Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA
Plastic injection molding is one of the most used methods for producing plastic products because it can be produced at a high production rate, low cost, and ease in manufacturing. However, one defect that affects product quality is namely warpage. To reduce plastic product warpage, the injection...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2023-04-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/5573 |
| _version_ | 1797852911443640320 |
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| author | Chiwapon Nitnara Kumpon Tragangoon |
| author_facet | Chiwapon Nitnara Kumpon Tragangoon |
| author_sort | Chiwapon Nitnara |
| collection | DOAJ |
| description | Plastic injection molding is one of the most used
methods for producing plastic products because it can be produced at a high
production rate, low cost, and ease in manufacturing. However, one defect that
affects product quality is namely warpage. To reduce plastic product warpage,
the injection molding process is required optimal process control to
increase plastic product quality. The objective of this paper is to
optimize injection molding process parameters for minimizing the warpage of
plastic glass. The optimization process is divided into two phases.
The Finite Element Method (FEM) was employed in the first phase to
simulate 32 experiments under various parameters. The parameters of this
process consist of melt temperature ranging from 180 to 230°C, mold
temperature in the range of 20 – 45°C, filling time from 0.82 to 0.92 s,
packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the
second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA)
was developed to predict the warpage and solve the optimization process to find
optimal parameters. Combining the intelligent method shows that ANN and GA
effectively find the optimal process parameters that can reduce the warpage of
the product by 35.73% from the maximum value. |
| first_indexed | 2024-04-09T19:42:00Z |
| format | Article |
| id | doaj.art-54d5fd3b11cc4ad4a519df256b513e41 |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-09T19:42:00Z |
| publishDate | 2023-04-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-54d5fd3b11cc4ad4a519df256b513e412023-04-04T05:09:16ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002023-04-0114242243310.14716/ijtech.v14i2.55735573Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GAChiwapon Nitnara0Kumpon Tragangoon1Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, 1518 Pracharat I, Bangsue, Bangkok 10800, ThailandDepartment of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, 1518 Pracharat I, Bangsue, Bangkok 10800, ThailandPlastic injection molding is one of the most used methods for producing plastic products because it can be produced at a high production rate, low cost, and ease in manufacturing. However, one defect that affects product quality is namely warpage. To reduce plastic product warpage, the injection molding process is required optimal process control to increase plastic product quality. The objective of this paper is to optimize injection molding process parameters for minimizing the warpage of plastic glass. The optimization process is divided into two phases. The Finite Element Method (FEM) was employed in the first phase to simulate 32 experiments under various parameters. The parameters of this process consist of melt temperature ranging from 180 to 230°C, mold temperature in the range of 20 – 45°C, filling time from 0.82 to 0.92 s, packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA) was developed to predict the warpage and solve the optimization process to find optimal parameters. Combining the intelligent method shows that ANN and GA effectively find the optimal process parameters that can reduce the warpage of the product by 35.73% from the maximum value.https://ijtech.eng.ui.ac.id/article/view/5573artificial neural network (ann)finite element method (fem)genetic algorithm (ga)optimizationplastic injection molding |
| spellingShingle | Chiwapon Nitnara Kumpon Tragangoon Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA International Journal of Technology artificial neural network (ann) finite element method (fem) genetic algorithm (ga) optimization plastic injection molding |
| title | Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA |
| title_full | Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA |
| title_fullStr | Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA |
| title_full_unstemmed | Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA |
| title_short | Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA |
| title_sort | simulation based optimization of injection molding process parameters for minimizing warpage by ann and ga |
| topic | artificial neural network (ann) finite element method (fem) genetic algorithm (ga) optimization plastic injection molding |
| url | https://ijtech.eng.ui.ac.id/article/view/5573 |
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