Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm
This paper presents a hybrid of artificial neural networks and artificial bee colony algorithm to optimize the process parameters in injection molding with the aim of minimize warpage of plastic products. A feedforward neural network is employed to obtain a mathematical relationship between the pro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2013-08-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/16309 |
| _version_ | 1797861703957872640 |
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| author | Alejandro Alvarado Iniesta Jorge L. García Alcaraz Manuel Iván Rodríguez Borbón |
| author_facet | Alejandro Alvarado Iniesta Jorge L. García Alcaraz Manuel Iván Rodríguez Borbón |
| author_sort | Alejandro Alvarado Iniesta |
| collection | DOAJ |
| description |
This paper presents a hybrid of artificial neural networks and artificial bee colony algorithm to optimize the process parameters in injection molding with the aim of minimize warpage of plastic products. A feedforward neural network is employed to obtain a mathematical relationship between the process parameters and the optimization goal. Artificial bee colony algorithm is used to find the optimal set of process parameters values that would result in the optimal solution. An experimental case is presented by coupling Moldflow simulations along with the intelligent schemes in order to validate the proposed approach. Melt temperature, mold temperature, packing pressure, packing time, and cooling time are considered as the design variables. Results revealed the proposed approach can efficiently support engineers to determine the optimal process parameters and achieve competitive advantages in terms of quality and costs.
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| first_indexed | 2024-04-09T22:07:23Z |
| format | Article |
| id | doaj.art-99715ce7ee134c31aa9efcd70c78df11 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:07:23Z |
| publishDate | 2013-08-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-99715ce7ee134c31aa9efcd70c78df112023-03-23T12:34:27ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442013-08-016710.17533/udea.redin.16309Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithmAlejandro Alvarado Iniesta0Jorge L. García Alcaraz1Manuel Iván Rodríguez Borbón2Autonomous University of Ciudad JuarezAutonomous University of Ciudad JuarezAutonomous University of Ciudad Juarez This paper presents a hybrid of artificial neural networks and artificial bee colony algorithm to optimize the process parameters in injection molding with the aim of minimize warpage of plastic products. A feedforward neural network is employed to obtain a mathematical relationship between the process parameters and the optimization goal. Artificial bee colony algorithm is used to find the optimal set of process parameters values that would result in the optimal solution. An experimental case is presented by coupling Moldflow simulations along with the intelligent schemes in order to validate the proposed approach. Melt temperature, mold temperature, packing pressure, packing time, and cooling time are considered as the design variables. Results revealed the proposed approach can efficiently support engineers to determine the optimal process parameters and achieve competitive advantages in terms of quality and costs. https://revistas.udea.edu.co/index.php/ingenieria/article/view/16309artificial bee colony algorithmartificial neural networks injection molding optimization of process parametersfinite element simulation |
| spellingShingle | Alejandro Alvarado Iniesta Jorge L. García Alcaraz Manuel Iván Rodríguez Borbón Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm Revista Facultad de Ingeniería Universidad de Antioquia artificial bee colony algorithm artificial neural networks injection molding optimization of process parameters finite element simulation |
| title | Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm |
| title_full | Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm |
| title_fullStr | Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm |
| title_full_unstemmed | Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm |
| title_short | Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm |
| title_sort | optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm |
| topic | artificial bee colony algorithm artificial neural networks injection molding optimization of process parameters finite element simulation |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/16309 |
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