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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. Source


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. Source


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. Source


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. Source


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. Source

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