Scaling Approach for Thin Wall Injection Moulding by Dimensional Analysis

Thin-wall injection moulding has received increasing attention over the past few years due to economic and environmental concerns. However, due to the difficulties encountered in the thin-wall moulding process, systematic investigation is lacking in machine performance, mould design/manufacture requ...

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Veröffentlicht in:Iranian polymer journal 2006-01, Vol.15 (1), p.41-46
Hauptverfasser: Gharagheizi, Farhad, Angaji, Mahmood Torabi
Format: Artikel
Sprache:eng
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Zusammenfassung:Thin-wall injection moulding has received increasing attention over the past few years due to economic and environmental concerns. However, due to the difficulties encountered in the thin-wall moulding process, systematic investigation is lacking in machine performance, mould design/manufacture requirement, moulding characteristics, computer aided engineering (CAE) simulation, part quality and part design criteria. Furthermore, the combination of viscoelastic materials, complex moulding geometry and cyclic processing conditions has generated some problems, such as flow marks, polymer degradation, sink marks and warpage, under high-speed and high-pressure injection moulding. So it is very important to design, operate, and control thin-wall moulding optimally to guarantee part quality as well as reduce cost. In thin wall injection moulding processes, parts thinner than 1 mm are produced using high injection pressures and velocities. Modelling has not been successful in predicting process physics during moulding. A dimensional analysis is performed, considering the most relevant variables of the process, the geometry and the non-linear material properties. Using similarity analysis with the material and process related dimensionless groups, the process is scaled by reducing the thickness. The scaled dimensionless groups are used to find relations between process conditions, material properties and other physical parameters, which lead to reasonable conclusions. Results fit experimental data well.
ISSN:1026-1265