Abstract: In this paper, the application of thermal spray
coatings in high speed shafts by a revolution up to 23000 RPM
has been studied. Gas compressor shafts are worn in contact
zone with journal therefore will be undersized. Wear
mechanisms of compressor shaft were identified. The
predominant wear mechanism is abrasion wear. The worn
surface was coated by hard WC-Co cermets using high
velocity oxy fuel (HVOF) after preparation. The shafts were in
satisfactory service in 8000h period. The metallurgical and
Tribological studies has been made on the worn and coated
shaft using optical microscopy, scanning electron microscopy
(SEM) and X-ray diffraction.
Abstract: In this paper back-propagation artificial neural
network (BPANN) 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: In this paper, the techniques for estimating the
residual stress in high velocity oxy fuel thermal spray coatings have
been discussed and compared. The development trend and the last
investigation have been studied. It is seemed that the there is not
effective study on the effect of the peening action in HVOF
analytically and 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 paper, the residual stress of thermal spray
coatings in gas turbine component by curvature method has been
studied. The samples and shaft were coated by hard WC-12Co
cermets using high velocity oxy fuel (HVOF) after preparation in
same conditions. The curvature of coated samples was measured by
using of coordinate measurement machine (CMM). The metallurgical
and Tribological studies has been made on the coated shaft using
optical microscopy and scanning electron microscopy (SEM)
Abstract: The techniques for estimating the adhesive and cohesive strength in high velocity oxy fuel (HVOF) thermal spray coatings have been discussed and compared. The development trend and the last investigation have been studied. We will focus on benefits and limitations of these methods in different process and materials.
Abstract: The mechanical and tribological properties in WC-Co
coatings are strongly affected by hardness and elasticity
specifications. The results revealed the effect of spraying distance on
microhardness and elasticity modulus of coatings. The metallurgical
studies have been made on coated samples using optical microscopy,
scanning electron microscopy (SEM).
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: The mechanical and tribological properties in WC-Co
coatings are strongly affected by hardness and elasticity
specifications. The results revealed the effect of spraying distance on
microhardness and elasticity modulus of coatings. The metallurgical
studies have been made on coated samples using optical microscopy,
scanning electron microscopy (SEM).
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: In this paper, the bond strength of thermal spray
coatings in high speed shafts has been studied. The metallurgical and
mechanical studies has been made on the coated samples and shaft
using optical microscopy, scanning electron microscopy (SEM).