On the Flatness Uncertainty Estimation Based on Data Elimination

Abstract

Coordinate measuring machine (CMM) plays a more and more important role in the flatness evaluation. The pre-processing for eliminating error data is an important process to improve the quality of flatness evaluation. So, flatness uncertainty estimation becomes a key problem. To solve the problem, a flatness uncertainty estimation method based on data elimination is presented. Firstly, a method for excluding the error data based on statistical theory is expatiated with the consideration of probability theory and mathematical statistics. Secondly, details of calculating flatness uncertainty based on least-square method are analyzed. Then the measured 3-dimensional data are processed so as to put the excluding method into use. So the error data can be deleted and the flatness uncertainty can be decreased and can achieve a good evaluation quality. Finally, an example is given and the result shows that the method proposed in this paper is feasible.

Data preprocessing for flatness measurements of large precision workpieces

“…Furthermore, the lowest and highest points on the surface are in agreement. The coordinates of the lowest point on the grid are (1,6), and the coordinates of the highest point on the grid are (8, 1) and (1,11).…”

“…Regarding the research work on flatness error measurement of large-size workpiece planes, relevant research mainly focuses on the direction of flatness error assessment methods [9][10]. Moreover, the data preprocessing methods for measuring large-sized planes are different, and there is relatively little research work in related areas [11][12]. In order to improve the measurement accuracy of flatness error of large-sized and high-precision workpieces, this paper proposes a new data preprocessing method based on MSE for measuring flatness by pitch method.…”

“…Figure 2. Grid nodes.Based on Formulas (8) to(11), it can be determined that for m×n grid nodes, m×n linear equations can be obtained regarding zi, j. In each linear equation, αi, j and βi, j are known values, forming a system of m×n linear equations.…”