Digitizing uncertainty modeling for reverse engineering applications: regression versus neural networks
The coordinate measuring machine is one of the two types of digitizers most popularly used in reverse engineering. A number of factors affect the digitizing uncertainty, such as travel speeds of the probe, pitch values (sampling points), probe angles (part orientations), probe sizes, and feature sizes. A proper selection of these parameters in a digitization or automatic inspection process can improve the digitizing accuracy for a given coordinate-measuring machine. To do so, some empirical models or decision rules are required. This paper applies and compares the nonlinear regression analysis and neural network modeling methods in developing empirical models for estimating the digitizing uncertainty. The models developed in this research can aid error prediction, accuracy improvement, and operation parameter selection in computer-aided reverse engineering and automatic inspection.
coordinate measuring machines regression analysis computer-aided reverse engineering artificial neural network predictive process engineering computational manufacturing