Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
In this research work, modeling and multi-objective optimization of dry foundry sand parameters were done using artificial neural network (ANN) and genetic algorithm (GA). ANN was used to predict dry compressive strength and unit production cost of dry foundry sand. The input parameters of the ANN w...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/05ac0f98d1f54612a6844ab42216638a |
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| Sumario: | In this research work, modeling and multi-objective optimization of dry foundry sand parameters were done using artificial neural network (ANN) and genetic algorithm (GA). ANN was used to predict dry compressive strength and unit production cost of dry foundry sand. The input parameters of the ANN were baking temperature, percentage additive (groundnut shell ash and ant hill soil) and baking time. The ANN predicted the dry compressive strength with a correlation coefficient of 0.99116 between the experimental values and predicted values, while the correlation coefficient between the observed unit cost and predicted unit cost was 1. The trained ANN was subsequently used as the fitness function for a GA used in the multi-objective optimization of the compressive strength and unit cost of production of the dry mould. The Pareto front showed the optimum strength and cost achievable with process input parameters. |
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