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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Chandrashekarappa, Manjunath Patel Gowdru
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2022Effect of Pin Geometry and Orientation on Friction and Wear Behavior of Nickel-Coated EN8 Steel Pin and Al6061 Alloy Disc Paircitations
- 2021Corrosion behaviour of high-strength Al 7005 alloy and its composites reinforced with industrial waste-based fly ash and glass fibre: comparison of stir cast and extrusion conditionscitations
- 2021Experimental investigation of selective laser melting parameters for higher surface quality and microhardness propertiescitations
- 2021Image processing of Mg-Al-Sn alloy microstructures for determining phase ratios and grain size and correction with manual measurementcitations
- 2021The effect of Zn and Zn–WO3 composites nano-coatings deposition on hardness and corrosion resistance in steel substratecitations
- 2016Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithmscitations
- 2016Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimizationcitations
- 2015Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approachescitations
- 2014Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches
- 2014Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approachescitations
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article
Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms
Abstract
<jats:p>The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.</jats:p>