Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques
In this study, statistical and soft computing techniques were developed to investigate effect of process parameters on diameter of extruded filament made of polypropylene in hot extrusion. A multi-factors experiment was designed with process parameters of screw speed, roller speed and die temperatur...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2019
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| Online Access: | http://eprints.uthm.edu.my/4390/ http://eprints.uthm.edu.my/4390/1/AJ%202019%20%28263%29.pdf |
| _version_ | 1848888274762858496 |
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| author | Ong, Pauline Ho, Choon Sin Chin, Desmond Daniel Vui Sheng Sia, Chee Kiong Ng, Chuan Huat Wahab, Md Saidin Bala, Abduladim Salem |
| author_facet | Ong, Pauline Ho, Choon Sin Chin, Desmond Daniel Vui Sheng Sia, Chee Kiong Ng, Chuan Huat Wahab, Md Saidin Bala, Abduladim Salem |
| author_sort | Ong, Pauline |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | In this study, statistical and soft computing techniques were developed to investigate effect of process parameters on diameter of extruded filament made of polypropylene in hot extrusion. A multi-factors experiment was designed with process parameters of screw speed, roller speed and die temperature. According to the design matrix, twenty four experiments were conducted. The diameter of the extruded plastic filament was measured in each experiment. Subsequently, statistical analysis was used to identify significant factors on diameter of extruded filament. Predictive models of response surface methodology (RSM) and radial basis function neural network(RBFNN)were applied to predict the diameter of extruded filament. The optimal process parameters to maintain the diameter of the filament closest to the target value were identified using the cuckoo search algorithm (CSA), and particle swarm optimization (PSO). Performance analysis demonstrated the superior predictive ability of both models, in which the prediction errors of 0.0245 and 0.0029 (in terms of mean squared error) were obtained byRSM and RBFNN, respectively. Considering the optimization methods, the optimization approaches of using CSA and PSO were promising, in which average relative error of 1.28% was obtained in confirmation tests. |
| first_indexed | 2025-11-15T20:07:41Z |
| format | Article |
| id | uthm-4390 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:07:41Z |
| publishDate | 2019 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-43902021-12-02T04:39:39Z http://eprints.uthm.edu.my/4390/ Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques Ong, Pauline Ho, Choon Sin Chin, Desmond Daniel Vui Sheng Sia, Chee Kiong Ng, Chuan Huat Wahab, Md Saidin Bala, Abduladim Salem QA299.6-433 Analysis In this study, statistical and soft computing techniques were developed to investigate effect of process parameters on diameter of extruded filament made of polypropylene in hot extrusion. A multi-factors experiment was designed with process parameters of screw speed, roller speed and die temperature. According to the design matrix, twenty four experiments were conducted. The diameter of the extruded plastic filament was measured in each experiment. Subsequently, statistical analysis was used to identify significant factors on diameter of extruded filament. Predictive models of response surface methodology (RSM) and radial basis function neural network(RBFNN)were applied to predict the diameter of extruded filament. The optimal process parameters to maintain the diameter of the filament closest to the target value were identified using the cuckoo search algorithm (CSA), and particle swarm optimization (PSO). Performance analysis demonstrated the superior predictive ability of both models, in which the prediction errors of 0.0245 and 0.0029 (in terms of mean squared error) were obtained byRSM and RBFNN, respectively. Considering the optimization methods, the optimization approaches of using CSA and PSO were promising, in which average relative error of 1.28% was obtained in confirmation tests. Springer 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/4390/1/AJ%202019%20%28263%29.pdf Ong, Pauline and Ho, Choon Sin and Chin, Desmond Daniel Vui Sheng and Sia, Chee Kiong and Ng, Chuan Huat and Wahab, Md Saidin and Bala, Abduladim Salem (2019) Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques. Journal of Intelligent Manufacturing, 30. pp. 1957-1972. ISSN 1572-8145 https://doi.org/10.1007/s10845-017-1365-8 |
| spellingShingle | QA299.6-433 Analysis Ong, Pauline Ho, Choon Sin Chin, Desmond Daniel Vui Sheng Sia, Chee Kiong Ng, Chuan Huat Wahab, Md Saidin Bala, Abduladim Salem Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| title | Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| title_full | Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| title_fullStr | Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| title_full_unstemmed | Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| title_short | Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| title_sort | diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques |
| topic | QA299.6-433 Analysis |
| url | http://eprints.uthm.edu.my/4390/ http://eprints.uthm.edu.my/4390/ http://eprints.uthm.edu.my/4390/1/AJ%202019%20%28263%29.pdf |