Measurement uncertainty assessment for virtual assembly

Abstract

Virtual assembly (VA) is a method for datum definition and quality prediction of assemblies considering local form deviations of relevant geometries. Point clouds of measured objects are registered in order to recreate the objects’ hypothetical physical assembly state. By VA, the geometrical verification becomes more accurate and, thus, increasingly function oriented. The VA algorithm is a nonlinear, constrained derivate of the Gaussian best fit algorithm, where outlier points strongly influence the registration result. In order to assess the robustness of the developed algorithm, the propagation of measurement uncertainties through the nonlinear transformation due to VA is studied. The work compares selected propagation methods distinguished from their levels of abstraction. The results reveal larger propagated uncertainties by VA compared to the unconstrained Gaussian best fit. 1 Current trends in dimensional metrology and state-of-the-art datum definition and uncertainty assessment As quality demands on products increase, tolerance specifications for parts become more and more complex. With these challenging geometrical specifications, verification algorithms are required that represent the geometrical system more precisely. According to Nielsen (2003), in the last few decades, dimensional tolerances shrank due to improved manufacturing systems. However, the form deviations could not be reduced by the same extent. Therefore, their consideration should be intensified. A main deficit in the current International Organization for Standardization (ISO) standard for datum definition, ISO 5459 (Deutsches Institut für Normung e.V., 2011), is the lack of consideration of local form deviations for datum features. A datum feature is defined as a “real (non-ideal) integral feature used for establishing a single datum” (Deutsches Institut für Normung e.V., 2017, p. 2). Datum systems composed of three datum features mathematically define a coordinate system. This allows the definition of tolerance zones for extrinsic tolerances (Weißgerber and Keller, 2014). About 80 % of all measurement tasks require datum systems, so a further function-oriented datum system definition has a strong impact on geometrical verification. Hence, an assessment of the uncertainty for datum systems is of broad interest. Figure 1 shows a datum definition, where three perpendicular associated planes are considered in a nested approach. The primary datum constrains 3 degrees of freedom (DOF), the secondary datum 2 DOF and the tertiary datum 1 DOF (Gröger, 2015). 1.1 Concept of the virtual assembly In this paper, measurement data of physical objects are gathered from measurements using industrial computed tomography (CT). Registration is the action of aligning a data set relatively to another according to a datum definition in a common coordinate system. Virtual assembly (VA) comprises the consideration of local form deviations in the datum system computation. As shown in Fig. 1a, through VA, the physical workpiece contact is simulated by computing the contact points. The registration for VA is mathematically stated as an optimization problem, as introduced in Weißgerber and Keller (2014). In the following, matrices are marked as boldface capital, vectors in boldface italic, and scalar Published by Copernicus Publications on behalf of the AMA Association for Sensor Technology. 102 M. Kaufmann et al.: Measurement uncertainty assessment for virtual assembly Figure 1. Datum definition by nested registration, using associated planes (a), registration approach according to the default case in the current standard, (b) and registration approach according to virtual assembly (c). values in roman formatting. The signed distances dsig,i of i ∈ 1. . .N , i ∈ N, corresponding pairs of points { p1,i,p2,i } , with p1,i ∈ P 1 and p2,i ∈ P 2 determine the clearance between the surfaces to register. P 1 and P 2 are point sets of surfaces 1 and 2, respectively, as presented in Fig. 1b and c. The objective function f of the optimization problem is as follows: f ( tx, ty, tz,φ,θ,ψ )