Jahandideh A.R.,Iran University of Industries and Mines |
Hamedi M.,University of Tehran |
Mansourzadeh S.A.,Sazeh Gostar Company |
Rahi A.,Iran University of Industries and Mines
Science and Technology of Welding and Joining | Year: 2011
The fracture mode of spot welded joints, made of SAPH440 steel sheets, is investigated. It was found that the weldment failure in the peel test of the joints occurred through the weld nugget. This is called an interfacial failure and is not acceptable because it is a sign of insufficient mechanical strength. Investigation showed that this kind of fracture is attributed to the brittleness of the nugget zone, caused by its martensitic microstructure due to the high cooling rate in the welding. For eliminating this defect, resistance spot welding procedures were augmented with postheating stage. This approach is intended to reduce the cooling rate after welding and also to temper the weld nugget, generating a more ductile microstructure in the weld zone. The results of this research can be used for planning spot welding process and provides a guideline for analysing the results of hardness and peel test. © 2011 Institute of Materials, Minerals and Mining.
Khademian N.,Islamic Azad University at Tehran |
Gholamipour R.,Iranian Research Organization for Science and Technology |
Shahri F.,Iran University of Industries and Mines |
Tamizifar M.,Iran University of Industries and Mines
Journal of Alloys and Compounds | Year: 2013
Effect of vanadium on the thermal and mechanical properties of the Zr 65Cu17.5Ni10Al7.5 bulk metallic glass has been studied. The vanadium substitution for zirconium in the bulk metallic glass leads to the decrease of the glass forming ability in constant cooling rate; as well as co-precipitation of Zr2Ni and Zr 2Cu crystalline phases in amorphous matrix. The size of the crystallites are about 20-50 nm in amorphous matrix and they act as a barrier against of rapid propagation of shear bands. In fact, the nanocrystalline phases in amorphous matrix cause the increase of the strain and the quasi-static compression strength about 58% and 20%, respectively. © 2012 Elsevier B.V. All rights reserved.
Farahnakian M.,Amirkabir University of Technology |
Razfar M.R.,Amirkabir University of Technology |
Moghri M.,Islamic Azad University at Kashan |
Asadnia M.,Iran University of Industries and Mines
International Journal of Advanced Manufacturing Technology | Year: 2011
During the past decade, polymer nanocomposites have emerged relatively as a new and rapidly developing class of composite materials and attracted considerable investment in research and development worldwide. An increase in the desire for personalized products has led to the requirement of the direct machining of polymers for personalized products. In this work, the effect of cutting parameters (spindle speed and feed rate) and nanoclay (NC) content on machinability properties of polyamide-6/nanoclay (PA-6/NC) nanocomposites was studied by using high speed steel end mill. This paper also presents a novel approach for modeling cutting forces and surface roughness in milling PA-6/NC nanocomposite materials, by using particle swarm optimization-based neural network (PSONN) and the training capacity of PSONN is compared to that of the conventional neural network. In this regard, advantages of the statistical experimental algorithm technique, experimental measurements artificial neural network and particle swarm optimization algorithm, are exploited in an integrated manner. The results indicate that the nanoclay content on PA-6 significantly decreases the cutting forces, but does not have a considerable effect on surface roughness. Also the obtained results for modeling cutting forces and surface roughness have shown very good training capacity of the proposed PSONN algorithm in comparison to that of a conventional neural network. © 2011 Springer-Verlag London Limited.
Mohammadnezhad M.,Isfahan University of Technology |
Javaheri V.,Research and Development Unit |
Javaheri V.,Iran University of Industries and Mines |
Shamanian M.,Isfahan University of Technology |
And 2 more authors.
Materials and Design | Year: 2013
Ni-Hard4 white cast iron is commonly used in applications requiring excellent abrasion resistance as in the mining and mineral ore processing industry. In this study, the effects of vanadium on the microstructure, mechanical properties and wear-resistance of Ni-Hard4 white cast iron were investigated. This study was conducted in six laboratory-made alloys with different vanadium contents. The microstructure of the samples was characterized using the optical microscopy, the scanning electron microscopy, and the energy dispersive X-ray spectrometry. The impact energy, hardness and wear resistance of the samples were determined. The results indicated that with an increase in vanadium concentration, the chromium carbides were refined, and the volume fraction of carbides was decreased. After increasing the vanadium content by 2%, the microstructure of Ni-Hard4 white cast iron became finer and the hardness and wear resistance were improved without reduction of fracture toughness. The result of on-line service (AG Mill) showed that the wear resistance of Ni-Hard4 white cast iron was modified by 2%, and vanadium liners were 40% better than the basic Ni-Hard4. © 2013 Elsevier Ltd.
