Hexapod machines are emerging as a new type of CNC machine tools. Among other things, stringent calibration is an important means to improve their accuracy. Traditionally, to perform system calibration, one needs to measure a number of machine poses using an external measuring device. However, this process is often labor-intensive and invasive, and difficult for on-line calibration.
In this paper, a systematic way of self-calibrating a hexapod machine tool is introduced. By adding a small number of redundant internal sensors, errors of the hexapod machine tool can be measured. This approach has the potential of automatically producing accurate measurement data over the entire workspace of the system with an extremely fast measurement rate. Once the measurement data is available, a recursive filter is applied to estimate machine parameter errors from the predicted geometric errors, and to update the model residing in the machine controller. Thus, it is possible to dynamically calibrate and compensate for various types of machine errors including those induced by thermal and loading variations, without interrupting the normal operation of the machine tool. To verify the concept, preliminary experimental studies were conducted on a Stewart platform built at Florida Atlantic University.