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Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring

机译:基于电流传感器的进给切削力智能估计和刀具磨损状态监控

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摘要

Tool wear condition monitoring has the potential to play a critical role in ensuring the dimensional accuracy of the workpiece and prevention of damage to cutting equipment. It could also help in automating cutting processes. In this paper, the feed cutting force estimated with the aid of an inexpensive current sensor installed on the AC servomotor of a computerized numerical control tuning center is used to monitor tool wear condition. To achieve this, the feed drive system is modeled, using neuro-fuzzy techniques, to provide the framework for estimating the feed cutting force based on the feed motor current measured. Functional dependence of the feed cutting force on tool wear and cutting parameters are then expressed in the form of a difference equation relating variation in the feed cutting force to tool wear rate. The computerized system automatically compares successive feed cutting force estimates and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that the tool wear condition monitoring is effective and industrially applicable.
机译:刀具磨损状态监控在确保工件尺寸精度和防止损坏切割设备方面可能发挥关键作用。它还可以帮助自动化切割过程。在本文中,借助于安装在计算机数控调谐中心的交流伺服电机上的廉价电流传感器估算的进给切削力可用于监视刀具的磨损情况。为此,使用神经模糊技术对进给驱动系统进行建模,以提供基于测得的进给电机电流估算进给切削力的框架。进给切削力对刀具磨损和切削参数的功能依赖性然后以将进给切削力的变化与刀具磨损率相关的差分方程的形式表示。该计算机系统自动比较连续的进给切削力估计值,并确定加速刀具磨损的开始时间,以便发出更换刀具的请求。实验结果表明,刀具磨损状态监测是有效的,可在工业上应用。

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