Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage
The main aim of this research is to optimize the injection molding process parameters in order to mitigate the shrinkage of polypropylene (PP) spur gears. The methodology used integrated experimental approaches with artificial neural networks (ANN), and Taguchi methods to determine the optimal combi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Semnan University
2025-08-01
|
| Series: | Mechanics of Advanced Composite Structures |
| Subjects: | |
| Online Access: | https://macs.semnan.ac.ir/article_8935_86230cc0bbf71c40b3c46f0f075349b2.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593587168083968 |
|---|---|
| author | Bikram Singh Solanki Devi Singh Rawat Harpreet Singh Tanuja Sheorey |
| author_facet | Bikram Singh Solanki Devi Singh Rawat Harpreet Singh Tanuja Sheorey |
| author_sort | Bikram Singh Solanki |
| collection | DOAJ |
| description | The main aim of this research is to optimize the injection molding process parameters in order to mitigate the shrinkage of polypropylene (PP) spur gears. The methodology used integrated experimental approaches with artificial neural networks (ANN), and Taguchi methods to determine the optimal combination of injection molding parameters. The experimental data was used to create an ANN model using Matlab software that accurately predicts unseen data with a variation of less than 5%. The trained ANN model was further used to predict gear shrinkage in the context of Taguchi-based design of experiments. The investigation involved the use of Taguchi and analysis of variance techniques, determining that cooling time is the most important and relevant parameter. This is followed by packing time and melt temperature. The analysis revealed that the gears saw the least amount of shrinkage when the molding was carried out using the optimal combination of injection molding parameters. |
| format | Article |
| id | doaj-art-fe0135a19e0d49fbbca270ab341aef09 |
| institution | Kabale University |
| issn | 2423-4826 2423-7043 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Semnan University |
| record_format | Article |
| series | Mechanics of Advanced Composite Structures |
| spelling | doaj-art-fe0135a19e0d49fbbca270ab341aef092025-01-20T11:30:37ZengSemnan UniversityMechanics of Advanced Composite Structures2423-48262423-70432025-08-0112224926010.22075/macs.2024.33801.16468935Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear ShrinkageBikram Singh Solanki0Devi Singh Rawat1Harpreet Singh2Tanuja Sheorey3Department of Mechanical Engineering, PDPM Indian Institute of Information Technology Design & manufacturing Jabalpur Dumna Airport Road, Dumna – 482005, IndiaDepartment of Mechanical Engineering, PDPM Indian Institute of Information Technology Design & manufacturing Jabalpur Dumna Airport Road, Dumna – 482005, IndiaDepartment of Mechanical Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar– 144008, IndiaDepartment of Mechanical Engineering, PDPM Indian Institute of Information Technology Design & manufacturing Jabalpur Dumna Airport Road, Dumna – 482005, IndiaThe main aim of this research is to optimize the injection molding process parameters in order to mitigate the shrinkage of polypropylene (PP) spur gears. The methodology used integrated experimental approaches with artificial neural networks (ANN), and Taguchi methods to determine the optimal combination of injection molding parameters. The experimental data was used to create an ANN model using Matlab software that accurately predicts unseen data with a variation of less than 5%. The trained ANN model was further used to predict gear shrinkage in the context of Taguchi-based design of experiments. The investigation involved the use of Taguchi and analysis of variance techniques, determining that cooling time is the most important and relevant parameter. This is followed by packing time and melt temperature. The analysis revealed that the gears saw the least amount of shrinkage when the molding was carried out using the optimal combination of injection molding parameters.https://macs.semnan.ac.ir/article_8935_86230cc0bbf71c40b3c46f0f075349b2.pdfinjection mouldingpolypropylene gearshrinkageartificial neural networkoptimisation |
| spellingShingle | Bikram Singh Solanki Devi Singh Rawat Harpreet Singh Tanuja Sheorey Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage Mechanics of Advanced Composite Structures injection moulding polypropylene gear shrinkage artificial neural network optimisation |
| title | Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage |
| title_full | Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage |
| title_fullStr | Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage |
| title_full_unstemmed | Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage |
| title_short | Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage |
| title_sort | integration of artificial neural network and taguchi method for prediction and minimisation of thick walled polypropylene gear shrinkage |
| topic | injection moulding polypropylene gear shrinkage artificial neural network optimisation |
| url | https://macs.semnan.ac.ir/article_8935_86230cc0bbf71c40b3c46f0f075349b2.pdf |
| work_keys_str_mv | AT bikramsinghsolanki integrationofartificialneuralnetworkandtaguchimethodforpredictionandminimisationofthickwalledpolypropylenegearshrinkage AT devisinghrawat integrationofartificialneuralnetworkandtaguchimethodforpredictionandminimisationofthickwalledpolypropylenegearshrinkage AT harpreetsingh integrationofartificialneuralnetworkandtaguchimethodforpredictionandminimisationofthickwalledpolypropylenegearshrinkage AT tanujasheorey integrationofartificialneuralnetworkandtaguchimethodforpredictionandminimisationofthickwalledpolypropylenegearshrinkage |