Improving Error Models of Machine Tools with Metrology Data
Improving Error Models of Machine Tools with Metrology Data
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DOI：
10.1016/j.procir.2016.07.053
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. Flynn;J. Muelaner;V. Dhokia;S. Newman
中科院分区：
文献类型：
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作者：
J. Flynn;J. Muelaner;V. Dhokia;S. Newman
随着制造业采用各种计量解决方案，测量数据的可用性和数量正在增加。制造资源之间的连通性趋势还可以提供前所未有的测量数据的通信和利用机制。本研究旨在深入了解与可访问，丰富和可通信的制造计量数据相关的机会。提出的问题，并严格讨论有关的一个特定方面的制造计量，即机床精度验证和校准。具体而言，一种方法，用于相关的CMM零件测量到个别机床的几何误差源进行了说明。一种新的蒙特卡罗模拟为基础的方法是用来估计以前未测量的误差值，而不使用进一步的测试。使用这种方法，使用先前捕获的验证和校准数据来识别零件缺陷的可能原因的优点被示出。可以设想，所提出的方法可以用于指示有针对性的机床验证和校准例程，以减少监测机床健康所需的测试次数。通过使用有针对性的测试，减少了测量所有机床误差源的需求，从而可以通过减少机床停机时间来提高生产率。
As the manufacturing community embraces the use of a variety of metrology solutions, the availability and quantity of measurement data is increasing. The tendency towards connectedness between manufacturing resources may also provide a mechanism for communication and exploitation of metrology data like never before. This research aims to provide an insight into the opportunities that are associated with accessible, abundant and communicable manufacturing metrology data. Issues are raised and critically discussed in relation to one particular aspect of manufacturing metrology, namely, machine tool accuracy verification and calibration. Specifically, a methodology for relating CMM part measurements to individual machine tool geometric error sources is described. A novel Monte Carlo simulation-based method is used to estimate previously unmeasured error values without the use of further testing. Using this method, the advantage of using previously captured verification and calibration data to identify likely causes of part defects is shown. It is envisaged that the proposed method can be used to instruct targeted machine tool verification and calibration routines to reduce the number of tests required to monitor a machine tool's health. By using targeted tests, the need to measure all machine error sources is reduced, which in turn can improve productivity by reducing machine tool downtime.