Models to Predict the Viscosity of Metal Injection Molding Feedstock Materials as Function of Their Formulation

Joamin Gonzalez-Gutierrez, Ivica Duretek, Christian Kukla, Andreja Poljšak, Marko Bek, Igor Emri, Clemens Holzer

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)
411 Downloads (Pure)

Abstract

The viscosity of feedstock materials is directly related to its processability during injection molding; therefore, being able to predict the viscosity of feedstock materials based on the individual properties of their components can greatly facilitate the formulation of these materials to tailor properties to improve their processability. Many empirical and semi-empirical models are available in the literature that can be used to predict the viscosity of polymeric blends and concentrated suspensions as a function of their formulation; these models can partly be used also for metal injection molding binders and feedstock materials. Among all available models, we made a narrow selection and used only simple models that do not require knowledge of molecular weight or density and have parameters with physical background. In this paper, we investigated the applicability of several of these models for two types of feedstock materials each one with different binder composition and powder loading. For each material, an optimal model was found, but each model was different; therefore, there is not a universal model that fits both materials investigated, which puts under question the underlying physical meaning of these models.
Original languageEnglish
Pages (from-to)129- 146
Number of pages17
JournalMetals : open access journal
Volume6
Issue number6
DOIs
Publication statusPublished - 28 May 2016

Keywords

  • feedstock
  • metal injection molding
  • models
  • polypropylene
  • polyoxymethylene
  • polymer blends
  • powder content
  • rheology
  • stainless steel
  • viscosity

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