assists in the evaluation of roughness and interface adhesion in different temperature samples, because, notably see Fig.
As shown in Fig. This is perceived by the color variation of the signature. However, other behaviors can also be identified. It is verified that in this region of the blocks, the coefficients show generally low variation; they have values near or below the average roughness R a avg. This behavior is highlighted by the coefficient graph. Another tool used in the evaluation of results, the histogram, allows us to compare the variation of roughness values between blocks of different firing temperatures.
Finally, the proposed tools allow a greater variation of the evaluation criteria of surface roughness in relation to the quantitative form presented in the reference works 6 , 11 and the subjective methods of 15 , 16 , 17 , 18 , 21 , As it can be verified in the presented results, it is possible to analyze in several levels of detail, allowing comparisons and assumptions that are not easily determined by the simple analysis of global coefficients.
The evaluation of the surface quality of materials through the measurement and analysis of roughness parameters is known 6 , 11 , 15 to be an effective way of determining the quality or standardization of surfaces. In addition, the possibility of computing the results in a localized way and through the spatial division and hierarchical organization and subdivision of these locations provides civil engineering professionals a more accurate control tool for the comparison and evaluation of surface quality.
For example, it is possible to submit several surface samples of the same material and to check the patterns of roughness of the samples, as well as to verify and evaluate the distortions due to the way the piece was constructed or environmental factors such as temperature, pressure, and friction. The proposed tools are effective for the analysis and evaluation of roughness following the concepts defined in the reference works 6 , 11 , and bring a new and advantageous perspective on the analysis of surface roughness, as they allow a localized and detailed assessment of roughness coefficients of surfaces and at the same time facilitate comparative analysis among different sample surfaces.
Although this work presents a method for surface roughness computation applied to the civil engineering context, it can be successfully used in other contexts as well. For the study of rock mass formations in geomechanics and geodynamics of rock masses, the roughness analysis is important to determine shear strength, deformation and seepage behaviors of rock surfaces discontinuities. Several works such as 19 , 36 , 37 point out the difficulty of data analysis by the traditional method and the tendency of using 3D point clouds to calculate surface roughness with efficiency and precision.
As described in 19 , 36 , 37 the roughness is a part of the calculation performed for shape measurement of a surface profile. This parameter is calculated based on the surface geometry and basically is a measure related to the distance of the point to the fitting plane of the surface, similar to the R a computed in this work. Poropat 36 further describes that roughness can affect shear strength at various scales, both at waviness and at micro-roughness levels. That the characterization of roughness must be understood and is associated with the scale.
The proposed method in this work can be used for multi-scale roughness computation, as it enables the analysis of roughness at various levels of hierarchical representation. In addition, the proposed roughness analysis tools help in understanding the roughness patterns, quantitatively and visually indicating texture variation behavior along the surface.
Finally, there are still several points that can be explored to try to improve the general evaluation of surfaces, such as data acquisition by photogrammetry and the evaluation of the area of the rough part, not just the height, this would indicate with more precision the adhesion that each surface can allow.
Thamboo, J. Characterisation of thin layer polymer cement mortared concrete masonry bond. Article Google Scholar.
Venkatarama Reddy, B. Enhancing bond strength and characteristics of soil-cement block masonry. Taha, M. The use of pozzolans to improve bond and bond strength. In 9 th Canadian masonry symposium. Myshkin, N. Simulation of real contact in tribology. Marshall, S. A review of adhesion science. Santos, P. A state-of-the-art review on roughness quantification methods for concrete surfaces.
Perez, F. Combination of mechanical and optical profilometry techniques for concrete surface roughness characterisation. Klingvall, E. The effect of ebm process parameters upon surface roughness. Rapid Prototyp. Garbacz, A. Characterization of concrete surface roughness and its relation to adhesion in repair systems. Evaluation of the height 3d roughness parameters of concrete substrate and the adhesion to epoxy resin.
Moreau, N. Miro, M. Grigoriadis, K. Use of laser interferometry for measuring concrete substrate roughness in patch repairs. Stolz, C. Berger, M. State of the art in surface reconstruction from point clouds. In Eurographics star reports , vol. Kazhdan, M.
