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Solanki, Bikram Singh, Rawat, Devi Singh, Singh, Harpreet, Sheorey, Tanuja. (1403). Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage. نشریه تخصصی هنرهای کاربردی, (), -. doi: 10.22075/macs.2024.33801.1646
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". نشریه تخصصی هنرهای کاربردی, , , 1403, -. doi: 10.22075/macs.2024.33801.1646
Solanki, Bikram Singh, Rawat, Devi Singh, Singh, Harpreet, Sheorey, Tanuja. (1403). 'Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage', نشریه تخصصی هنرهای کاربردی, (), pp. -. doi: 10.22075/macs.2024.33801.1646
Solanki, Bikram Singh, Rawat, Devi Singh, Singh, Harpreet, Sheorey, Tanuja. Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage. نشریه تخصصی هنرهای کاربردی, 1403; (): -. doi: 10.22075/macs.2024.33801.1646

Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage

Mechanics of Advanced Composite Structures
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 25 مرداد 1403    اصل مقاله (949.49 K)
نوع مقاله: Special Issue: Mechanics of Advanced Fiber Reinforced Composite Structures
شناسه دیجیتال (DOI): 10.22075/macs.2024.33801.1646
نویسندگان
Bikram Singh Solanki* 1؛ Devi Singh Rawat1؛ Harpreet Singh2؛ Tanuja Sheorey1
1Department of Mechanical Engineering, PDPM Indian Institute of Information Technology Design & manufacturing Jabalpur Dumna Airport Road, Dumna – 482005, India
2Department of Mechanical Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar
تاریخ دریافت: 27 فروردین 1403،  تاریخ بازنگری: 14 مرداد 1403،  تاریخ پذیرش: 25 مرداد 1403 
چکیده
The main aim of this research intended to optimise the injection moulding process parameters in order to mitigate the shrinkage of polypropylene (PP) spur gears. The methodology was used that integrated experimental approaches with artificial neural networks (ANN) and Taguchi methods to determine the most optimal combination of injection moulding 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 ANN model was then used to predict 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 moulding was carried out using the optimal combination of injection moulding parameters.
کلیدواژه‌ها
Injection moulding؛ Polypropylene gear؛ Shrinkage؛ Artificial neural network؛ Optimisation
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