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FME Transactions
2020, vol. 48, br. 3, str. 693-700
jezik rada: engleski
vrsta rada: neklasifikovan
objavljeno: 24/06/2020
doi: 10.5937/fme2003693K
Creative Commons License 4.0
Modeliranje površinske hrapavosti na bazi veštačke neuronske mreže kod obrade polimernih (epoksi) nanokompozita ojačanih višezidnim ugljeničnim nanocevima
Madan Mohan Malaviya University of Technology, Department of Mechanical Engineering, Gorakhpur, India

e-adresa: rajeshverma.nit@gmail.com

Projekat

This experimentation work performed in this article was supported by the Collaborative research scheme of AICTE, New Delhi, India.
This experimentation work performed in this article was supported by the Uttar Pradesh Council of Science and Technology Lucknow India.

Sažetak

Površinska hrapavost je u procesu proizvodnje najvažniji element kvaliteta mašinski obrađenog proizvoda. Rad se bavi modeliranjem površinske hrapavosti korišćenjem ANN kod obrade polimernog nanokompozita ojačanog sa MWCNT. ANN je razvijena kao ekonomičan modul za samo-učenje i fleksibilan za promenljive vrednosti složenih podataka. Tagučijev plan eksperimenta L27 je savršeno iskorišćen za postupak obrade. Parametri obrade: MWCNT (tež.%), brzina vretena, brzina pomoćnog kretanja i dubina rezanja su analizirani da bi se dobila minimalna površinska hrapavost obrađenih uzoraka. ANOVA analiza je pokazala da su za hrapavost najvažniji parametri brzine pomoćnog kretanja (55,25%), zatim brzine vretena, težinskog procenta MWCNT i dubine rezanja. Mreža propagacije unapred i unazad je korišćena za ANN model sa funkcijama TRAINLM i LEARNGDM koje se koriste kao algoritam za trening i učenje. Izbor adekvatnog modela je izvršen na bazi koeficijenta korelacije (R2 ), srednje kvadratne greške (MSE) i prosečne procentne greške (ARE). Dobijeni model ima veliku preciznost: R2 > 99%, MSE < 0,2%, APE < 3%. Prikazana eksperimentalna i predviđena vrednost pokazuju da je model adekvatan i primenljiv za uslove mašinske obrade.

Ključne reči

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