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

Document Type : Special Issue: Mechanics of Advanced Fiber Reinforced Composite Structures

Authors

1 Department of Mechanical Engineering, PDPM Indian Institute of Information Technology Design & manufacturing Jabalpur Dumna Airport Road, Dumna – 482005, India

2 Department of Mechanical Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar– 144008, India

Abstract

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.

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Main Subjects

References

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Mechanics of Advanced Composite Structures
Volume 12, Issue 2 - Serial Number 25
Special Issue on Mechanics of Advanced Fiber-Reinforced Composite Structures: Celebrating the 50th Anniversary of Semnan University, Handled by the Esteemed Journal Editor, Prof. Dr. Mavinkere Rangappa Sanjay - In Progress
August 2025
Pages 249-260

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