A neural-network-based methodology for the prediction of surface roughness in a turning process
A neural-network-based methodology for the prediction of surface roughness in a turning process
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DOI：
10.1007/s00170-003-1810-z
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
A. Kohli;U. S. Dixit
中科院分区：
文献类型：
--
作者：
A. Kohli;U. S. Dixit
提出了一种以保持器径向振动加速度为反馈的车削加工表面粗糙度神经网络预测方法。该方法使用很少的实验数据来训练和测试网络，从而预测表面粗糙度的上、最可能和下估计值。使用反向传播算法训练网络模型。学习率，隐藏层中的神经元数量，错误目标，以及训练和测试数据集的大小，以自适应的方式自动找到。由于训练和测试数据都是从实验中收集的，因此采用了数据过滤方案来去除错误数据。该方法的验证进行了干和湿车削钢使用高速钢和硬质合金刀具。据观察，本方法是能够利用小尺寸的训练和测试数据集进行准确的预测表面粗糙度。
A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.