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Naslov:Machinability analysis and multi-response optimization using NGSA-II algorithm for particle reinforced aluminum based metal matrix composites
Avtorji:ID Umer, U. (Avtor)
ID Mohammed, M. K. (Avtor)
ID Abidi, M. H. (Avtor)
ID Alkhalefah, H. (Avtor)
ID Kishawy, Hossam A. (Avtor)
Datoteke:.pdf APEM17-2_205-218.pdf (1,08 MB)
MD5: C2D7B8D7581C54F7C282904CC18BDAC6
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:In this study the effects of reinforcement particle size and cutting parameters on machining performance variables like cutting force, maximum tool-chip interface temperature and surface roughness of the machined surface have been investigated while machining Aluminum based metal matrix composites (MMCs). MMC bars with silicon carbide reinforcement having 10 % volume fraction and particle sizes of 5 μm, 10 μm and 15 μm are machined with polycrystalline diamond (PCD) inserts. Experiments are performed using central composite design (CCD) having four parameters with three levels. Response surfaces for each performance variables are generated using polynomial models. Single variable and interaction effects have been investigated using principal component analysis and 3D response charts. Multi-response optimization has been performed to minimize surface roughness and maximum tool-chip interface temperature using non-dominated sorting genetic algorithm II (NSGA-II). In addition, constraints have been applied to the optimization search to filter design points with high cutting forces and low material removal rate. Most of the optimal solutions are found to be with moderate cutting speeds, low feed rate and low depth of cuts.
Ključne besede:metal matrix composites, MMC, machining, reinforcement particle, machinability, multi-objective optimization, non-dominated sorting genetic algorithm, NSGA-II
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:08.03.2022
Datum sprejetja članka:31.08.2022
Datum objave:31.08.2022
Založnik:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
Leto izida:2022
Št. strani:str. 205-218
Številčenje:Vol. 17, no. 2
PID:20.500.12556/DKUM-97225 Novo okno
UDK:658.5
COBISS.SI-ID:269411331 Novo okno
DOI:10.14743/apem2022.2.431 Novo okno
ISSN pri članku:1854-6250
Avtorske pravice:Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation, and DOI.
Datum objave v DKUM:24.02.2026
Število ogledov:158
Število prenosov:1
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
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Gradivo je del revije

Naslov:Advances in production engineering & management
Skrajšan naslov:Adv produc engineer manag
Založnik:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:genetski algoritmi


Zbirka

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  1. Advances in production engineering & management

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