H.Mohammadi majd

Publications

11 Effect of Spray Stand-off on Elasticity Modulus of Thermally Sprayed Coatings

Authors: M.Jalali Azizpour, S.Norouzi, D.Sajedipour, H.Mohammadi majd, M.M.Rabieh, A. Jaderi

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).

Keywords: elasticity modulus, HVOF, thermal spray, WC-Co, Micro-indentation

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10 Adhesion Strength Evaluation Methods in Thermally Sprayed Coatings

Authors: M.Jalali Azizpour, H.Mohammadi majd, Milad Jalali, H.Fasihi

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.

Keywords: Adhesion, Cohesion, bonding strength, HVOF Thermal spray

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9 Effect of Spray Stand-off on Hardness of Thermally Sprayed Coatings

Authors: M.Jalali Azizpour, S.Norouzi, H.Mohammadi majd

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).

Keywords: HVOF, thermal spray, WC-Co, Micro-indentation

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8 Prediction the Limiting Drawing Ratio in Deep Drawing Process by Back Propagation Artificial Neural Network

Authors: M.Jalali Azizpour, H.Mohammadi majd, M. Goodarzi

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.

Keywords: prediction, deep drawing, BPANN, limiting drawingratio (LDR), Levenberg–Marquardt algorithm

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7 Bond Strength in Thermally Sprayed Gas Turbine Shafts

Authors: M.Jalali Azizpour, S.Norouzi, D.Sajedipour, H.Mohammadi majd, S.A.Hosseini, H.Talebi, A.Ghamari

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).

Keywords: Residual Stress, HVOF, thermal spray, wear mechanism, Gas compressor shafts

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6 A Study on Barreling Behavior during Upsetting Process using Artificial Neural Networks with Levenberg Algorithm

Authors: M.Jalali Azizpour, H.Mohammadi majd

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

Keywords: prediction, upsetting, Back-propagation artificial neural network(BPANN)

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5 Development Trend in Investigation of Residual Stresses in WC-Co Coating by HVOF Thermal Spraying

Authors: M.Jalali Azizpour, S.Norouzi, D.Sajedipour, H.Mohammadi majd, R.Mohammadi Sadr, M.Derakhshan Mehr, S.A Shoabi, R.Mohammadi

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.

Keywords: Residual Stress, HVOF, compressive stress, WC-Co

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4 Residual Stresses in Thermally Sprayed Gas Turbine Components

Authors: M.Jalali Azizpour, S.Norouzi, D.Sajedipour, H.Mohammadi majd

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)

Keywords: Residual Stress, HVOF, thermal spray, wear mechanism, Gas compressor shafts

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3 Prediction the Deformation in Upsetting Process by Neural Network and Finite Element

Authors: M.Jalali Azizpour, H.Mohammadi majd, Foad Saadi

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

Keywords: prediction, upsetting, Back-propagation artificial neural network(BPANN)

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2 Application of Neural Network and Finite Element for Prediction the Limiting Drawing Ratio in Deep Drawing Process

Authors: M.Jalali Azizpour, H.Mohammadi majd, A.V. Hoseini

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.

Keywords: prediction, deep drawing, Back-propagation artificial neural network(BPANN), limiting drawing ratio (LDR)

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1 Application of HVOF Thermal Spraying inHigh Speed Gas Compressor Shafts

Authors: M.Jalali Azizpour, S.Norouzi, H.Mohammadi majd, H.Talebi, A.Ghamari

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.

Keywords: Residual Stress, HVOF, thermal spray, wear mechanism, Gas compressor shafts

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Abstracts

1 The Effect of Particle Temperature on the Thickness of Thermally Sprayed Coatings

Authors: H.Mohammadi majd, M. Jalali Azizpour

Abstract:

In this paper, the effect of WC-12Co particle Temperature in HVOF thermal spraying process on the coating thickness has been studied. The statistical results show that the spray distance and oxygen-to-fuel ratio are more effective factors on particle characterization and thickness of HVOF thermal spraying coatings. Spray Watch diagnostic system, scanning electron microscopy (SEM), X-ray diffraction and thickness measuring system were used for this purpose.

Keywords: temperature, velocity, HVOF, WC-12Co, thickness

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