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Proceedings Paper

Cognitive high speed defect detection and classification in MWIR images of laser welding
Author(s): Yago L. Lapido; Jorge Rodriguez-Araújo; Antón García-Díaz; Gemma Castro; Félix Vidal; Pablo Romero; Germán Vergara
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Paper Abstract

We present a novel approach for real-time defect detection and classification in laser welding processes based on the use of uncooled PbSe image sensors working in the MWIR range. The spatial evolution of the melt pool was recorded and analyzed during several welding procedures. A machine learning approach was developed to classify welding defects. Principal components analysis (PCA) is used for dimensionality reduction of the melt pool data. This enhances classification results and enables on-line classification rates close to 1 kHz with non-optimized code prototyped in Python. These results point to the feasibility of real-time defect detection.

Paper Details

Date Published: 1 July 2015
PDF: 5 pages
Proc. SPIE 9657, Industrial Laser Applications Symposium (ILAS 2015), 96570B (1 July 2015); doi: 10.1117/12.2177890
Show Author Affiliations
Yago L. Lapido, AIMEN Technology Ctr. (Spain)
Jorge Rodriguez-Araújo, AIMEN Technology Ctr. (Spain)
Antón García-Díaz, AIMEN Technology Ctr. (Spain)
Gemma Castro, AIMEN Technology Ctr. (Spain)
Félix Vidal, AIMEN Technology Ctr. (Spain)
Pablo Romero, AIMEN Technology Ctr. (Spain)
Germán Vergara, New Infrared Technologies Ltd. (Spain)


Published in SPIE Proceedings Vol. 9657:
Industrial Laser Applications Symposium (ILAS 2015)
Mike Green; Cath Rose, Editor(s)

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