Mechanical Properties Prediction in High-Precision Foundry Production

被引:11
|
作者
Nieves, Javier [1 ]
Santos, Igor [1 ]
Penya, Yoseba K. [1 ]
Rojas, Sendoa [1 ]
Salazar, Mikel [1 ]
Bringas, Pablo G. [1 ]
机构
[1] Deusto Technol Fdn, S3Lab, Bilbao, Basque Country, Spain
来源
2009 7TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, VOLS 1 AND 2 | 2009年
关键词
COMPRESSION;
D O I
10.1109/INDIN.2009.5195774
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Mechanical properties are the attributes of a metal to withstand several forces and tensions. Specifically, ultimate tensile strength is the force a material can resist until it breaks. The only way to examine this mechanical property is the employment of destructive inspections that renders the casting invalid with the subsequent cost increment. In a previous work we showed that modelling the foundry process as a probabilistic constellation of interrelated variables allows Bayesian networks to infer causal relationships. In other words, they may guess the value of a variable (for instance, the value of ultimate tensile strength). Against this background, we present here the first ultimate tensile strength prediction system that, upon the basis of a Bayesian network, is able to foresee the values of this property in order to correct it before the casting is made. Further, we have tested the accuracy and error rate of the system with data of a real foundry.
引用
收藏
页码:31 / 36
页数:6
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