Modeling the surface roughness and cutting force for turning
Modeling the surface roughness and cutting force for turning
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DOI：
10.1016/s0924-0136(00)00835-9
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发表时间：
2001-01-17
影响因子：
6.3
通讯作者：
Wu, CL
中科院分区：
文献类型：
--
作者：
Lin, WS;Lee, BY;Wu, CL
采用外展神经网络建立了表面粗糙度和切削力的预测模型。该网络由多个功能节点组成，这些功能节点通过使用预测平方误差（PSE）准则自配置以形成最优网络层次结构。一旦给定加工参数（切削速度、进给量和切削深度），该网络就可以预测表面粗糙度和切削力。为了验证溯因网络的准确性，本文采用回归分析方法建立了表面粗糙度和切削力的二次预测模型。两种模型的比较表明，溯因网络建立的预测模型比回归分析建立的预测模型更准确。实验结果证实了这种方法的有效性。(C)出版社：Elsevier Science B.V.
In this paper, an abductive network is adopted to construct a prediction model for surface roughness and cutting force. This network is composed of a number of functional nodes, which are self-configured to form an optimal network hierarchy by using a predicted square error (PSE) criterion. Once the process parameters (cutting speed, feed rate and depth of cut) are given, the surface roughness and cutting force can be predicted by this network. To verify the accuracy of the abductive network, regression analysis has been adopted in the paper to develop a second prediction model for surface roughness and cutting force. Comparison of the two models indicates that the prediction model developed by the abductive network is more accurate than that by regression analysis. Experimental results are provided to confirm the effectiveness of this approach. (C) 2001 Published by Elsevier Science B.V.