Abstract
Injection molding is classified as one of the economical manufacturing processes for high volume production of plastic parts. However, it is a complex process, as there are many factors that could lead to process variations and thus the quality issues of final products. One common quality issue is the presence of shrinkage and its associated warpage. Part shrinkage is largely affected by molding conditions, as well as mold design and material properties. The main objective of this paper is to predict the shrinkage of injection molded parts under different processing parameters. The second objective is to facilitate the setup of injection molding machine and reduce the need for trial and error. To meet these objectives, an artificial neural network (ANN) model was presented in this study, to predict the part shrinkage from the optimal molding parameters. Molding parameters studied include injection speed, holding time, and cooling time. A Taguchi-based experimental study was conducted, to identify the optimal molding condition which can lead to the minimum shrinkages in the length and width directions. A L27 (33) orthogonal array (OA) was applied in the Taguchi experimental design, with three controllable factors and one non-controllable noise factor. The feedforward neural network model, trained in back propagation, was validated by comparing the predicted shrinkage with the actual shrinkage obtained from Taguchi-based experimental results. It demonstrates that the ANN model has a high prediction accuracy, and can be used as a quality control tool for part shrinkage in injection molding.
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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. However, the corresponding author appreciates the Caterpillar Fellowship supported from Bradley University.
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Abdul, R., Guo, G., Chen, J.C. et al. Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design. Int J Interact Des Manuf 14, 345–357 (2020). https://doi.org/10.1007/s12008-019-00593-4
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DOI: https://doi.org/10.1007/s12008-019-00593-4