Finite element modeling of the filament winding process

Liyang Zhao, Susan C. Mantell, David Cohen, Reed McPeak

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

A finite element model of the wet filament winding process was developed. In particular, a general purpose software for finite element analysis was used to calculate the fiber volume fraction under different process conditions. Several unique user defined subroutines were developed to modify the commercial code for this specific application, and the numerical result was compared with experimental data for validation. In order to predict the radial distribution of the fiber volume fraction within a wet wound cylinder, three unique user defined subroutines were incorporated into the commercial finite element code: a fiber consolidation/compaction model, a thermochemical model of the resin and a resin mixing model. The fiber consolidation model describes the influence of the external radial compaction pressure of a new layer as it is wound onto the surface of existing layers. The thermochemical model includes both the cure kinetics and viscosity of the resin. This model analyzes the composite properties and tracks the viscosity of the resin, which is a function of the degree of cure of the resin. The resin mixing model describes the mixing of "old" and "new" resin as plies are compacted. Validations were made by comparing image analysis data of fiber volume fraction in each ply for filament wound cylinders with the FEM results. The good agreement of these comparisons demonstrated that the FEM approach has can predict fiber volume fraction over a range of winding conditions. This approach, then, is an invaluable tool for predicting the effects of winding parameters on cylinder structural quality.

Original languageEnglish (US)
Pages (from-to)499-510
Number of pages12
JournalComposite Structures
Volume52
Issue number3-4
DOIs
StatePublished - May 2001

Bibliographical note

Funding Information:
This research was supported by grants from Alliant Techsystems and the University of Minnesota Supercomputer Institute.

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