DocumentCode :
2336786
Title :
Machine-learning-based surface defect detection and categorisation in high-precision foundry
Author :
Pastor-López, Iker ; Santos, Igor ; Santamaría-Ibirika, Aitor ; Salazar, Mikel ; De-la-Peña-Sordo, Jorge ; Bringas, Pablo G.
Author_Institution :
S3Lab., Univ. of Deusto, Bilbao, Spain
fYear :
2012
fDate :
18-20 July 2012
Firstpage :
1359
Lastpage :
1364
Abstract :
Foundry is an important industry that supplies key castings to other industries where they are critical. Hence, foundry castings are subject to very strict safety controls to assure the quality of the manufactured castings. One of the type of flaws that may appear in the castings are defects on the surface; in particular, our work focuses in inclusions, cold laps and misruns. We propose a new approach that detects imperfections on the surface using a segmentation method that marks the regions of the casting that may be affected by some of these defects and, then, applies machine-learning techniques to classify the regions in correct or in the different types of faults. We show that this method obtains high precision rates.
Keywords :
casting; foundries; learning (artificial intelligence); moulding equipment; production engineering computing; categorisation; foundry castings; high-precision foundry; machine-learning techniques; machine-learning-based surface defect detection; manufactured castings; safety controls; Casting; Entropy; Foundries; Histograms; Image segmentation; Object segmentation; Standards;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-2118-2
Type :
conf
DOI :
10.1109/ICIEA.2012.6360934
Filename :
6360934
Link To Document :
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