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Prediction of Part Shrinkage for Injection Molded Crystalline Polymer via Cavity Pressure and Melt Temperature Monitoring

Authors :
Shia-Chung Chen
Bi-Lin Tsai
Cheng-Chang Hsieh
Nien-Tien Cheng
En-Nien Shen
Ching-Te Feng
Source :
Applied Sciences, Vol 13, Iss 17, p 9884 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

During an injection molding process, different parts of the molded material are subjected to various thermal–mechanical stresses, such as variable pressures, temperatures, and shear stresses. These variations form different pressure–temperature paths on the pressure–volume–temperature diagram. If these paths cannot converge at a specific target volume value during ejection, it often leads to different levels of shrinkage and associated warping, which pose a significant challenge for molders during mold trials and part quality control. The situation is particularly complicated when molding crystalline polymers because the degree of crystallinity depends on the processing conditions and may vary across different locations. In this study, we propose an innovative and practical approach to improving part shrinkage when molding crystalline polymers. For the first time, we utilized melt temperature profile monitoring rather than the previous mold temperature measurement to detect the crystallization process and determine the time taken to complete the crystallization at different melt and mold temperatures. In addition, we used response surface methodology to build a crystallization time prediction model. The feasibility of the prediction model was verified by determining the warpage of parts molded at various cooling times. Based on this model, we varied the packing pressure, packing time, and melt temperatures to determine the correlation with part shrinkage. Through regression analysis, the time-averaged solidification pressure values can accurately control part shrinkage. Two prediction models provide reasonable accuracy and efficiency for part shrinkage control, as demonstrated by subsequent verification experiments.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.5231434f3ea14d9d83b46eae34862a51
Document Type :
article
Full Text :
https://doi.org/10.3390/app13179884