Enhanced foundry production control

Authors:
Javier Nieves;Igor Santos;Yoseba K. Penya;Felix Brezo;Pablo G. Bringas
Affiliations:
S³Lab, University of Deusto, Bilbao, Basque Country;S³Lab, University of Deusto, Bilbao, Basque Country;eNergy Lab, University of Deusto, Bilbao, Basque Country;S³Lab, University of Deusto, Bilbao, Basque Country;S³Lab, University of Deusto, Bilbao, Basque Country
Venue:
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Year:
2010

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Abstract

Mechanical properties are the attributes that measure the faculty of a metal to withstand several loads and tensions. Specifically, ultimate tensile strength is the force a material can resist until it breaks and, thus, it is one of the variables to control in the foundry process. The only way to examine this feature is the use of destructive inspections that renders the casting invalid with the subsequent cost increment. Nevertheless, the foundry process can be modelled as an expert knowledge cloud upon which we may apply several machine learnings techniques that allow foreseeing the probability for a certain value of a variable to happen. In this paper, we extend previous research on foundry production control by adapting and testing support vector machines and decision trees for the prediction in beforehand of the mechanical properties of castings. Finally, we compare the obtained results and show that decision trees are more suitable than the rest of the counterparts for the prediction of ultimate tensile strength.