Predicting Forming Forces and Lack of Volume with Data Mining Methods for a Flange Forging Process
Predicting Forming Forces and Lack of Volume with Data Mining Methods for a Flange Forging Process
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DOI：
10.7763/ijmo.2017.v7.613
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
N. Rasche;J. Langner;M. Stonis;B. Behrens
中科院分区：
文献类型：
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作者：
N. Rasche;J. Langner;M. Stonis;B. Behrens
在锻造行业，就像在许多其他经济部门一样，在执行实验试验之前模拟成形过程是很常见的。迭代模拟过程比只进行试验更经济，但仍然需要大量时间。使用真实参数进行模拟需要花费数小时。为了实现经济生产，提出了利用数据挖掘技术对模拟结果进行预测的思想。在本文中，介绍了四种不同的数据挖掘方法用于预测模拟法兰锻造过程的某些特征。采用人工神经网络、支持向量机、线性回归和多项式回归等方法对成形力和缺料量进行预测。这两个参数都是成功模拟锻造过程的重要参数。模拟结果表明，无论是锻造成形力还是模拟后的体积不足，人工神经网络都是最合适的。
In the forging industry, like in many other economic sectors, it is common to simulate forming processes before executing experimental trials. An iterative simulation process is more economic than trials only but still takes a lot of time. A simulation with realistic parameters takes many hours. For an economical production the idea of predicting some main results of the simulation by Data mining was developed. Within this paper, the use of four different Data mining methods for the prediction of certain characteristics of a simulated flange forging process are presented. The methods artificial neural network, support vector machine, linear regression and polynomial regression are used to predict forming forces and the lack of volume. Both are important parameters for a successful simulation of a forging process. Regarding both, forging forming forces and lack of volume after the simulation, it is revealed that an artificial neural network is the most suitable.