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 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: this paper presents a novel neural network controller
with composite adaptation low to improve the trajectory tracking
problems of biped robots comparing with classical controller. The
biped model has 5_link and 6 degrees of freedom and actuated by
Plated Pneumatic Artificial Muscle, which have a very high power to
weight ratio and it has large stoke compared to similar actuators. The
proposed controller employ a stable neural network in to approximate
unknown nonlinear functions in the robot dynamics, thereby
overcoming some limitation of conventional controllers such as PD
or adaptive controllers and guarantee good performance. This NN
controller significantly improve the accuracy requirements by
retraining the basic PD/PID loop, but adding an inner adaptive loop
that allows the controller to learn unknown parameters such as
friction coefficient, therefore improving tracking accuracy.
Simulation results plus graphical simulation in virtual reality show
that NN controller tracking performance is considerably better than
PD controller tracking performance.
Abstract: This work is focused on the steady boundary layer flow
near the forward stagnation point of plane and axisymmetric bodies
towards a stretching sheet. The no slip condition on the solid
boundary is replaced by the partial slip condition. The analytical
solutions for the velocity distributions are obtained for the various
values of the ratio of free stream velocity and stretching velocity, slip
parameter, the suction and injection velocity parameter, magnetic
parameter and dimensionality index parameter in the series forms with
the help of homotopy analysis method (HAM). Convergence of the
series is explicitly discussed. Results show that the flow and the skin
friction coefficient depend heavily on the velocity slip factor. In
addition, the effects of all the parameters mentioned above were more
pronounced for plane flows than for axisymmetric flows.
Abstract: In this paper back-propagation artificial neural
network (BPANN) with Levenberg–Marquardt algorithm is
employed to predict the limiting drawing ratio (LDR) of the deep
drawing process. To prepare a training set for BPANN, some finite
element simulations were carried out. die and punch radius, die arc
radius, friction coefficient, thickness, yield strength of sheet and
strain hardening exponent were used as the input data and the LDR
as the specified output used in the training of neural network. As a
result of the specified parameters, the program will be able to
estimate the LDR for any new given condition. Comparing FEM and
BPANN results, an acceptable correlation was found.
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.
Abstract: The importance of machining process in today-s
industry requires the establishment of more practical approaches to
clearly represent the intimate and severe contact on the tool-chipworkpiece
interfaces. Mathematical models are developed using the
measured force signals to relate each of the tool-chip friction
components on the rake face to the operating cutting parameters in
rough turning operation using multilayers coated carbide inserts.
Nonlinear modeling proved to have high capability to detect the
nonlinear functional variability embedded in the experimental data.
While feedrate is found to be the most influential parameter on the
friction coefficient and its related force components, both cutting
speed and depth of cut are found to have slight influence. Greater
deformed chip thickness is found to lower the value of friction
coefficient as the sliding length on the tool-chip interface is reduced.
Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.