A new artificial neural network approach to modeling ball-end milling
A new artificial neural network approach to modeling ball-end milling
复制标题
一种新的人工神经网络方法来建模球头铣削
DOI：
10.1007/s00170-009-2217-2
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Gadallah
中科院分区：
文献类型：
--
作者：
H. El;J. Briceno;M. Gadallah
径向基网络（RBN）是一种特殊类型的人工神经网络（ANN），被引入到机械加工过程建模与仿真领域。这种前馈三层完全互连的神经网络被成功地用于建立球头铣削加工条件（输入）和工艺参数（输出）之间的关系。选择一组四个关键的输入参数来表示切削条件，而四个重要特征的瞬时切削力被用作输出集。实验进行训练，以及验证和评估所提出的网络的性能。此外，还结合工业中典型的加工场景进行了案例研究，对模型进行了测试和验证。一个非常好的协议之间观察到的新模型预测的力量和他们的实验同行，从而验证了新的方法。
Radial basis network (RBN), a special type of artificial neural networks (ANN), is introduced to the field of machining process modeling and simulation. This feed-forward three-layer fully interconnected neural network is successfully used to establish the relationship between the machining conditions (inputs) and process parameters (outputs) for the case of ball end milling. A set of four key input parameters is selected to represent the cutting conditions, while four important characteristics of the instantaneous cutting force are used as the output set. Experiments are conducted to train as well as to validate and assess the performance of the proposed network. In addition, a case study, consisting of a typical machining scenario found in industry, is performed to test and verify the model. A very good agreement is observed between the forces predicted by the new model and their experimental counterparts, thus validating the new approach.