When dealing with precision in tolerancing of assembly systems, the modelling complexity of the mechanism increases. At first, one can distinguish the ID tolerancing approach that only concerns variations of dimension. Then, several models are defined to set 3D tolerances, considering that the form error is negligible compared to the orientational and translational variations. Finally, some approaches are proposed to take into account the form variations in the tolerancing of mechanisms. However, some modelling approaches considers the form error as a tolerance zone to add to the 3D tolerances as defined by Rule#l of the ASME standard, or ISO 8015. This paper proposes another point of view, considering the positioning of parts through contact points of their rigid deviation shapes under a defined assembly force and set-up. Rather than considering the positioning of a single part, here is proposed an approach of batch parts assembly by a statistical description of shapes. The result of the method is a statistical positioning error of one part on the other considering the form deviations of parts.
International audienceTolerancing of assembly mechanisms is a major interest in the product life cycle. One can distinguish several models with growing complexity, from 1-dimensional (1D) to 3-dimensional (3D) (including form deviations), and two main tolerancing assumptions, the worst case and the statistical hypothesis. This paper presents an approach to 3D statistical tolerancing using a new acceptance criterion. Our approach is based on the 1D inertial acceptance criterion that is extended to 3D and form acceptance. The modal characterisation is used to describe the form deviation of a geometry as the combination of elementary deviations (location, orientation and form). The proposed 3D statistical tolerancing is applied on a simple mechanism with lever arm. It is also compared to the traditional worst-case tolerancing using a tolerance zone
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.