Measurements in production must be rapid, robust and automated. In this paper a new method is proposed to automatically extract features and parameters of screw threads via Hough transformation from 2D point clouds acquired from profile measuring machines. The described method can be used to automate many operations during screw thread prealignment and drastically reduce operator's influence on the measurement process resulting in lower measurement times and increased repeatability.
Determination of realistic uncertainty values in coordinate metrology is a challenging task due to the complexity of the implementation of numerical algorithms involved. Monte-Carlo error propagation is used to estimate the uncertainty of a position tolerance using least-squares criterion. In this paper all the required steps are sequentially performed using a number real-world datasets. Since no reference data sets are available for position tolerance evaluation hence drawings and numerical values of such data sets are proposed.
Measurements and inspection in production must be rapid, robust and automated. In this paper a new method is proposed to automatically detect screw threads in 3D density fields obtained from computed tomography measurement devices. The described method can be used to automate many operations during screw thread inspection process and drastically reduce operator's influence on the measurement process resulting in lower measurement times and increased repeatability.
Usage of scanning coordinate-measuring machines for inspection of screw threads has become a common practice nowadays. Compared to touch trigger probing, scanning capabilities allow to speed up the measuring process while still maintaining high accuracy. However, in some cases accuracy drastically depends on the scanning speed. In this paper a compensation method is proposed allowing to reduce the influence of inertia of the probing system while scanning screw threads on coordinate-measuring machines.
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