Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm
This study aimed at improving the residual wall thickness uniformity (RWTU), which was closely related to the mechanical properties of plastic parts with a hollow cross-section, in short-fiber reinforced composites (SFRC) overflow water-assisted injection molding (OWAIM). The influences of five inde...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Polymer Technology |
| Online Access: | http://dx.doi.org/10.1155/2020/6154694 |
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| author | Haiying Zhou Hesheng Liu Tangqing Kuang Qingsong Jiang Zhixin Chen Weipei Li |
| author_facet | Haiying Zhou Hesheng Liu Tangqing Kuang Qingsong Jiang Zhixin Chen Weipei Li |
| author_sort | Haiying Zhou |
| collection | DOAJ |
| description | This study aimed at improving the residual wall thickness uniformity (RWTU), which was closely related to the mechanical properties of plastic parts with a hollow cross-section, in short-fiber reinforced composites (SFRC) overflow water-assisted injection molding (OWAIM). The influences of five independent process parameters (melt temperature, mold temperature, delay time, water pressure, and water temperature) on RWTU were investigated through the methods such as central composite design, regression equation, and analyses of variance. Response surface methodology (RSM) and artificial neural network (ANN) optimized by genetic algorithm (GA) were employed to map the relationship between the process parameters and the standard deviation (SD) depicting the RWTU. Comparison assessments of three models (RSM, ANN, and ANN-GA) were carried out through some statistical indexes. It was concluded that the effect of melt temperature, delay time, and water temperature were significant to RWTU; the hybrid ANN-GA model had the best performance for predicting SD compared with RSM and ANN; the least SD obtained in optimization using ANN-GA as a fitness function was 0.0972. |
| format | Article |
| id | doaj-art-7bb56472ad1343cc911ba7a27f8ee030 |
| institution | Kabale University |
| issn | 0730-6679 1098-2329 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Polymer Technology |
| spelling | doaj-art-7bb56472ad1343cc911ba7a27f8ee0302025-02-03T01:04:17ZengWileyAdvances in Polymer Technology0730-66791098-23292020-01-01202010.1155/2020/61546946154694Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic AlgorithmHaiying Zhou0Hesheng Liu1Tangqing Kuang2Qingsong Jiang3Zhixin Chen4Weipei Li5School of Mechanical and Electrical Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Mechanical and Electrical Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Mechanical and Electrical Engineering, East China Jiaotong University, Nanchang 330013, ChinaJiangxi Province Key Laboratory of Polymer Micro/Nano Manufacturing and Devices, East China University of Technology, Nanchang 330013, ChinaJiangxi Province Key Laboratory of Polymer Micro/Nano Manufacturing and Devices, East China University of Technology, Nanchang 330013, ChinaJiangxi Province Key Laboratory of Polymer Micro/Nano Manufacturing and Devices, East China University of Technology, Nanchang 330013, ChinaThis study aimed at improving the residual wall thickness uniformity (RWTU), which was closely related to the mechanical properties of plastic parts with a hollow cross-section, in short-fiber reinforced composites (SFRC) overflow water-assisted injection molding (OWAIM). The influences of five independent process parameters (melt temperature, mold temperature, delay time, water pressure, and water temperature) on RWTU were investigated through the methods such as central composite design, regression equation, and analyses of variance. Response surface methodology (RSM) and artificial neural network (ANN) optimized by genetic algorithm (GA) were employed to map the relationship between the process parameters and the standard deviation (SD) depicting the RWTU. Comparison assessments of three models (RSM, ANN, and ANN-GA) were carried out through some statistical indexes. It was concluded that the effect of melt temperature, delay time, and water temperature were significant to RWTU; the hybrid ANN-GA model had the best performance for predicting SD compared with RSM and ANN; the least SD obtained in optimization using ANN-GA as a fitness function was 0.0972.http://dx.doi.org/10.1155/2020/6154694 |
| spellingShingle | Haiying Zhou Hesheng Liu Tangqing Kuang Qingsong Jiang Zhixin Chen Weipei Li Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm Advances in Polymer Technology |
| title | Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm |
| title_full | Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm |
| title_fullStr | Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm |
| title_full_unstemmed | Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm |
| title_short | Optimization of Residual Wall Thickness Uniformity in Short-Fiber-Reinforced Composites Water-Assisted Injection Molding Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm |
| title_sort | optimization of residual wall thickness uniformity in short fiber reinforced composites water assisted injection molding using response surface methodology and artificial neural network genetic algorithm |
| url | http://dx.doi.org/10.1155/2020/6154694 |
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