| 基于砂带磨削的铝合金磨削力和表面粗糙度的研究 |
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| 引用本文： | 王福明,黄云.基于砂带磨削的铝合金磨削力和表面粗糙度的研究[J].机床与液压,2008,36(1):43-45. |
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| 作者姓名： | 王福明 黄云 |
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| 作者单位： | 重庆大学机械工程学院,重庆,400044;重庆市材料表面精密加工及成套装备工程技术研究中心,重庆,400021 |
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| 摘 要： | 建立了采用人工神经网络方法预测砂带磨削铝合金时磨削力和磨削表面粗糙度的分析模型.此模型可精确地描述砂带线速度、进给速度以及磨削深度对磨削力和磨削表面粗糙度的影响,实现了砂带磨削铝合金时磨削参数的优化.并可利用有限的试验数据得出整个工作范围内磨削力和表面粗糙度的预测值,大量减少了试验次数.
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| 关 键 词： | 人工神经网络算法 砂带磨削 铝合金 磨削力 表面粗糙度 |
| 文章编号： | 1001-3881（2008）1-043-3 |
| 收稿时间： | 2007-03-05 |
| 修稿时间： | 2007年3月5日 |
Research of Grinding Force and Surface Roughness of Aluminum Alloy Based on the Abrasive Belt Grinding |
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| WANG Fuming,HUANG Yun.Research of Grinding Force and Surface Roughness of Aluminum Alloy Based on the Abrasive Belt Grinding[J].Machine Tool & Hydraulics,2008,36(1):43-45. |
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| Authors: | WANG Fuming HUANG Yun |
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| Abstract: | An analysis model which can predict grinding force and surface roughness of aluminum alloy with abrasive belt grinding by using artificial neural networks was established. This model can accurately describe the effect of the abrasive belt velocity, feed velocity and grinding depth on grinding force and surface roughness, and it achieves the optimization of grinding parameters by abrasive belt grinding aluminum alloy. The predicting values of the surface roughness in the working range can be obtained by using the limited test data, thus a great number of test can be avoid. |
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| Keywords: | Artificial neural network algorithm Belt grinding Aluminum alloy Grinding force Surface roughness |
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