Abstract:
This study explores the effectiveness of non-linear regression and artificial neural network (ANN) in predicting percentage shrinkage during plastic injection molding processes for HDPE material. By analyzing critical process parameters such as mold temperature, melt temperature and injection pressure, the research aims to identify optimal modeling techniques to improve % shrinkage accuracy so that better parts can be produced. A comparative analysis has been done between non-linear regression (NLR) and learning-ANN which shows understandings into the accuracy and limitations of each type of investigation method. Outcomes shows that while learning-ANN models offer greater adaptability, non-linear regression gives a more interpretable approach, having reliable shrinkage predictions with minimized error.
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