Parts geometrical and dimensional error for a machining process can be attributed to several factors, including tool wear, thermal deformation, the machine tool positioning error and force-induced process error. Although the latter two factors are often more significant, their effect on the parts accuracy is more elusive and difficult to predict due to their inherent statistical dispersion property. It is therefore the subject of this investigation to quantitatively relate the parts error to machine tool spatial error and process-induced errors. Through root mean square calculation, a part error model is established by combining the machine tool positioning error, work vibration and tool vibration. The part error model considers two ranges of surface error consisting of surface roughness and cutting depth error of a machined plate. Using milling process as an example, the part error is predicted and compared with measurement result. The validity of this model is verified through a series of milling experiments under various cutting conditions.

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