| Literature DB >> 33327451 |
Jue Hu1,2, Hai Zhang2, Stefano Sfarra3, Claudia Sergi4, Stefano Perilli3, Clemente Ibarra-Castanedo2, Guiyun Tian1, Xavier Maldague2.
Abstract
Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.Entities:
Keywords: hybrid composites; infrared thermography; non-destructive testing; sparse pattern extraction
Year: 2020 PMID: 33327451 DOI: 10.3390/s20247159
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576