Abstract: The hydrodynamics behavior of fluid flow in microconverging
plates is investigated analytically. Effects of Knudsen number () on the microchannel hydrodynamics behavior and the
coefficient of friction are investigated. It is found that as increases the slip in the hydrodynamic boundary condition increases.
Also, the coefficient of friction decreases as increases.
Abstract: In a metal forming process, the friction between the
material and the tools influences the process by modifying the stress
distribution of the workpiece. This frictional behaviour is often taken
into account by using a constant coefficient of friction in the finite
element simulations of sheet metal forming processes. However,
friction coefficient varies in time and space with many parameters.
The Stribeck friction model is investigated in this study to predict
springback behaviour of AA6061-T4 sheets during V-bending
process. The coefficient of friction in Stribeck curve depends on
sliding velocity and contact pressure. The plane-strain bending
process is simulated in ABAQUS/Standard. We compared the
computed punch load-stroke curves and springback related to the
constant coefficient of friction with the defined friction model. The
results clearly showed that the new friction model provides better
agreement between experiments and results of numerical simulations.
The influence of friction models on stress distribution in the
workpiece is also studied numerically
Abstract: In this paper back-propagation artificial neural network
(BPANN )with Levenberg–Marquardt algorithm is employed to
predict the deformation of the upsetting process. To prepare a
training set for BPANN, some finite element simulations were
carried out. The input data for the artificial neural network are a set
of parameters generated randomly (aspect ratio d/h, material
properties, temperature and coefficient of friction). The output data
are the coefficient of polynomial that fitted on barreling curves.
Neural network was trained using barreling curves generated by
finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
Abstract: In this study, some physical and mechanical properties
of jujube fruits, were measured and compared at constant moisture
content of 15.5% w.b. The results showed that the mean length, width
and thickness of jujube fruits were 18.88, 16.79 and 15.9 mm,
respectively. The mean projected areas of jujube perpendicular to
length, width, and thickness were 147.01, 224.08 and 274.60 mm2,
respectively. The mean mass and volume were 1.51 g and 2672.80
mm3, respectively. The arithmetic mean diameter, geometric mean
diameter and equivalent diameter varied from 14.53 to 20 mm, 14.5
to 19.94 mm, and 14.52 to 19.97 mm, respectively. The sphericity,
aspect ratio and surface area of jujube fruits were 0.91, 0.89 and
926.28 mm2, respectively. Whole fruit density, bulk density and
porosity of jujube fruits were measured and found to be 1.52 g/cm3,
0.3 g/cm3 and 79.3%, respectively. The angle of repose of jujube fruit
was 14.66° (±0.58°). The static coefficient of friction on galvanized
iron steel was higher than that on plywood and lower than that on
glass surface. The values of rupture force, deformation, hardness and
energy absorbed were found to be between 11.13-19.91N, 2.53-
4.82mm, 3.06-5.81N mm and 20.13-39.08 N/mm, respectively.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
Abstract: Physical and mechanical properties of Russian olive
fruits were measured at moisture content of 14.43% w.b. The results
revealed that the mean length, width and thickness of Russian olive
fruits were 20.72, 15.73 and 14.69mm, respectively. Mean mass and
volume of Russian olive fruits were measured as 1.45 g and 2.55 cm3,
respectively. The sphericity, aspect ratio and surface area were
calculated as 0.81, 0.72 and 8.96 cm2, respectively, while arithmetic
mean diameter, geometric mean diameter and equivalent diameter of
Russian olive fruits were 17.05, 16.83 and 16.84 mm, respectively.
Whole fruit density, bulk density and porosity of jujube fruits were
measured and found to be 1.01 g/cm3, 0.29 g/cm3 and 69.5%,
respectively. The values of static coefficient of friction on three
surfaces of glass, galvanized iron and plywood were 0.35, 0.36 and
0.43, respectively. The skin color (L*, a*, b*) varied from 9.92 to
16.08; 2.04 to 3.91 and 1.12 to 3.83, respectively. The values of
rupture force, deformation, energy absorbed and hardness were found
to be between 12.14-16.85 N, 2.16-4.25 mm, 3.42-6.99 N mm and
17.1-23.85 N/mm.
Abstract: A diamond-like carbon (DLC) based solid-lubricant
film was designed and DLC films were successfully prepared using a
microwave plasma enhanced magnetron sputtering deposition
technology. Post-test characterizations including Raman
spectrometry, X-ray diffraction, nano-indentation test, adhesion test,
friction coefficient test were performed to study the influence of
substrate bias voltage on the mechanical properties of the W- and
S-doped DLC films. The results indicated that the W- and S-doped
DLC films also had the typical structure of DLC films and a better
mechanical performance achieved by the application of a substrate
bias of -200V.