scholarly journals A Novel Light Sectioning Vision System for A Three Dimensional Surface Roughness Assessment (Dept.M)

2020 ◽  
Vol 35 (3) ◽  
pp. 14-26
Author(s):  
Ossama Abouelatta
Keyword(s):  
Surface Roughness ◽  
Vision System ◽  
Three Dimensional ◽  
Roughness Assessment ◽  
Light Sectioning ◽  
Dimensional Surface

Enhancement of Heat Transfer Performance in Nuclear Fuel Rod Using Nanofluids and Surface Roughness Technique

2015 ◽  
Author(s):  
Kang Liu ◽  
Titan C. Paul ◽  
Leo A. Carrilho ◽  
Jamil A. Khan
Keyword(s):  
Heat Transfer ◽  
Surface Roughness ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Nuclear Fuel ◽  
Three Dimensional ◽  
Pressurized Water ◽  
Bulk Fluid ◽  
Fuel Rod ◽  
Dimensional Surface

The experimental investigations were carried out of a pressurized water nuclear reactor (PWR) with enhanced surface using different concentration (0.5 and 2.0 vol%) of ZnO/DI-water based nanofluids as a coolant. The experimental setup consisted of a flow loop with a nuclear fuel rod section that was heated by electrical current. The fuel rod surfaces were termed as two-dimensional surface roughness (square transverse ribbed surface) and three-dimensional surface roughness (diamond shaped blocks). The variation in temperature of nuclear fuel rod was measured along the length of a specified section. Heat transfer coefficient was calculated by measuring heat flux and temperature differences between surface and bulk fluid. The experimental results of nanofluids were compared with the coolant as a DI-water data. The maximum heat transfer coefficient enhancement was achieved 33% at Re = 1.15 × 105 for fuel rod with three-dimensional surface roughness using 2.0 vol% nanofluids compared to DI-water.

Three-Dimensional Scene Reconstruction

2011 ◽  
pp. 142-162
Author(s):  
Dimitrios Chrysostomou ◽  
Antonios Gasteratos
Keyword(s):  
Vision System ◽  
Three Dimensional ◽  
3D Models ◽  
Scene Reconstruction ◽  
Depth Information ◽  
Vision Systems ◽  
Working Space ◽  
Scene Structure ◽  
Long Time ◽  
Dimensional Surface

The production of 3D models has been a popular research topic already for a long time, and important progress has been made since the early days. During the last decades, vision systems have established to become the standard and one of the most efficient sensorial assets in industrial and everyday applications. Due to the fact that vision provides several vital attributes, many applications tend to use novel vision systems into domestic, working, industrial, and any other environments. To achieve such goals, a vision system should robustly and effectively reconstruct the 3D surface and the working space. This chapter discusses different methods for capturing the three-dimensional surface of a scene. Geometric approaches to three-dimensional scene reconstruction are generally based on the knowledge of the scene structure from the camera’s internal and external parameters. Another class of methods encompasses the photometric approaches, which evaluate the pixels’ intensity to understand the three-dimensional scene structure. The third and final category of approaches, the so-called real aperture approaches, includes methods that use the physical properties of the visual sensors for image acquisition in order to reproduce the depth information of a scene.

scholarly journals Multiscale three-dimensional surface reconstruction and surface roughness of porcine left anterior descending coronary arteries

2019 ◽  
Vol 6 (9) ◽  
pp. 190915 ◽  
Author(s):  
Hanna E. Burton ◽  
Rachael Cullinan ◽  
Kyle Jiang ◽  
Daniel M. Espino
Keyword(s):  
Surface Roughness ◽  
Coronary Arteries ◽  
Medical Devices ◽  
Ex Vivo ◽  
Three Dimensional ◽  
Force Microscopy ◽  
Optical Scanning ◽  
Atomic Force ◽  
Reconstructed Surface ◽  
Dimensional Surface

The aim of this study was to investigate the multiscale surface roughness characteristics of coronary arteries, to aid in the development of novel biomaterials and bioinspired medical devices. Porcine left anterior descending coronary arteries were dissected ex vivo , and specimens were chemically fixed and dehydrated for testing. Surface roughness was calculated from three-dimensional reconstructed surface images obtained by optical, scanning electron and atomic force microscopy, ranging in magnification from 10× to 5500×. Circumferential surface roughness decreased with magnification, and microscopy type was found to influence surface roughness values. Longitudinal surface roughness was not affected by magnification or microscopy types within the parameters of this study. This study found that coronary arteries exhibit multiscale characteristics. It also highlights the importance of ensuring consistent microscopy parameters to provide comparable surface roughness values.

Measuring and Quantifying the Surface-Roughness Anisotropy of Modeled and Industrially Produced Surfaces

2005 ◽  
Vol 6-8 ◽  
pp. 573-582 ◽  
Author(s):  
C.M. Wichern ◽  
W. Rasp
Keyword(s):  
Surface Roughness ◽  
Rolling Direction ◽  
Three Dimensional ◽  
Roughness Parameter ◽  
Orientation Angle ◽  
Textured Surface ◽  
Angular Orientation ◽  
Textured Surfaces ◽  
Average Roughness ◽  
Dimensional Surface

‘Three-dimensional surface profilometry’ when used for analysis and product specification reports roughness parameters that provide an average surface description over a relatively large area. Many commercial sheet steels are produced with special textured surfaces for tribological benefits or appearance benefits. These surfaces, as well as others, may demonstrate high levels of roughness anisotropy that is not quantifiable by simple three dimensional surface parameters. This anisotropy can play an important role in the surface appearance of the finished product and in the tribological behaviour during forming. The current work presents a method for quantifying surface-roughness features as a function of angular orientation with respect to rolling direction. The measurement methodology was applied to several model surfaces and one industrially produced electron-beam textured-surface (EBT). This methodology extracts multiple surface-height profiles of the same angular orientation from a single surface and calculates an average roughness parameter for the orientation angle based on the multiple profiles. Particularly interesting results were the large number of profiles necessary to obtain repeatable values for the roughness variation with respect to direction and the strong influence of surface feature size on the repeatability of said results. These results indicate that care must be taken when using a single extracted profile to represent a ‘three-dimensional’ surface.

