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Paper Information

Journal:   MODARES MECHANICAL ENGINEERING   JANUARY 2017 , Volume 16 , Number 10 ; Page(s) 364 To 374.
 
Paper: 

MULTIOBJECTIVE OPTIMIZATION OF HOT METAL GAS FORMING PROCESS TO PRODUCTION OF SQUARE PARTS

 
 
Author(s):  NASROLLAHZADE MEHRDAD, SHAHBAZI KARAMI JAVAD, MOSLEMI NAEINI HASSAN*, HASHEMI SEYED JALAL, MOHAMMADI NAJAFABADI HOSEIN
 
* FACULTY OF MECHANICAL ENGINEERING, TARBIAT MODARES UNIVERSITY, TEHRAN, IRAN
 
Abstract: 

Hot gas forming process forms materials with low formability or high strength at room temperature such as aluminum, magnesium and titanium. With increasing temperature, the formability of these metals increases and the strength decreases. In this process, for producing products with desirable properties, determination of optimal parameters is essential. In this study, hot gas forming process was investigated numerically by using Abaqus software, and the effects of input parameters on the outputs were studied by analyzing simulation results. In order to validate simulation results, the experimental test was carried out by using hot gas forming setup. For modeling hot gas forming process, artificial neural network and regression models was created by using data obtained from the numerical simulation and then the accuracy of these models was examined. In these models, pressure, axial feeding and time were assumed as input parameters and the radius, minimum and maximum thickness were considered as output. In the next stage, best model was used as input function in multi-objective genetic optimization algorithm to obtain Pareto front and optimum process parameters. Optimum parameters were obtained as follows: pressure 15.82bar, axial feeding 2.76mm and time 69.02s. In addition, values of radius corner, minimum and maximum thickness achieved from using optimum parameters are 4.43mm, 0.808mm and 1.93mm respectively.

 
Keyword(s): HOT METAL GAS FORMING, OPTIMIZATION, GENETIC ALGORITHM, NEURAL NETWORK, REGRESSION MODEL
 
References: 
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