On prediction of friction coefficient using artificial neural networks

机译：基于人工神经网络的摩擦系数预测

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Friction plays very important role in machining. It can be used to dissipate energy generated in the cutting zone and it can be used to provide extra support specially when machining flexible parts or when there is an accessibility problem. This paper investigate the application of AI schemes to predict the friction coefficient on the contact face as an alternative to running time consuming experiments, taking into accounts factors like surface roughness, material properties, etc. The results are compared with experimentally obtained data.

机译：摩擦在加工中起着非常重要的作用。它可用于消散在切削区域中产生的能量，并且可用于在加工柔性零件或存在可及性问题时特别提供额外的支撑。本文考虑了表面粗糙度，材料特性等因素，研究了AI方案在预测接触面摩擦系数方面的应用，以替代耗时的实验，并将结果与实验所得数据进行了比较。

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来源
《Mechatronics and its Applications, 2009. ISMA '09》|2009年|1-6|共6页
会议地点 Sharjah(AE);Sharjah(AE)
作者
Deiab I.M.; Al Shammari A.T.;
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Mech. Eng. Dept., American Univ. of Sharjah, Sharjah, United Arab Emirates;

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关键词
cutting; friction; machining; mechanical engineering computing; neural nets; surface roughness; artificial neural networks; cutting zone; friction coefficient;

机译：切削;摩擦;机械加工;机械工程计算;神经网络;表面粗糙度;人工神经网络;切削区域;摩擦系数;

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On prediction of friction coefficient using artificial neural networks

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