MODELLING OF ADDITIVE MANUFACTURING - COMPRESSION MOLDING PROCESS USING COMPUTATIONAL FLUID DYNAMICS

Berin Šeta, Marco Brander, Md Tusher Mollah, Vipin Kumar, Deepak Kumar Pokkalla, Seokpum Kim, Jon Spangenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A computational fluid dynamics model has been developed to predict the behavior of a printed strands during a novel material extrusion additive manufacturing and compression molding process. While the traditional additive manufacturing process enables control over the fiber orientation within the part, it would also result in high void content. On the other hand, compression molding produces parts with low porosity levels at rapid processing cycle time but lacks control over the microstructure. The novel additive manufacturing - compression molding (AM-CM) integrating both these processes offers control over both the microstructure and porosity to manufacturing high performance composite parts. The numerical model developed here enables to analyze the effect of processing parameters on the behaviour of printed layer that is subsequently compressed and to determine the optimal printing parameters for highperformance composite part design.

Original languageEnglish
Title of host publication2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
PublisherAmerican Society for Precision Engineering, ASPE
Pages119-123
Number of pages5
ISBN (Electronic)9781713859192
StatePublished - 2022
Event2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing - Knoxville, United States
Duration: Jul 11 2022Jul 14 2022

Publication series

Name2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing

Conference

Conference2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
Country/TerritoryUnited States
CityKnoxville
Period07/11/2207/14/22

Funding

The authors would like to acknowledge the support of the Innovation Fund Denmark (Grant no. 0223-00084B).

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