Five axis machine tool volumetric error prediction through an indirect estimation of intra- and inter-axis error parameters by probing facets on a scale enriched uncalibrated indigenous artefact

•Error motions and axis locations errors of a 5-axis machine tool are estimated.•Facets on the machine table are probed as indigenous uncalibrated artefact.•All machine errors are embedded into 3rd order cubic ordinary polynomials.•86 machine coefficients and the unknown artefact geometry are co-estimated.•The model predictive capability is assessed against CMM metrology data.

The volumetric accuracy of five-axis machine tools is affected by intra-axis geometric errors (error motions) and inter-axis geometric errors (axes relative position and orientation errors). Self-probing of uncalibrated facets on the existing machine tool table is proposed to provide the necessary data for the self-calibration of the machine error parameters and of the artefact geometry using an indirect approach. A set of 86 non-confounded coefficients are selected from the ordinary cubic polynomials used to model both the intra- and inter-axis errors. A scale bar is added to provide the isotropic scale factor. The estimated model is then used to predict the actual tool to workpiece position. Experimental trials are conducted on a five-axis horizontal machining centre using its original unmodified machine table as an artefact. For validation purposes only, the estimated artefact geometry is compared to accurate coordinate measuring machine (CMM) measurements. A study of the volumetric error predictive capability of the model for selected subsets of estimated error coefficients is also conducted.