Recent years have seen an increase in the adoption of geometric tolerances. It is often possible to find several geometric tolerances defined on a single part. However, this poses inspection issues: the values of the geometric error may be interrelated; therefore, the presence of multiple tolerances should be considered in inspection design. In this work, a methodology is proposed for planning CMM sampling strategies for multiple tolerances based on the minimization of inspection costs. A model for inspection costs is proposed, which takes into account the influence of the inspection strategy on measurement and inspection errors costs, both directly and through its impact on measurement uncertainty. The cost is then minimized by means of a suitable optimization algorithm, thus defining an optimal sampling strategy. The approach can be adopted both to optimize generic, uniform, sampling strategies, and to generate manufacturing specific strategies, which consider the manufacturing signature, i.e., the part shape deviation from design nominal inherent to a specific manufacturing process. The latter kind of strategies is shown to be the most effective to minimize costs. A case study which illustrates the methodology is presented.

Inspection strategies and multiple geometric tolerances

MORONI, GIOVANNI;PETRO', STEFANO
2012-01-01

Abstract

Recent years have seen an increase in the adoption of geometric tolerances. It is often possible to find several geometric tolerances defined on a single part. However, this poses inspection issues: the values of the geometric error may be interrelated; therefore, the presence of multiple tolerances should be considered in inspection design. In this work, a methodology is proposed for planning CMM sampling strategies for multiple tolerances based on the minimization of inspection costs. A model for inspection costs is proposed, which takes into account the influence of the inspection strategy on measurement and inspection errors costs, both directly and through its impact on measurement uncertainty. The cost is then minimized by means of a suitable optimization algorithm, thus defining an optimal sampling strategy. The approach can be adopted both to optimize generic, uniform, sampling strategies, and to generate manufacturing specific strategies, which consider the manufacturing signature, i.e., the part shape deviation from design nominal inherent to a specific manufacturing process. The latter kind of strategies is shown to be the most effective to minimize costs. A case study which illustrates the methodology is presented.
Proceedings of the 12th CIRP Conference on Computer Aided Tolerancing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/715747
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