Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications [PDF]
, 2012 Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6-22 m(2) and decomposed them ...
Seidel, Moritz +5 more
core +1 more source
Surface morphology in engineering applications: Influence of roughness on sliding and wear in dry fretting [PDF]
, 2011 Influence of initial surface roughness on friction and wear processes under fretting conditions was investigated experimentally. Rough surfaces (Ra=0.15-2.52 [mu]m) were prepared on two materials: carbon alloy (AISI 1034) and titanium alloy (Ti-6Al-4V ...
Mathia, T.G. +8 more
core +1 more source
Analysis of surface roughness in relation to soil loss and runoff at high rainfall intensities. [PDF]
, 2002 The decay of roughness is an important factor governing surface processes such as infiltration and soil erosion. Thus the decay of surface roughness under different surface conditions was investigated and related to quantitative amounts of soil loss ...
Idowu, O. J. +2 more
core +1 more source
An experimental investigation on friction characteristics of air flow in microtube with structured surface roughness [PDF]
, 2011 This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece.
Yang, CY +4 more
core +6 more sources
Impact of the surface roughness on the electrical capacitance [PDF]
, 2006 A new hybrid approach consists to use the advantages of both systems namely the high geometric aspects of the electrodes of the ultracapacitor and the high dielectric strength of polymer materials used in dielectric capacitors.
Flahaut, Emmanuel +6 more
core +1 more source
Surface profile prediction and analysis applied to turning process [PDF]
, 2008 An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate.
LU, Chen, COSTES, Jean-Philippe
core +1 more source
Sensitivity of material failure to surface roughness: A study on titanium alloys Ti64 and Ti407
Materials & Design, 2021 The relationship between material failure and surface roughness has been investigated using two titanium alloys: Ti64 and the more ductile Ti407. Three surface types were created (polished, sandblasted and scratched) with instances spanning a wide range ...
Scott Sneddon +5 more
doaj +1 more source
How to select the most relevant 3D roughness parameters of a surface [PDF]
, 2014 In order to conduct a comprehensive roughness analysis, around sixty 3D roughness parameters are created to describe most of the surface morphology with regard to specific functions, properties or applications.
Bigerelle, M. +11 more
core +1 more source
Results in Engineering, 2023 Recently, the correlation between surface roughness and fineness of materials including pozzolans has been subjected to too much debate in literature.
David Sinkhonde
doaj +1 more source
Dynamic evolution of interface roughness during friction and wear processes [PDF]
, 2014 Dynamic evolution of surface roughness and influence of initial roughness (Sa=0.282 to 6.73 µm) during friction and wear processes has been analyzed experimentally.
Bigerelle, M. +19 more
core +2 more sources