Improving Error Models of Machine Tools with Metrology Data

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. (C) 2016 Published by Elsevier B.V.