DocumentCode :
2854980
Title :
Optimization and modeling of turning process for aluminium - silicon carbide composite using Artificial Neural Network Models
Author :
Jeyapaul, R. ; Sivasankar, S.
Author_Institution :
Dept. of Production Eng., Nat. Inst. of Technol., Tiruchirappalli, India
fYear :
2011
fDate :
6-9 Dec. 2011
Firstpage :
773
Lastpage :
778
Abstract :
The major work elements of this paper are manufacturing of Metal Matrix Composites (MMC), Machining of MMC and Optimization and modeling of Machining parameters. The cast is produced through permanent moulding process for the mixing ratio of 15% SiC and 85% Al. A Taguchi´s Orthogonal Array (OA) experiment is designed to carry out the machining operation. Four parameters, namely Tool materials, speed, feed and depth of cut are considered as factors. The output parameters are cutting power, cutting force, shear strength, surface finish and Material removal rate. The output responses are combined to have a single objective as multi response performance index (MRPI) and Manufacturer value function (MVF). ANN models are developed for mapping the relationship between parameters with MRPI and MVF. The optimal process parameters are selected based on the output given by the ANN. The results of both functions are compared by using S/N ratio analysis.
Keywords :
Taguchi methods; aluminium; cutting; mixing; moulding; neural nets; optimisation; particle reinforced composites; production engineering computing; silicon compounds; turning (machining); ANN models; Taguchi orthogonal array; aluminium-silicon carbide composite; artificial neural network models; cutting force; cutting power; machining parameter modeling; manufacturer value function; material removal rate; metal matrix composite manufacturing; mixing; multiresponse performance index; permanent moulding process; shear strength; surface finish; tool materials; turning process modeling; turning process optimization; Artificial neural networks; Feeds; Force; Machining; Materials; Performance analysis; Silicon carbide; ANN; MMC; Machining; Optimization; S/N ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
ISSN :
2157-3611
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
Type :
conf
DOI :
10.1109/IEEM.2011.6118021
Filename :
6118021
Link To Document :
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