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RISS 인기검색어
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- 저자 : Hamid Baseri (검색결과 5 건)
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Jafar Baseri,Rahim Naghizadeh,Hamid Reza Rezaie 한양대학교 세라믹연구소 2017 Journal of Ceramic Processing Research Vol.18 No.1
Alumina-spinel(NiAl2O4, CoAl2O4 solid solution)-metal(nickel, cobalt solid solution) composites were fabricated by partialreduction of spinel in carbon-bed and sintered with three different processes namely, pressureless, hot pressing, and sparkplasma sintering (SPS). The microstructural features and mechanical properties of composites were investigated. Thepressureless samples, SPS samples, and hot pressed samples reached > 91%, > 97% and > 98% theoretical density,respectively. The flexural strength of SPS, hot pressed, and pressureless samples were about 415 MPa, 367 MPa, and 247 MPa,respectively. Vickers Microhardness of SPS, hot pressed, and pressureless sintering were about 15.3, 14.5, and 10.98 GPa,respectively. The fracture toughness of SPS and hot pressed samples did not have a significant difference, and they were about7.2 and 7.8 MPa.m1/2, repectively
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Reza Teimouri,Hamid Baseri 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.2
In this work, two models of feed forward back-propagation neural network (FFBP-NN) and adaptive neuro-fuzzy inference system (ANFIS) have been developed to predict the performance of magnetic abrasive finishing process, based on experimental data of literature [7]. Input parameters of process are electromagnet's voltage, mesh number of abrasive particles, poles rotational speed and weight percent of abrasive particles, and also the output is percentage of surface roughness variation. In order to select the best model, a comparison between developed models has been done based on their mean absolute error (MAE) and root mean square error (RMSE). Moreover, optimization methods based on simulated annealing (SA) and particle swarm optimization (PSO) algorithms were used to maximize the percent of surface roughness variation and select the optimal process parameters. Results indicated that the models based on artificial intelligence predict much more precise values with respect to predictive regression model developed in main literature [7]. Also, the ANFIS model had a lowest value of MAE and RMSE with respect to others. So it was used as an objective function to maximize the surface roughness variation by using SA and PSO. Comparison between the obtained optimal solutions and analysis of results in main literature indicated that SA and PSO could find the optimal answers logically and precisely.
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Investigation of near dry EDM compared with wet and dry EDM processes
Ahad Gholipoor,Hamid Baseri,Mohammadreza Shabgard 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.5
Material removal rate (MRR), tool wear ratio (TWR) and surface roughness (SR) obtained by near-dry EDM process were comparedwith wet and dry EDM at three levels of discharge energy in drilling of SPK steel. Surface integrity machined by this process was studiedand compared with wet and dry EDM processes, by scanning electron microscopy (SEM). The results showed that at high level of dischargeenergy, wet EDM has the most MRR, TWR and SR, and dry EDM has the least MRR, TWR and SR, while at low dischargeenergy levels, near-dry EDM process has the most MRR and the least SR. SEM micrographs showed that the quality of surface obtainedby near-dry EDM process is better than others and the machined surfaces by near-dry EDM process have lower micro-cracks and craters,relatively.
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Pressure Path Optimization of Hydrodynamic Deep Drawing of Cylindrical-Conical Parts
Amirreza Yaghoobi,Hamid Baseri,Mohammad Bakhshi-Jooybari,Abdolhamid Gorji 한국정밀공학회 2013 International Journal of Precision Engineering and Vol. No.
Pressure path is one of the most important parameters in hydrodynamic deep drawing process which affect on thickness distribution and bursting in the parts. In this study, a combination of finite element simulation and artificial intelligence was used to optimize the pressure path in hydrodynamic deep drawing process of cylindrical-conical parts to reach the minimum thickness reduction in critical region of product. First, a finite element simulation model was verified based on experimental results. Then, a neural network model was developed using the data generated from the verified finite element model to predict the thickness in critical region of product. The results indicated that the neural network model can be applied successfully for prediction of sheet thickness. In addition, the neural network model was used as a function in simulated annealing algorithm to maximize the thickness in the mentioned critical region. The final results showed that utilization of the optimized loading path yields good uniform thickness distribution of the part.
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Determination of optimum SLA process parameters of H-shaped parts
Emad Rajabi Khorasani,Hamid Baseri 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.3
A model was proposed for optimization of stereolithography (SLA) process parameters to achieve the minimum shrinkage of H-shaped parts. A neural network was designed to correlate the input parameters to dimensional error of the parts manufactured by SLA. For this purpose, the data of a previous study from the literature was used that investigated the effect of three important parameters (layer thickness, hatch overcure and hatch spacing) of the SLA process by measuring the H-shaped parts manufactured by SLA 250. Then, the neural network model was imported into two optimization algorithms (genetic algorithm and simulated annealing) and the optimal values were determined. Results showed that the combination of neural network and optimization algorithms could determine the optimal input parameters for the minimum shrinkage with good accuracy.
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