Using multi-objective optimization to design parameters in electro-discharge machining by wire | Zendy

Carlos Alberto Ochoa | Zendy; Lourdes Margain | Zendy; Francisco Javier Ornelas | Zendy; Sandra Guadalupe Jiménez | Zendy; Teresa Padilla | Zendy

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Using multi-objective optimization to design parameters in electro-discharge machining by wire

Author(s) -

Carlos Alberto Ochoa,

Lourdes Margain,

Francisco Javier Ornelas,

Sandra Guadalupe Jiménez,

Teresa Padilla

Publication year - 2014

Publication title -

adcaij advances in distributed computing and artificial intelligence journal

Language(s) - English

Resource type - Journals

ISSN - 2255-2863

DOI - 10.14201/adcaij20143219

Subject(s) - sorting , taguchi methods , orthogonal array , surface roughness , genetic algorithm , population , machining , electrical discharge machining , mathematical optimization , design of experiments , chromosome , process (computing) , signal (programming language) , mechanical engineering , computer science , engineering , algorithm , mathematics , materials science , statistics , composite material , machine learning , chemistry , sociology , operating system , biochemistry , programming language , demography , gene

The following paper describes the main objective to follow the methodology used and proposed to obtain the optimal values of WEDM process operation on the machine Robofil 310 by robust parameter design (RPD) of Dr. G. Taguichi [TAGUCHI, G. 1993], through controllable factors which result in more inferences regarding the problem to noise signal (S / N), which for this study is the variability of the hardness of samples from 6061, also studied the behaviour of the output parameters as the material removal rate (MRR) and surface roughness (Ra), subsequently took the RPD orthogonal array and characterized the individuals in the population, each optimal value is a gene and each possible solution is a chromosome, used multi-objective optimization using Non-dominated Sorting Genetic Algorithm to cross and mutate this population to generate better results MRR and Ra.

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