Amorphous Silica Slab Models with Variable Surface Roughness and Silanol Density for Use in Simulations of Dynamics and Catalysis

2021 ◽  
Author(s):  
Pubudu N. Wimalasiri ◽  
Nuong P. Nguyen ◽  
Hasini S. Senanayake ◽  
Brian B. Laird ◽  
Ward H. Thompson
Keyword(s):  
Surface Roughness ◽  
Amorphous Silica ◽  
Variable Surface

scholarly journals Influence of Support Vector Regression (SVR) on Cryogenic Face Milling

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Rao M. C. Karthik ◽  
Rashmi L. Malghan ◽  
Fuat Kara ◽  
Arunkumar Shettigar ◽  
Shrikantha S. Rao ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Comparative Analysis ◽  
Support Vector ◽  
Depth Of Cut ◽  
Convergence Factor ◽  
Learning Capability ◽  
Variable Surface ◽  
Relationship Model ◽  
Optimal Target ◽  
Independent Variable

The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, “one parametric approach” was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is “multiple linear regression.” Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.

Influence of Machining Strategies on Surface Roughness in Ball End Milling of Inclined Surfaces

2012 ◽  
Vol 488-489 ◽  
pp. 836-840 ◽  
Author(s):  
S. Shajari ◽  
M.H. Sadeghi ◽  
H. Hassanpour ◽  
B. Jabbaripour
Keyword(s):  
Surface Roughness ◽  
Tool Path ◽  
End Milling ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Scallop Height ◽  
Inclined Surfaces ◽  
Milling Operation ◽  
Variable Surface ◽  
Machining Strategies

Inclined surfaces are commonly used in the aerospace and die/mold industries. For machining this kind of surfaces, many aspects have to be considered as machinability considerations including milling strategies, machining parameters and etc. In machining, achieving better quality is challenging task. Various tool-path strategies during milling operation leads to variable surface roughness on machined samples. The objective of this study is to analyze different machining strategies in 3-axis milling of a typical curved geometry part. The machining parameters used in this study, are cutting speed, feedrate and stepover. This paper also presents an approach to develop a mathematical model for measuring Scallop height size and distribution for different machining strategies to show that Scallop height size has direct relation with Surface roughness measurements in each strategy. Finally the optimized strategy based on the results was determined.

Effect of Surface Roughness on the Acid Etching of Amorphous Silica

2005 ◽  
Vol 79 (3) ◽  
pp. 688-694 ◽  
Author(s):  
Mario Affatigato ◽  
Danny H. Osborne ◽  
Richard F. Haglund
Keyword(s):  
Surface Roughness ◽  
Amorphous Silica ◽  
Acid Etching ◽  
Effect Of Surface

Modeling the Surface Roughness for Fine Turning of AISI304 Stainless Steel

2007 ◽  
Vol 364-366 ◽  
pp. 644-648 ◽  
Author(s):  
Wei Shin Lin
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Prediction Model ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Variable Surface ◽  
Fine Turning

High ductility, high strength, high work hardening rate and low thermal conductivity of stainless steels are the main factors that make their machinability difficult. In this study, determination of the optimum cutting condition has been aimed at when fine turning an AISI 304 austenitic stainless steel using ceramic cutting tools. The cutting speeds for the turning test were from 80 to 320 m / min, feed rates were from 0.04 to 0.10 mm / rev and the depth of cut was fixed at 0.1 mm. According to the test results, we can find that the values of surface roughness were decreased when the cutting speed was increasing, and decrease with the decrease of feed rate. But when the cutting speed was greater than 360 m / min, or the feed rate was smaller than 0.02 mm / rev，the surface roughness would be deteriorated because of the chatter phenomenon. In this paper, a polynomial network is adopted to construct a prediction model on surface roughness for fine turning of AISI304 austenitic stainless steel. The polynomial network is composed of a number of functional nodes. These functional nodes are self-organized to form an optimal network architecture by using a predicted square error (PSE) criterion. It is shown that the polynomial network can correctly correlate the input variables (cutting speed and feed rate) with the output variable (surface roughness). Based on the surface roughness prediction model constructed, the surface roughness of the workpiece can be predicted with reasonable accuracy if the turning conditions are given and it is also consistent with the experimental results very well.

scholarly journals Simulating the Effects of Surface Roughness on Reinforced Concrete T Beam Bridge under Single and Multiple Vehicles

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Rahul Kalyankar ◽  
Nasim Uddin
Keyword(s):  
Surface Roughness ◽  
Reinforced Concrete ◽  
Dynamic Amplification Factor ◽  
Variable Surface ◽  
Multiple Vehicles ◽  
Single Vehicle ◽  
Spatial System ◽  
Road Surface Roughness ◽  
Beam Bridge ◽  
T Beam

