| This dissertation presents an integrated statistical framework for optimization of Coordinate Measuring Machine (CMM) measurements based on Design of Experiments (DOE), Response Surface Model (RSM), and Analysis of Covariance (ANCOVA) principles. The framework takes into amount sampling method (i.e., form fitting criterion, sampling method, and sample size), surface deviations due to manufacturing process variations, CMM measurement performance, and CMM measurement uncertainty impact to verify the estimation of surface deviations. As a result, the proposed framework presents a valid model to fully explore the influence and interplay of these contributed factors, resulting in robust recommendations on optimal CMM inspection guidelines. The methodology has been demonstrated for inspection of flatness and circularity form tolerances. Extensions of the approach for future research are discussed. |
