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:	Chidozie Chukwuemeka Nwobi-Okoye, Patrick Chukwuka Okonji, Stanley Okiy
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	artificial neural network genetic algorithm multi-objective optimization ant hill groundnut shell ash dry mould Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/05ac0f98d1f54612a6844ab42216638a
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id	oai:doaj.org-article:05ac0f98d1f54612a6844ab42216638a
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spelling	oai:doaj.org-article:05ac0f98d1f54612a6844ab42216638a2021-11-04T15:51:56ZOptimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm2331-191610.1080/23311916.2019.1681055https://doaj.org/article/05ac0f98d1f54612a6844ab42216638a2019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1681055https://doaj.org/toc/2331-1916In 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.Chidozie Chukwuemeka Nwobi-OkoyePatrick Chukwuka OkonjiStanley OkiyTaylor & Francis Grouparticleartificial neural networkgenetic algorithmmulti-objective optimizationant hillgroundnut shell ashdry mouldEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network genetic algorithm multi-objective optimization ant hill groundnut shell ash dry mould Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	artificial neural network genetic algorithm multi-objective optimization ant hill groundnut shell ash dry mould Engineering (General). Civil engineering (General) TA1-2040 Chidozie Chukwuemeka Nwobi-Okoye Patrick Chukwuka Okonji Stanley Okiy Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
description	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.
format	article
author	Chidozie Chukwuemeka Nwobi-Okoye Patrick Chukwuka Okonji Stanley Okiy
author_facet	Chidozie Chukwuemeka Nwobi-Okoye Patrick Chukwuka Okonji Stanley Okiy
author_sort	Chidozie Chukwuemeka Nwobi-Okoye
title	Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
title_short	Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
title_full	Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
title_fullStr	Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
title_full_unstemmed	Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm
title_sort	optimization of dry compressive strength of groundnut shell ash particles (gsap) and ant hill bonded foundry sand using ann and genetic algorithm
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/05ac0f98d1f54612a6844ab42216638a
work_keys_str_mv	AT chidoziechukwuemekanwobiokoye optimizationofdrycompressivestrengthofgroundnutshellashparticlesgsapandanthillbondedfoundrysandusingannandgeneticalgorithm AT patrickchukwukaokonji optimizationofdrycompressivestrengthofgroundnutshellashparticlesgsapandanthillbondedfoundrysandusingannandgeneticalgorithm AT stanleyokiy optimizationofdrycompressivestrengthofgroundnutshellashparticlesgsapandanthillbondedfoundrysandusingannandgeneticalgorithm
_version_	1718444692271529984

Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm

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