A datum selective method in a part machining process

Datum selective is an important phase of setup planning, it has very important influence on the dimensional quality of parts in a machining process. Current approaches of machining process datum selective are experience-based that due to adopt higher machining process cost to ensure final part quality. This research aims to develop a methodology to obtain an optimal machining datum, which can satisfy the quality characteristic and minimize the expected value of the sum of machining costs in a machining process at the same time. In this paper, a physical process model is adopted to describe the variation propagation effect of datum selective throughout all stages in a machining process and Bayesian estimation method is used to estimate the corresponding fixture layout variation. Based on the process model and Bayesian estimation, a quantitative datum selective evaluation method driven by cost constraint is proposed to clarify what is optimality of datum selective. At last, an optimal datum is obtained through a dynamic programming problem solution. And then, a case study is provided to illustrate the validity and the significance of the proposed datum selective method.