Publications

@article{chaves_automatic_2023,

title = {Automatic classification of pores in aluminum castings using machine learning},

author = {Deisy Chaves and Eduardo Fidalgo and Pablo Rodriguez Gonzalez and AI Fernández Abia and Enrique Alegre and Joaquín Barreiro},

url = {https://ruc.udc.es/dspace/handle/2183/33692},

year  = {2023},

date = {2023-01-01},

journal = {XLIV Jornadas de Automática},

pages = {849–854},

abstract = {This paper proposes automating the classification of porosity defects in aluminum parts manufactured by casting. Images of parts produced by traditional sand molding and the Binder Jetting (BJ) additive technique are analyzed. The method uses SIFT descriptors and BoVW features to train two SVM classifiers: one for detecting pores and another for classifying the type of porosity (gas-related or shrinkage-related). This automated approach improves inspection efficiency and accuracy compared to traditional manual methods.},

note = {Publisher: Universidade da Coruña. Servizo de Publicacións},

keywords = {casting manufacturing, Image classification, porosity detection, SVM classifiers},

pubstate = {published},

tppubtype = {article}

}