Title
Automatic determination of fiber-length distribution in composite material using 3D CT data
Abstract
Determining fiber length distribution in fiber reinforced polymer components is a crucial step in quality assurance, since fiber length has a strong influence on overall strength, stiffness, and stability of the material. The approximate fiber length distribution is usually determined early in the development process, as conventional methods require a destruction of the sample component. In this paper, a novel, automatic, and nondestructive approach for the determination of fiber length distribution in fiber reinforced polymers is presented. For this purpose, high-resolution computed tomography is used as imaging method together with subsequent image analysis for evaluation. The image analysis consists of an iterative process where single fibers are detected automatically in each iteration step after having applied image enhancement algorithms. Subsequently, a model-based approach is used together with a priori information in order to guide a fiber tracing and segmentation process. Thereby, the length of the segmented fibers can be calculated and a length distribution can be deduced. The performance and the robustness of the segmentation method is demonstrated by applying it to artificially generated test data and selected real components.
Year
DOI
Venue
2010
10.1155/2010/545030
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
image enhancement algorithm,approximate fiber length distribution,length distribution,fiber length distribution,segmented fiber,single fiber,ct data,automatic determination,iterative process,fiber-length distribution,composite material,fiber length,development process,image analysis
Computer vision,Fiber,Iterative and incremental development,Segmentation,Computer science,Stiffness,A priori and a posteriori,Algorithm,Robustness (computer science),Test data,Artificial intelligence,Tracing
Journal
Volume
Issue
ISSN
2010,
1
1687-6180
Citations 
PageRank 
References 
1
0.37
1
Authors
6
Name
Order
Citations
PageRank
Matthias Teßmann160.81
Stephan Mohr210.37
Svitlana Gayetskyy310.70
Ulf Hassler462.24
Randolf Hanke510.37
Günther Greiner659880.74