NDLI: Collective classification for the prediction of microshrinkages in foundry production

Content Provider	IEEE Xplore Digital Library
Author	Santos, Igor Nieves, Javier Laorden, Carlos Sanz, Borja Bringas, Pablo Garcia
Copyright Year	2012
Description	Author affiliation: S3Lab, DeustoTech - Computing, University of Deusto, Bilbao, Spain (Santos, Igor; Nieves, Javier; Laorden, Carlos; Sanz, Borja; Bringas, Pablo Garcia)
Abstract	Microshrinkages are known as probably the most difficult defects to avoid in high-precision foundry. This failure is not corrigible, with the subsequent cost increment. Modelling the foundry process as an expert knowledge cloud allows machine learning algorithms to foresee the value of a certain variable, in this case, the probability that a microshrinkage appears within a foundry casting. However, this approach needs to label every instance to generate the model that will classify the castings. In this paper, we present a new approach for detecting faulty castings through collective classification to reduce the labelling requirements of completely supervised approaches. Collective classification is a type of semi-supervised learning that optimises the classification of partially-labelled data. We performed an empirical validation demonstrating that the system maintains a high accuracy rate while the labelling efforts are lower than when using supervised learning.
Starting Page	1
Ending Page	6
File Size	5596180
Page Count	6
File Format	PDF
ISBN	9781467344975
ISSN	21544824
e-ISBN	9781889334479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Casting Foundries Training Accuracy Metals Labeling
Content Type	Text
Resource Type	Article

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