Assessing solids conveying in injection moulding machines using coupled numerical simulations based on the discrete element method (DEM) and multibody systems (MBS)
Abstract
Design of single screw machines for polymer processing often focuses on the melt dominated areas of the screw. However, solids conveying is a key aspect for processes with high screw speeds, grooved feed sections, small screw diameters and material with low bulk density. In injection moulding, throughput limitations are highly relevant in packaging applications as due to low cooling times, plasticizing affects the cycle time. In addition, insufficient solids conveying is a primary cause for air residues in the melt and final product. Therefore, well-designed feed sections are required, especially as direct processing of regrind in recycling applications becomes more relevant due to governmental restrictions. Existing models for injection moulding are based on analytical equations and do not allow to assess new feed sections and feed opening designs, adapted to high screw speeds or regrind. In this paper, numerical simulations based on the Discrete Element Method (DEM), previously used in the field of extrusion, are carried out. In order to replicate the cyclic, superimposed rotation and translation of the screw, a coupled approach of DEM and Multibody Systems Simulation (MBS) is pursued. To verify the accuracy of such coupled simulations, a special test setup is added to a conventional injection moulding machine. Pure solids conveying is investigated, as DEM does not accommodate for large plastic deformations or melting. Different screw and intake designs as well as smooth and grooved barrels are investigated. Selected resins, pellet shapes and regrind are processed, varying the processing parameters and comparing the results to the simulation. The coupled approach replicates reality well in terms of throughput, confirming that DEM can be utilised to further investigate process phenomena and develop calculation models for solids conveying in injection moulding.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Jan Landgraeber; JL: Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Volker Schoeppner; VS: Writing – review, Supervision, Funding acquisition, Conceptualization. Florian Bruening; FB: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.
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Use of Large Language Models, AI and Machine Learning Tools: DeepL for translation. ChatGPT for programming of evaluation tools.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The project was supported by Deutsche Forschungsgemeinschaft (German Research Foundation, DFG) [project number 502078131].
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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