"Purpose: For some years now, our research group has been developing a new methodology for automatic tolerance inspection starting from an acquired high-density 3D model. In this paper, with a view to grouping together all the information recognisable in a scanned object, a new data structure, called Recognised Geometric Model (RGM), is proposed. Based on this data structure, the evaluation of the non-idealities of the acquired object (form, orientation and location non-idealities) can be automatically carried out. Method: RGM is the result of an approach founded on the concepts of non-ideal feature and intrinsic nominal reference. The object to be inspected is segmented into a set of non-ideal features and, for each of them, one or more intrinsic nominal references are identified. An Intrinsic Nominal Reference is detected when a geometric property has been recognised to be common to a set of adjacent points in the 3D data set representing the acquired object. The recognition of these references from a scanned object is carried out based on some rules which, therefore, play a leading role in the definition of the domain of the representable entities within RGM. Result: New and old categories of form non-idealities are here defined and some procedures are proposed for a more robust process of verification of traditional tolerance categories (such as the straightness of a cylinder generatrix). Discussion & Conclusion:. When using the RGM, tolerances can be specified according to the set of available and recognisable intrinsic nominal references. This allows for the automatic geometric inspection of the workpiece. However, the approach here proposed does not rule out the possibility of querying the RGM data structure by explicit geometric product specifications, in order to gather some quantitative information concerning special intrinsic geometric parameters and\/or non-idealities."

Construction of a geometric reference model for automatic non-ideality evaluation of an acquired high-density workpiece

DI ANGELO, LUCA;DI STEFANO, PAOLO;
2011-01-01

Abstract

"Purpose: For some years now, our research group has been developing a new methodology for automatic tolerance inspection starting from an acquired high-density 3D model. In this paper, with a view to grouping together all the information recognisable in a scanned object, a new data structure, called Recognised Geometric Model (RGM), is proposed. Based on this data structure, the evaluation of the non-idealities of the acquired object (form, orientation and location non-idealities) can be automatically carried out. Method: RGM is the result of an approach founded on the concepts of non-ideal feature and intrinsic nominal reference. The object to be inspected is segmented into a set of non-ideal features and, for each of them, one or more intrinsic nominal references are identified. An Intrinsic Nominal Reference is detected when a geometric property has been recognised to be common to a set of adjacent points in the 3D data set representing the acquired object. The recognition of these references from a scanned object is carried out based on some rules which, therefore, play a leading role in the definition of the domain of the representable entities within RGM. Result: New and old categories of form non-idealities are here defined and some procedures are proposed for a more robust process of verification of traditional tolerance categories (such as the straightness of a cylinder generatrix). Discussion & Conclusion:. When using the RGM, tolerances can be specified according to the set of available and recognisable intrinsic nominal references. This allows for the automatic geometric inspection of the workpiece. However, the approach here proposed does not rule out the possibility of querying the RGM data structure by explicit geometric product specifications, in order to gather some quantitative information concerning special intrinsic geometric parameters and\/or non-idealities."
978-88-7784-328-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/88584
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