- Tytuł:
- Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization
- Autorzy:
-
Patel, G. C. M.
Krishna, P.
Vundavilli, P. R.
Parappagoudar, M. B. - Powiązania:
- https://bibliotekanauki.pl/articles/379601.pdf
- Data publikacji:
- 2016
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
squeeze casting process
multi-objective optimization
genetic algorithm
squeeze casting
prasowanie stopu
optymalizacja wielokryterialna
algorytm genetyczny - Opis:
- The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.
- Źródło:
-
Archives of Foundry Engineering; 2016, 16, 3; 172-186
1897-3310
2299-2944 - Pojawia się w:
- Archives of Foundry Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł