A new conceptual approach for systematic error correction in CNC machine tools minimizing worst case prediction error

Abstract

A new artifact-based method to identify the systematic errors in multi-axis CNC machine tools minimizing the worst case prediction error is presented. The closed loop volumetric error is identified by simultaneously moving the axes of the machine tool. The physical artifact is manufactured on the machine tool and later measured on a coordinate measuring machine. The artifact consists of a set of holes in the machine tool workspace at locations that minimize the worst case prediction error for a given bounded measurement error. The number of holes to be drilled depends on the degree of the polynomials used to model the systematic error and the number of axes of the machine tool. The prediction error is also function of the number and location of the holes. The feasibility of the method is first investigated for a two-axis machine to find the best experimental setting. Finally based on the two-axis case study, we extend the results to machine tools with any number of axes. The obtained results are very promising and require only a short time to produce the artifact