Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms
Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropyl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Polymers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4360/14/13/2739 |
| _version_ | 1797408331909824512 |
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| author | Amit Kumar Gope Yu-Shu Liao Chung-Feng Jeffrey Kuo |
| author_facet | Amit Kumar Gope Yu-Shu Liao Chung-Feng Jeffrey Kuo |
| author_sort | Amit Kumar Gope |
| collection | DOAJ |
| description | Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropylene (PP) was selected as the experimental material, and the quality achieved by adjusting the melt spinning machine’s processing parameter settings was used as the basis for judgement. The processing parameters included screw temperature, gear pump temperature, die head temperature, screw speed, gear pump speed, and take-up speed as the six control factors. The four quality characteristics included fineness, breaking strength, elongation at break, and elastic energy modulus. In the first part of our study, we applied fast deep-learning characteristic grid calculations on a 440-item historical data set to train a deep learning neural network and determine methods for multi-quality optimization. In the second part, with the best processing parameters as a benchmark, and given abnormal quality data derived from processing parameter settings deviating from these optimal values, several machine learning and deep learning methods were compared in their ability to find the settings responsible for the abnormal data, which was randomly split into a 210-item training data set and a 210-item verification data set. The random forest method proved to be the best at identifying responsible parameter settings, with accuracy rates of single and double identification classifications together of 100%, for single factor classification of 98.3%, and for double factor classification of 96.0%, thereby confirming that the diagnostic method proposed in this study can effectively predict product abnormality and find the parameter settings responsible for product abnormality. |
| first_indexed | 2024-03-09T03:56:55Z |
| format | Article |
| id | doaj.art-cca0ea20472d476ea1bfd33e34670cc9 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-09T03:56:55Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-cca0ea20472d476ea1bfd33e34670cc92023-12-03T14:19:23ZengMDPI AGPolymers2073-43602022-07-011413273910.3390/polym14132739Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning AlgorithmsAmit Kumar Gope0Yu-Shu Liao1Chung-Feng Jeffrey Kuo2Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanMelt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropylene (PP) was selected as the experimental material, and the quality achieved by adjusting the melt spinning machine’s processing parameter settings was used as the basis for judgement. The processing parameters included screw temperature, gear pump temperature, die head temperature, screw speed, gear pump speed, and take-up speed as the six control factors. The four quality characteristics included fineness, breaking strength, elongation at break, and elastic energy modulus. In the first part of our study, we applied fast deep-learning characteristic grid calculations on a 440-item historical data set to train a deep learning neural network and determine methods for multi-quality optimization. In the second part, with the best processing parameters as a benchmark, and given abnormal quality data derived from processing parameter settings deviating from these optimal values, several machine learning and deep learning methods were compared in their ability to find the settings responsible for the abnormal data, which was randomly split into a 210-item training data set and a 210-item verification data set. The random forest method proved to be the best at identifying responsible parameter settings, with accuracy rates of single and double identification classifications together of 100%, for single factor classification of 98.3%, and for double factor classification of 96.0%, thereby confirming that the diagnostic method proposed in this study can effectively predict product abnormality and find the parameter settings responsible for product abnormality.https://www.mdpi.com/2073-4360/14/13/2739melt spinning machineartificial intelligencemachine learningrandom forestdeep learningneural network |
| spellingShingle | Amit Kumar Gope Yu-Shu Liao Chung-Feng Jeffrey Kuo Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms Polymers melt spinning machine artificial intelligence machine learning random forest deep learning neural network |
| title | Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms |
| title_full | Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms |
| title_fullStr | Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms |
| title_full_unstemmed | Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms |
| title_short | Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms |
| title_sort | quality prediction and abnormal processing parameter identification in polypropylene fiber melt spinning using artificial intelligence machine learning and deep learning algorithms |
| topic | melt spinning machine artificial intelligence machine learning random forest deep learning neural network |
| url | https://www.mdpi.com/2073-4360/14/13/2739 |
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