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This paper presents the mathematical modelling and parametric optimization on flank wear and surface roughness based on response surface methodology and grey-based Taguchi method in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coated carbide (TiN/TiCN/Al2O3/TiN) insert under dry environment.
For this reason, an EC model was used to simulate roughness, based on which an influence mechanism equation was deduced.
A mathematical model of roughness based on disc wheel grinding for face gears is proposed.
Calibration adjusts spatially distributed Manning's roughness based on modeled-observed discharge.
The process planner uses this data list and enables users to evaluate surface roughness based on a mathematical model.
Finally, to predict loudness and roughness based on biodiesel chemical properties, surface response models have been developed.
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It should also be noted that many prediction models revealing the relationship between SSD and surface roughness were based on the quasi-static indentation fracture model proposed by Lawn et al. [64].
Spatial surface roughness parameters based on the autocorrelation function allow a quantitative correlation between roughness and microstructure.
Land-cover data recorded during this historic survey was used to establish floodplain roughness values based on published Manning's n for the various land-cover types, and these roughness values were then adjusted to calibrate the model.
In order to realize artificial hierarchical micro-/nano- dual-scale surface roughness, silica based raspberry-like nanoparticles were investigated.
In this paper, the feasibility of roughness measurements based on interferometric techniques is discussed.
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