Raw data based image processing algorithm for fast detection of surface breaking cracks
- The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component’s life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non- contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks. Currently in laser thermographicThe aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component’s life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non- contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks. Currently in laser thermographic inspection lacks a compromising data processing algorithm which is necessary for the fast crack detection and also the analysis of data is done as part of post processing. In this work we introduced a raw data based image processing algorithm which results precise, better and fast crack detection. The algorithm we developed gives better results in both experimental and modeling data. By applying this algorithm we carried out a detailed investigation Variation of thermal contrast with crack parameters like depth and width. The algorithm we developed is applied for various surface temperature data from the 2D scanning model and also validated credibility of algorithm with experimental data.…
Autor*innen: | S. K. P. Krishna, N. Puthiyaveetil, R. Kidangan, Sreedhar Unnikrishnakurup, Mathias ZieglerORCiD, Philipp Myrach, Krishnan Balasubramaniam, B. Purushothaman |
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Dokumenttyp: | Beitrag zu einem Tagungsband |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | QNDE conference 2016 - Review of progress in quantitative nondestructive evaluation |
Jahr der Erstveröffentlichung: | 2017 |
Verlag: | AIP Publishing |
Verlagsort: | New York |
Jahrgang/Band: | 1806 |
Ausgabe/Heft: | 1 |
Erste Seite: | UNSP 140008, 1 |
Letzte Seite: | 9 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Laserthermografie; Risserkennung; Thermografie Crack Detection |
Veranstaltung: | QNDE conference 2016 - Review of progress in quantitative nondestructive evaluation |
Veranstaltungsort: | Atlanta, GA, USA |
Beginndatum der Veranstaltung: | 18.07.2016 |
Enddatum der Veranstaltung: | 22.07.2016 |
DOI: | 10.1063/1.4974723 |
ISSN: | 0094-243X |
ISBN: | 978-0-7354-1474-7 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 15.03.2017 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 07.07.2017 |