Mathematical models have been created for surface roughness, namely Ra and Rz, through response surface methodology (RS M.
Using these models, the changes in height roughness, namely the radius, can be varied without changing the Wenzel roughness factors.
The wettability properties are controlled by a systematic and reproducible variation of the surface roughness, namely the construction of geometrical structure [43].
In their study, damage depth was found to be proportional to the second order function of surface roughness, namely, (delta = chi SR^{2} + l), where constant l is related to the geometric shape of abrasives and to material properties.
Two roughness parameters, namely average roughness (Ra) and maximum roughness (maximum height of profile, Rz) were used to evaluate surface roughness of the specimens.
To this purpose, four different surfaces used as substrates, namely low roughness (LRG), high roughness (HRG), drilled holes (DH), and combination of high roughness with drilled holes (HRGDH) surfaces were prepared.
The present work focuses on prediction of wheel wear and surface roughness using two techniques, namely design of experiments and neural network.
Structural parameters, namely surface roughness of the PFF derived carbon (CNx) microfibers, concentrations and chemical states of the dopants, etc. have been found to vary significantly with pyrolysis temperature (Tp).
The objective of this work is to model the output responses namely; surface roughness (Ra), cutting force (Fc), cutting power (Pc), specific cutting force (Ks) and metal removal rate (MRR), during the face milling of the austenitic stainless steel X2CrNi18-9 with coated carbide inserts (GC4040).
It is clearly noted that by altering or modifying the surface texture, namely, the roughness of titanium implants, in particular, desired effects can be obtained like bone implant contact, removal torque values, tissues response, and biocompatibility.
Fractal geometry was introduced to model the rough surface, and the roughness was characterized by two parameters, namely, relative roughness height and fractal dimension.
