The inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its digital design model. Scan data, however, are typically very large scale (i.e., many points), unorganized, noisy, and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for denoising and reduction in scan data by extended geometric filter. The proposed method is applied directly on the scanned points and is automatic, fast, and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.
Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection
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Miropolsky, A., and Fischer, A. (September 4, 2009). "Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection." ASME. J. Manuf. Sci. Eng. October 2009; 131(5): 051001. https://doi.org/10.1115/1.3207738
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