Optimizing Glass Fiber Molding Process Design by Reverse Warping.

Materials (Basel)

Key Laboratory of Intelligent Manufacturing Technology, Shantou University, Ministry of Education, Shantou 515063, China.

Published: March 2020

The purpose of this study is to clarify the influence of changes in glass fiber properties on warpage prediction, and to demonstrate the importance of accurate material property data in the numerical simulation of injection molding. In addition, this study proposes an optimization method based on the reverse warping agent model, in which the thermal conductivity of the plastic material is reduced, and the solidified layer on the surface of the mold is reduced and transferred from the molding material to the mold wall. This means that by the end of the cooling phase, the shrinkage of the molten zone within the component will continue, resulting in warpage. Based on the optimal process parameters, the sensitivity of the warpage prediction to the relationship between the two most important material properties, the glass fiber and holding pressure time, was analyzed. The material property model constants used for numerical simulations can sometimes vary significantly due to inherent experimental measurement errors, the resolution of the test device, or the manner in which the curve fit is performed to determine the model constants. This model has been developed to intelligently determine the preferred processing parameters and to gradually correct the details of the molding conditions. Thus, the cavity is separated in the critical warpage region, and a new cavity geometry with a reverse warped profile is placed into the mold base slot. The results show that the hypothetical and reasonable variation of the glass fiber model constant and the holding pressure time relationship may significantly affect the magnitude of the warpage prediction of glass fiber products. The greatest differences were found as a result of the warping orientation of the glass fiber material.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084986PMC
http://dx.doi.org/10.3390/ma13051151DOI Listing

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