Abstract
A new technique is presented for measuring the yarn deformations induced by the complete manufacturing process of woven composites. This approach relies on a well suited relative analysis using Digital Volume Correlation (Mendoza et al., 2018). Two pairs of composite samples observed through high-resolution X-ray computed tomography are used as illustration. The measured differences between samples allows identifying a clear typology of strain patterns. They are in good agreement with phenomena known by experts but whose analysis is traditionally performed through manual inspection of tomographic data. For such reasons, these results are exploited using machine learning techniques so as to automate the unsupervised identification of these strain patterns. These results show the potential of the method in terms of automated inspection of 3D woven textiles for quality control, or for fine adjustment of manufacturing parameters.
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