Developing empirical models from observational data using artificial neural networks
Developing empirical models from observational data using artificial neural networks
复制标题
使用人工神经网络根据观测数据开发经验模型
DOI：
--
复制
发表时间：
1993
影响因子：
8.3
通讯作者：
KARL F. Arnold
中科院分区：
文献类型：
--
作者：
S. Yerramareddy;S. Lu;KARL F. Arnold
计算机集成制造要求在计算机上实现制造过程的模型。在工艺规划过程中，过程模型是设计自适应控制系统和选择最优参数所必需的。从机械加工科学原理发展而来的机械模型对于在计算机上实现是有用的。然而，尽管在机械过程建模方面取得了进展，但许多制造过程还没有准确的模型可用。从实验数据得到的经验模型在制造过程建模中仍然发挥着重要作用。通常，统计回归技术被用于开发这样的模型。然而，这些技术有几个缺点。回归模型的结构(有效项)需要事先确定。随着新数据的出现，这些技术不能用于逐步改进模型。鉴于传感器技术的进步，允许经济地在线收集制造数据，这一限制尤其重要。在本文中，我们探索使用人工神经网络(ANN)从加工过程的实验数据中建立经验模型。将这些模型与多项式回归模型进行比较，以评估人工神经网络作为计算机集成制造的建模工具的适用性。
Computer-integrated manufacturing requires models of manufacturing processes to be implemented on the computer. Process models are required for designing adaptive control systems and selecting optimal parameters during process planning. Mechanistic models developed from the principles of machining science are useful for implementing on a computer. However, in spite of the progress being made in mechanistic process modeling, accurate models are not yet available for many manufacturing processes. Empirical models derived from experimental data still play a major role in manufacturing process modeling. Generally, statistical regression techniques are used for developing such models. However, these techniques suffer from several disadvantages. The structure (the significant terms) of the regression model needs to be decided a priori. These techniques cannot be used for incrementally improving models as new data becomes available. This limitation is particularly crucial in light of the advances in sensor technology that allow economical on-line collection of manufacturing data. In this paper, we explore the use of artificial neural networks (ANN) for developing empirical models from experimental data for a machining process. These models are compared with polynomial regression models to assess the applicability of ANN as a model-building tool for computer-integrated manufacturing.