Chavoshi S.Z.,Iran University of Industries and Mines |
Tajdari M.,Islamic Azad University
International Journal of Material Forming | Year: 2010
In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don't have acceptable accuracy. © 2010 Springer/ESAFORM.
Yazdi M.R.S.,University of Tehran |
Chavoshi S.Z.,Iran University of Industries and Mines
Production Engineering | Year: 2010
In this study, the effect of cutting parameters and machining forces on surface roughness and material removal rate of AL6061 in CNC face milling operation is investigated. Based on the experimental data, two different modeling techniques, namely regression analysis and multilayer perceptron, MLP, neural network, have been used to estimate the state variables (i.e. surface roughness, Ra, and material removal rate, MRR). Simulation results presented using machining data demonstrate that the MLP neural network possesses more powerful capacity than the regression analysis and performs the estimation of the Ra and MRR, simultaneously. © German Academic Society for Production Engineering (WGP) 2010.
Parsa M.H.,University of Tehran |
Mohammadi S.V.,Iran University of Industries and Mines |
Aghchai A.J.,K. N. Toosi University of Technology
International Journal of Advanced Manufacturing Technology | Year: 2014
Metal/polymer/metal laminated sheets have shown promising properties, as being light weight, in automotive and aircraft industries. In this paper, the effect of punch radius on springback in the early stage of V-die bending process of aluminum/polypropylene/aluminum sheets is studied both numerically and experimentally. To analyze springback behavior, contact pressure evolution during bending process was studied using numerical simulation. Springback behavior relies on bending moment and sheet thinning. Bending moment and sheet thinning depend on circumferential and transverse stress distributions along sheet thickness and sheet length. Overall opposite effect of bending moment and sheet thinning makes an increase in springback with punch radius. Parametric study was performed to investigate the effect of polymeric core on springback. It was observed that in a given thickness configuration, springback of laminate lies between those of polymeric and metallic monolayer sheets with the same thickness. Also, an increase in sandwich sheet thickness leads to a decrease in springback. © 2014, Springer-Verlag London.
Zare Chavoshi S.,Iran University of Industries and Mines
International Journal of Advanced Manufacturing Technology | Year: 2011
In this paper, the effect of feed rate, voltage, and flow rate of electrolyte on some performance parameters such as surface roughness, material removal rate, and over-cut of SAE-XEV-F valve-steel during electrochemical drilling in NaCl and NaNo3 electrolytic solutions have been studied using the main effect plot, the interaction plot and the ANOVA analysis. In continuation, in this case which the training dataset was small, an investigation has been done on the capability of the optimum presented regression analysis (RA), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) to predict the surface roughness, material removal rate and over-cut. The predicted parameters by the employed models have been compared with the experimental data. The comparison of results indicated that in electrochemical drilling using different electrolytic solutions, CANFIS gives the best results to predict the surface roughness and over-cut as well, while ANN is the best for predicting the material removal rate. © 2010 Springer-Verlag London Limited.
Salehghaffari S.,Mississippi State University |
Tajdari M.,Iran University of Industries and Mines |
Panahi M.,Iran University of Industries and Mines |
Mokhtarnezhad F.,Iran University of Industries and Mines
Thin-Walled Structures | Year: 2010
In this paper, experimental investigation of two new structural design solutions with the aim of improving crashworthiness characteristics of cylindrical metal tubes is performed. In the first design method, a rigid steel ring is press-fitted on top of circular aluminum tubes. When this arrangement of dissipating energy is subjected to axial compression, the rigid ring is driven into the cylindrical tube and expands its top area; then, plastic folds start shaping along the rest of the tube length as the compression of the structure continues. In the second design method, wide grooves are cut from the outer surface of steel thick-walled circular tubes. In fact, this method converts thick-walled tubes into several thin-walled tubes of shorter length, being assembled together coaxially. When this energy absorbing device is subjected to axial compression, plastic deformation occurs within the space of each wide groove, and thick portions control and stabilize collapsing of the whole structure. In the present study, several specimens of each developed design methods with various geometric parameters are prepared and compressed quasi-statistically. Also, some ordinary tubes of the same size of these specimens are compressed axially to investigate efficiency of the presented structural solutions in energy absorption applications. Experimental results show the significant efficiency of the presented design methods in improving crashworthiness characteristics and collapse modes of circular tubes under axial loading. © 2010 Elsevier Ltd. All rights reserved.
Chavoshi S.Z.,Iran University of Industries and Mines
Production Engineering | Year: 2011
Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect. © 2010 German Academic Society for Production Engineering (WGP).