Abstract: Metal matrix composites have been increasingly used
as materials for components in automotive and aerospace industries
because of their improved properties compared with non-reinforced
alloys. During machining the selection of appropriate machining
parameters to produce job for desired surface roughness is of great
concern considering the economy of manufacturing process. In this
study, a surface roughness prediction model using fuzzy logic is
developed for end milling of Al-SiCp metal matrix composite
component using carbide end mill cutter. The surface roughness is
modeled as a function of spindle speed (N), feed rate (f), depth of cut
(d) and the SiCp percentage (S). The predicted values surface
roughness is compared with experimental result. The model predicts
average percentage error as 4.56% and mean square error as 0.0729.
It is observed that surface roughness is most influenced by feed rate,
spindle speed and SiC percentage. Depth of cut has least influence.
Abstract: Carriers scattering in the inversion channel of n-
MOSFET dominates the drain current. This paper presents an effective
electron mobility model for the pocket implanted nano scale
n-MOSFET. The model is developed by using two linear pocket
profiles at the source and drain edges. The channel is divided into
three regions at source, drain and central part of the channel region.
The total number of inversion layer charges is found for these three
regions by numerical integration from source to drain ends and the
number of depletion layer charges is found by using the effective
doping concentration including pocket doping effects. These two
charges are then used to find the effective normal electric field,
which is used to find the effective mobility model incorporating the
three scattering mechanisms, such as, Coulomb, phonon and surface
roughness scatterings as well as the ballistic phenomena for the
pocket implanted nano-scale n-MOSFET. The simulation results show
that the derived mobility model produces the same results as found
in the literatures.
Abstract: ZnO-SnO2 i.e. Zinc-Tin-Oxide (ZTO) thin films were
deposited on glass substrate with varying concentrations (ZnO:SnO2
- 100:0, 90:10, 70:30 and 50:50 wt.%) at room temperature by flash
evaporation technique. These deposited ZTO film were annealed at
450 0C in vacuum. These films were characterized to study the effect
of annealing on the structural, electrical, and optical properties.
Atomic force microscopy (AFM) and Scanning electron microscopy
(SEM) images manifest the surface morphology of these ZTO thin
films. The apparent growth of surface features revealed the formation
of nanostructure ZTO thin films. The small value of surface
roughness (root mean square RRMS) ensures the usefulness in
optical coatings. The sheet resistance was also found to be decreased
for both types of films with increasing concentration of SnO2. The
optical transmittance found to be decreased however blue shift has
been observed after annealing.
Abstract: Industrial surveys shows that manufacturing
companies define the qualities of thermal removing process based on
the dimension and physical appearance of the cutting material
surface. Therefore, the roughness of the surface area of the material
cut by the plasma arc cutting process and the rate of the removed
material by the manual plasma arc cutting machine was importantly
considered. Plasma arc cutter Selco Genesis 90 was used to cut
Standard AISI 1017 Steel of 200 mm x100 mm x 6 mm manually
based on the selected parameters setting. The material removal rate
(MRR) was measured by determining the weight of the specimens
before and after the cutting process. The surface roughness (SR)
analysis was conducted using Mitutoyo CS-3100 to determine the
average roughness value (Ra). Taguchi method was utilized to
achieve optimum condition for both outputs studied. The
microstructure analysis in the region of the cutting surface is
performed using SEM. The results reveal that the SR values are
inversely proportional to the MRR values. The quality of the surface
roughness depends on the dross peak that occurred after the cutting
process.
Abstract: The objective of this study is to design an adaptive
neuro-fuzzy inference system (ANFIS) for estimation of surface
roughness in grinding process. The Used data have been generated
from experimental observations when the wheel has been dressed
using a rotary diamond disc dresser. The input parameters of model
are dressing speed ratio, dressing depth and dresser cross-feed rate
and output parameter is surface roughness. In the experimental
procedure the grinding conditions are constant and only the dressing
conditions are varied. The comparison of the predicted values and the
experimental data indicates that the ANFIS model has a better
performance with respect to back-propagation neural network
(BPNN) model which has been presented by the authors in previous
work for estimation of the surface roughness.
Abstract: Nowadays, the demand for high product quality
focuses extensive attention to the quality of machined surface. The
(CNC) milling machine facilities provides a wide variety of
parameters set-up, making the machining process on the glass
excellent in manufacturing complicated special products compared to
other machining processes. However, the application of grinding
process on the CNC milling machine could be an ideal solution to
improve the product quality, but adopting the right machining
parameters is required. In glass milling operation, several machining
parameters are considered to be significant in affecting surface
roughness. These parameters include the lubrication pressure, spindle
speed, feed rate and depth of cut. In this research work, a fuzzy logic
model is offered to predict the surface roughness of a machined
surface in glass milling operation using CBN grinding tool. Four
membership functions are allocated to be connected with each input
of the model. The predicted results achieved via fuzzy logic model
are compared to the experimental result. The result demonstrated
settlement between the fuzzy model and experimental results with the
93.103% accuracy.