Evaluation and ANN-Based Prediction on Functional Parameters of Surface Roughness in Precision Grinding of Cast Iron

2014 ◽  
Vol 1017 ◽  
pp. 166-171
Author(s):  
Bin Zhao ◽  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Surface Roughness ◽  
Cast Iron ◽  
Three Dimensional ◽  
Ann Model ◽  
Linear Speed ◽  
Functional Parameters ◽  
Grinding Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Dimensional Surface

Three-dimensional surface roughness parameters are widely applied to characterize frictional and lubricating properties, corrosion resistance, fatigue strength of surfaces. Among them, the functional parameters of surface roughness, such as Sbi, Sci, and Svi, are used to evaluate bearing and fluid retention properties of surfaces. In this study, the effects of grinding parameters, including wheel linear speed (Vs), workpiece linear speed (Vw), grinding depth (ap), longitudinal feed rate (fa), and dressing rate (F), on functional parameters were studied in grinding of cast iron. An artificial neural network (ANN) model was developed for predicting the functional parameters of three-dimensional surface roughness. The inputs of the ANN models were grinding parameters (Vs, Vw, ap, fa, F), and the output parameters of the models were functional parameters of surface roughness (Sbi, Sci, Svi). With small errors (e.g MSE = 0.09%, 0.61%, and 0.0014%. ), the ANN-based models are considered sufficiently accurate to predict functional parameters of surface roughness in grinding of cast iron.

Modeling Effects of Material Properties and Three-Dimensional Surface Roughness on Thermal Barrier Coatings

2000 ◽  
Vol 645 ◽  
Author(s):  
Michael L. Glynn ◽  
K.T. Ramesh ◽  
P.K. Wright ◽  
K.J. Hemker
Keyword(s):  
Surface Roughness ◽  
Residual Stresses ◽  
Thermal Barrier Coatings ◽  
Material Properties ◽  
Bond Coat ◽  
Coefficient Of Thermal Expansion ◽  
Three Dimensional ◽  
Thermal Barrier ◽  
Barrier Coatings ◽  
Dimensional Surface

ABSTRACTThermal barrier coatings (TBCs) are known to spall as a result of the residual stresses that develop during thermal cycling. TBC's are multi-layered coatings comprised of a metallic bond coat, thermally grown oxide and the ceramic top coat, all on top of a Ni-base superalloy substrate. The development of residual stresses is related to the generation of thermal, elastic and plastic strains in each of the layers. The focus of the current study is the development of a finite element analysis (FEA) that will model the development of residual stresses in these layers. Both interfacial roughness and material parameters (e.g., modulus of elasticity, coefficient of thermal expansion and stress relaxation of the bond coat) play a significant role in the development of residual stresses. The FEA developed in this work incorporates both of these effects and will be used to study the consequence of interface roughness, as measured in SEM micrographs, and material properties, that are being measured in a parallel project, on the development of these stresses. In this paper, the effect of an idealized three-dimensional surface roughness is compared to residual stresses resulting from a grooved surface formed by revolving a sinusoidal wave about an axis of symmetry. It is shown that cylindrical and flat button models give similar results, while the 3-D model results in stresses that are significantly larger than the stresses predicted in 2-D.

scholarly journals Process Capability Assessment Using Vision System

2021 ◽  
Vol 13 (2) ◽  
pp. 96-102
Author(s):  
Shivanna Dodda Mallappa ◽  
◽  
Kiran Mysore Bhaskar ◽  
Venkatesh Gude Subbaraya ◽  
Kavitha Shimoga Divakar ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Vision System ◽  
Research Work ◽  
Stable Process ◽  
Process Capability ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Measuring Surface ◽  
Roughness Assessment

Surface roughness assessment would help in predicting a component’s functionality. This clearly shows the significance of measuring the surface roughness of machined components. Thus, each machined component, depending upon its intended function, requires a certain surface finish. To predict the surface roughness of a machined component, a detailed understanding of the machining parameters is essential. This is because, surface roughness generated on a component, depends upon machining parameters speed, feed, and depth of cut. A stable manufacturing process gives a consistent surface finish on all the manufactured components. Thus, only by having a stable process, consistent quality of manufactured products is possible. The capability of the machine is defined as the capability of the machine to carry out the set process efficiently and effectively to produce parts as per the specification limits. Machining parameters, tools, coolant flow rate, etc. An effort has been made in this research work, to show how by measuring surface roughness of machined components process capability can be assessed. Thus, the method is a novel technique of assessing the process capability of a given process. A capable process would help a manufacturing company in meeting customer expectations. The proposed method is of non-contact type, quick, and industry-friendly.

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