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首页> 外文期刊>Journal of Materials Engineering and Performance >Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351
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Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351

机译：Al 7075-T7351平面铣削时预测表面粗糙度的人工神经网络

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摘要

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R_a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip's width, and chip's thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed.
机译：在这项工作中，开发了不同的人工神经网络（ANN）来预测7075-T7351铝合金的平面铣削加工后的表面粗糙度（R_a）值。选择了径向基（RBNN），前馈（FFNN）和广义回归（GRNN）网络，用于训练这些网络的数据来自使用高速铣床进行的实验。 Taguchi设计了实验，以减少实验的时间和成本。从这项研究中，使用均方误差百分比来衡量本研究中使用的每种人工神经网络的性能，并观察到FFNN取得了最佳结果。还计算了皮尔逊相关系数，以分析为具有选定输出（表面粗糙度）的网络选择的五个输入（切削速度，每齿进给量，切削轴向深度，切屑宽度和切屑厚度）之间的相关性。结果表明，切屑厚度与表面粗糙度以及切削速度之间具有很强的相关性。

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