Signature of Machine Tool Errors on Surface Texture by DDS

Abstract

The dynamic effect of machine tool errors leaves its signature on machined surfaces. This paper proposes an application of data dependent systems (DDS) methodology to recover this signature from the surfaces in a comprehensive manner and provides a realistic basis for machine-tool acceptance. The approach is illustrated by experimental results from three different lathes. It is shown that relative contributions due to the feed wavelength reveal the dynamic performance of the machine tools. The deviation from the nominal and the spread of the estimated feed wavelength quantify the accuracy and rigidity, the feed wavelength being precisely maintained with minimal spread in a machine tool with high accuracy and high rigidity. Presence of other wavelengths and their relative contribution to the RMS surface roughness serve as quantitative indicators of other machine tool errors.