Povzetek

This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the input-output relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10%. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %.

Ključne besede

ANFIS;discharge current;pulse duration;duty cycle;graphite powder;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 621.3
COBISS: 168505859 Povezava se bo odprla v novem oknu
ISSN: 0039-2480
Št. ogledov: 12
Št. prenosov: 0
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarni naslov: Uporaba mehke logike za napovedovanje površinske hrapavosti po elektroerozijski obdelavi titanove zlitine s primešanim prahom
Sekundarne ključne besede: ANFIS;tok razelektritve;trajanje impulza;impulzni faktorji;grafitni prah;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 376-387
Letnik: ǂVol. ǂ69
Zvezek: ǂno. ǂ9/10
Čas izdaje: Sept./Oct. 2023
DOI: 10.5545/sv-jme.2023.561
ID: 20386139
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