Generating virtual textile composite specimens using statistical data from micro-computed tomography: 3D tow representations

Rinaldi, Renaud, Blacklock, Matthew, Bale, Hrishikesh, Begley, Matthew and Cox, Brian (2012) Generating virtual textile composite specimens using statistical data from micro-computed tomography: 3D tow representations. Journal of the Mechanics and Physics of Solids, 60 (8). pp. 1561-1581. ISSN 0022-5096

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Recent work presented a Monte Carlo algorithm based on Markov Chain operators for generating replicas of textile composite specimens that possess the same statistical characteristics as specimens imaged using high resolution x-ray computed tomography. That work represented the textile reinforcement by one-dimensional tow loci in three-dimensional space, suitable for use in the Binary Model of textile composites. Here analogous algorithms are used to generate solid, three-dimensional (3D) tow representations, to provide geometrical models for more detailed failure analyses. The algorithms for generating 3D models are divided into those that refer to the topology of the textile and those that deal with its geometry. The topological rules carry all the information that distinguishes textiles with different interlacing patterns (weaves, braids, etc.) and provide instructions for resolving interpenetrations or ordering errors among tows. They also simplify writing a single computer program that can accept input data for generic textile cases. The geometrical rules adjust the shape and smoothness of the generated virtual specimens to match data from imaged specimens. The virtual specimen generator is illustrated using data for an angle interlock weave, a common 3D textile architecture.

Item Type: Article
Uncontrolled Keywords: Textile composite, Stochastic microstructure, Virtual specimen, Topology, Geometry
Subjects: H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Becky Skoyles
Date Deposited: 10 Jul 2018 13:40
Last Modified: 11 Oct 2019 20:00

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