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

TL;DR

This paper introduces an adaptive Monte Carlo method to quantify uncertainties in identifying link errors of a five-axis machine tool, incorporating measurement noise, drift, and transformation errors for improved accuracy.

Contribution

It develops a novel adaptive Monte Carlo approach that models and propagates various uncertainty sources in the error identification process of a five-axis machine tool.

Findings

Machine drift significantly impacts error uncertainty estimates.

The adaptive Monte Carlo method effectively includes drift as a time-dependent disturbance.

Uncertainty estimates closely match experimental results.

Abstract

Knowledge of a machine tool axis to axis location errors allows compensation and correcting actions to be taken to enhance its volumetric accuracy. Several procedures exist, involving either lengthy individual test for each geometric error or faster single tests to identify all errors at once. This study focuses on the closed kinematic Cartesian chain method which uses a single setup test to identify the eight link errors of a five axis machine tool. The identification is based on volumetric error measurements for different poses with a non-contact measuring instrument called CapBall, developed in house. In order to evaluate the uncertainty on each identified error, a multi-output Monte Carlo approach is implemented. Uncertainty sources in the measurement and identification chain - such as sensors output, machine drift and frame transformation uncertainties - can be included in the model and propagated to the identified errors. The estimated uncertainties are finally compared to experimental results to assess the method. It shows that the effect of the drift, a disturbance, must be simulated as a function of time the Monte Carlo approach. The machine drift is found to be an important uncertainty in sources for the machine tested.