Poisson surface reconstruction. Screened poisson surface reconstruction. ACM Transactions on Graph. ToG 32 , 29 Taubin, G. Smooth signed distance surface reconstruction and applications. Pattern Recognition, Image Analysis, Comput. Vision, Appl. Mah, J. Rock Mech. Lee, C. Mesh saliency. Schall, O. Surface from scattered points. Nan, L. Polyfit: Polygonal surface reconstruction from point clouds. Wang, K. A fast roughness-based approach to the assessment of 3D mesh visual quality.
Labatut, P. Efficient multi-view reconstruction of large-scale scenes using interest points, Delaunay triangulation and graph cuts. Zhou, Y.
Detail preserved surface reconstruction from point cloud. Wang, W. Surface reconstruction from unoriented point clouds by a new triangle selection strategy. Bolitho, M. Multilevel streaming for out-of-core surface reconstruction. In Symposium on geometry processing , 69—78 Citeseer, Parallel poisson surface reconstruction. In International symposium on visual computing, — Springer, Wang, R.
Lidar point clouds to 3-d urban models: a review. IEEE J. Earth Obs. Gadelmawla, E. Roughness parameters. Sander, M. Mummery, L. Thomas, T. Rough Surfaces Imperial College Press, A roughness measure for 3d mesh visual masking. In Proceedings of the 4th symposium on Applied perception in graphics and visualization , 57—60 ACM, A local roughness measure for 3d meshes and its application to visual masking.
ACM Trans. Poropat, G. Measurement of surface roughness of rock discontinuities. Rock Eng. Difficult Cond. Ge, Y. A description for rock joint roughness based on terrestrial laser scanner and image analysis. Download references. Leandro Tonietto, Luiz Gonzaga Jr. You can also search for this author in PubMed Google Scholar. All authors reviewed the manuscript.
Correspondence to Leandro Tonietto. Reprints and Permissions. Tonietto, L. Sci Rep 9, Download citation. Received : 14 February Accepted : 03 October Published : 21 October Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Journal of Biosystems Engineering By submitting a comment you agree to abide by our Terms and Community Guidelines.
If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
Advanced search. Skip to main content Thank you for visiting nature. Download PDF. Subjects Civil engineering Computer science. Introduction The most traditional technique of vertical closure of buildings is masonry lined with coating mortar.
Related Works To compute roughness coefficients or parameters to evaluate a surface, it is necessary to obtain the data that form the sampled surface.
Figure 1. Full size image. Method for Point Cloud Acquisition The success of cloud computing is directly related to the quality of the input data. Figure 2. Figure 3. Blocks production process. Each block must be carefully prepared for the reading process. Figure 4. Process for reading the surfaces.
Figure 5. Surface Roughness Computition After the point cloud acquisition process, the process of computing and representing of the roughness coefficients of the surface as a quadtree is executed. Figure 6. Height The height represents the distance between the reference surface and each point on the scale-limited surface.
A point lower than the reference plane has a negative value. Hill Region around a peak such that all maximal upward paths end at the peak L-filter L-filter is a filter eliminating the largest scale elements from the surface high-pass filter.
This filter is used to remove undulation and other lateral components from the surface, and thus allows for the extraction of only the roughness components. Local peak height Height difference between the peak and its nearest saddle point connected by ridge line Local pit height Height difference between the pit and its nearest saddle point connected by course line Peak Point on the surface that is higher than all other points within a neighborhood of that point Pit Point on the surface that is lower than all other points within a neighborhood of that point Primary surface Primary surface is the surface obtained after S-filtering the real surface.
Real surface Real surface indicates the surface constituted from measurement data in the XY plane direction. Reference surface Reference surface is the base for the scale-limited surface, and represents the plane at the mean height of the evaluation area as per the ISO Surface Texture function. Ridge line Curve separating adjacent dales S-filter S-filter is a filter eliminating the smallest scale elements from the surface low-pass filter. In the case of contact-type surface roughness measurement, noise attributable to edge shapes is removed.
S-F surface Surface filter is a surface obtained after applying an F-operator to the primary surface. Saddle point Point at which the ridge lines and course line cross Scale-limited surface Scale-limited surface means either the S-F surface or the S-L surface. Surface filter Surface filter is a filtration operator applied to a surface. Segmentation Watershed algorithm The watershed algorithm is employed to partition regions, which are used in the calculation of feature parameters.
Wolf pruning Peaks and pits merely need to be higher or lower that other points in their respective neighborhoods. Closed area, Open area A region that is in contact with the boundary of the definition area at the material height c is called an "open area," while a region that is not is called a "closed area.
0コメント