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首页> 外文期刊>IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews >Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring
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Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring

机译:基于电流测量的进给切削力模糊估计-以智能刀具磨损状态监测为例

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摘要

It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neuro-fuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring applications.
机译:使用可靠且便宜的传感器来获得有关制造过程的有用信息,例如用于监控自动化加工的切削力,这一点非常重要。在本文中,使用廉价的电流传感器估算进给切削力,该电流传感器安装在计算机数控(CNC)车削中心的交流伺服电机上,并将结果应用于智能刀具磨损监测系统。该数学模型用于揭示进给切削力对进给电动机电流和进给速度的隐含依赖性。然后,仅使用电流测量,使用神经模糊网络来识别切削力。这种混合的数学模糊方法将减少建模不确定性和测量成本。最后,将估计的切削力应用于刀具磨损监测过程。成功的实验证明了该方法在各种刀具磨损监测应用中的鲁棒性和有效性。

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