This research focuses on the application of the spatial system of finite element modeling for the vehicle-bridge interaction on reinforced concrete US Girder Bridge in order to obtain the effect of surface roughness. Single vehicle and multiple vehicles on reinforced concrete T beam bridge were studied with variable surface roughness profiles. The effects of six different surface roughness profiles (very good, good, measured, average, poor, and very poor) were investigated for vehicle-bridge interaction. The values of the Dynamic Amplification Factor (DAF) were obtained for single and multiple vehicles on T Beam Bridge for different surface roughness profiles, along with the distances between the axles of heavy vehicle. It was observed that when the bridge has very good, good, measured, and average surface roughness, the DAF values for the single vehicle over the bridge were observed to be within acceptable limits specified by AASHTO. However, for the bridge with multiple vehicles only very good and measured surface roughness profiles showed a DAF and vehicle axle distances within the acceptable limits. From the current studies, it was observed that the spatial system showed reliable responses for predicting the behavior of the bridge under variable road surface roughness conditions and was reliable in vehicle axle detection, and therefore, it has a potential to be use for realistic simulations.

Investigation on surface roughness of sintered PA2200 prototypes using Taguchi method

2019 ◽  
Vol 25 (3) ◽  
pp. 454-461 ◽  
Author(s):  
Naveen Kumar Mavoori ◽  
Sriram Vekatesh ◽  
Manzoor Hussain M.
Keyword(s):  
Surface Roughness ◽  
Layer Thickness ◽  
Process Parameters ◽  
Laser Power ◽  
Research Paper ◽  
Content Type ◽  
Taguchi Design Of Experiments ◽  
Variable Surface ◽  
Level 3 ◽  
Level 2

Purpose The purpose of this research paper is to optimize the process parameters of selective laser sintering process, and the sintered parts of PA2200 prototypes are built with minimum surface roughness within the range of 10-12 microns using the Taguchi design of experiments approach. Design/methodology/approach In this research paper, a 3D model is created using catia V5 and exported to rapid prototype machine, and the 3D model file was repaired by using Magics software to remove the facets and saved with file extension .stl (standard triangulation language).Taguchi design of experiments approach L9 orthogonal array was selected with three factors at three levels each and total nine experiments were conducted with the quality index lower-the-better signal-to-noise ratio to produced better quality prototypes by optimizing the process parameters like laser power, layer thickness and temperature and tested on surface tester for surface roughness. The experimental results of surface roughness were compared with Regression Analysis, S/N Ratio, Analysis of Mean and predicted model on sintered prototypes. Findings The experimental results obtained after testing on the surface tester compared with mathematical model for the quality index lower-the-better signal-noise ratio with optimal process parameters operating at Temperature at level 3, Layer thickness at level 3, and Laser power at level 3, regression analysis, and predictive model the output response variable surface roughness, is with in the range of 9-10.5 microns are all most same and from ANOM (Analysis of Mean), temperature at leve1, layer thickness at level 2, laser power at level 2 is 9 -9.6 microns. Research limitations/implications The process parameters such as beam diameter and table speed were not considered on output response variable surface roughness in this research paper. Originality/value All the experiments were conducted and the parts are produced by using the material PA2200 in the powder from and sintered by Co2 laser by varying the process parameters with optimal settings to produce minimum surface roughness the out put from this paper is the influence of process parameters on surface roughness can be predicted at optimal settings with in less time and cost.

Influence of Surface Roughness on the Aerodynamic Losses of a Turbine Vane

2005 ◽  
Vol 128 (3) ◽  
pp. 568-578 ◽  
Author(s):  
Qiang Zhang ◽  
Matt Goodro ◽  
Phillip M. Ligrani ◽  
Ricardo Trindade ◽  
Sri Sreekanth
Keyword(s):  
Surface Roughness ◽  
Mach Number ◽  
Kinetic Energy ◽  
Three Dimensional ◽  
Aerodynamic Loss ◽  
Variable Surface ◽  
Turbine Vanes ◽  
Turbine Vane ◽  
Loss Coefficients ◽  
Aerodynamic Losses

The effects of surface roughness on the aerodynamic performance of a turbine vane are investigated for three Mach number distributions, one of which results in transonic flow. Four turbine vanes, each with the same shape and exterior dimensions, are employed with different rough surfaces. The nonuniform, irregular, three-dimensional roughness on the tested vanes is employed to match the roughness which exists on operating turbine vanes subject to extended operating times with significant particulate deposition on the surfaces. Wake profiles are measured for two different positions downstream the vane trailing edge. The contributions of varying surface roughness to aerodynamic losses, Mach number profiles, normalized kinetic energy profiles, Integrated Aerodynamics Losses (IAL), area-averaged loss coefficients, and mass-averaged loss coefficients are quantified. Total pressure losses, Mach number deficits, and deficits of kinetic energy all increase at each profile location within the wake as the size of equivalent sandgrain roughness increases, provided the roughness on the surfaces is uniform. Corresponding Integrated Aerodynamic Loss IAL magnitudes increase either as Mach numbers along the airfoil are higher, or as the size of surface roughness increases. Data are also provided which illustrate the larger loss magnitudes which are present with flow turning and cambered airfoils, than with symmetric airfoils. Also described are wake broadening, profile asymmetry, and effects of increased turbulent diffusion, variable surface roughness, and streamwise development.

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