A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

被引：0
作者：
M. Fernández-Delgado
M. Reboreda
E. Cernadas
S. Barro
机构：
[1] University of Santiago de Compostela,Intelligent Systems Group, Department of Electronics and Computer Science
[2] Troqueles y Moldes de Galicia S.A. (TROMOSA),undefined
来源：
Neural Computing and Applications | 2010年 / 19卷
关键词：
Automotive industry; Plastic injection mold; Support vector regression; Radial basis function; Multi-layer perceptron; Generalized regression neural networks; Cascade correlation; K-nearest neighbors; Generalized ART;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In the industrial environment, specifically in the automotive industry, an accurate prediction of execution times for each production task is very useful in order to plan the work and to optimize the human, technical and material resources. In this paper, we applied several regression neural networks to predict the execution times of the tasks in the production of parts for plastic injection molds. These molds are used to make a variety of car components in automotive industry. The prediction is based on the geometric features of the mold parts to be made. The accuracy of the predicted times is high enough to be used as a tool for the design stage of the mold parts, e.g. guiding the design process in order to get the lowest production time.
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收藏
页码：741 / 754
页数：13
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