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Knowledge of a machine tool axis to axis location errors allows compensationand correcting actions to be taken to enhance its volumetric accuracy. Severalprocedures exist, involving either lengthy individual test for each geometricerror or faster single tests to identify all errors at once. This study focuseson the closed kinematic Cartesian chain method which uses a single setup testto identify the eight link errors of a five axis machine tool. Theidentification is based on volumetric error measurements for different poseswith a non-contact measuring instrument called CapBall, developed in house. Inorder to evaluate the uncertainty on each identified error, a multi-outputMonte Carlo approach is implemented. Uncertainty sources in the measurement andidentification chain - such as sensors output, machine drift and frametransformation uncertainties - can be included in the model and propagated tothe identified errors. The estimated uncertainties are finally compared toexperimental results to assess the method. It shows that the effect of thedrift, a disturbance, must be simulated as a function of time the Monte Carloapproach. The machine drift is found to be an important uncertainty in sourcesfor the machine tested.

Adaptive Monte Carlo applied to uncertainty estimation in a five axis machine tool link errors identification