NDLI: Overcoming data gathering errors for the prediction of mechanical properties on high precision foundries

Content Provider	IEEE Xplore Digital Library
Author	Nieves, J. Santos, I. Bringas, P.G.
Copyright Year	2010
Description	Author affiliation: S3 Lab, University of Deusto, Bilbao, Spain (Nieves, J.; Santos, I.; Bringas, P.G.)
Abstract	Mechanical properties are the attributes of a metal to withstand several loads and tensions. More accurately, ultimate tensile strength (UTS) is the force a material can resist until it breaks. The only way to examine this feature is the use of destructive inspections that render the casting invalid with the subsequent cost increment. In our previous researches we showed that the foundry process can be modelled as an expert knowledge cloud to anticipate the value of the UTS with outstanding results. Nevertheless, the data gathering phase for the training of machine learning classifiers is performed in a manual manner. In this paper, we present the use of Singular Value Decomposition (SVD) and Latent Semantic Analysis (LSA) with the aim of reducing the number of ambiguities and noise in the dataset. Furthermore, we have tested this approach comparing the results without this pre-processing step in order to illustrate the effectiveness of the proposed method.
Starting Page	1
Ending Page	6
File Size	278732
Page Count	6
File Format	PDF
ISBN	9781424496730
ISSN	21544824
e-ISBN	9781889335421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-19
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Support vector machines Matrix decomposition Gallium Foundries Semantics Transmission line matrix methods industrial processes optimization fault prediction machine-learning
Content Type	Text
Resource Type	Article

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