Manufacturing error and assembly error should be taken into consideration during evaluation and analysis of accurate product performance in the design phase. Traditional tolerance analysis methods establish error propagation model based on dimension chains with tolerance values being regarded as error boundaries, and obtain the limit of target feature error through optimization methods or conducting statistical analysis with the tolerance domain being the boundary. As deviations of the tolerance feature (TF) on degrees of freedom (DOF) have coupling relations, accurate deviations on all DOF may not be obtained, even though these deviations constitute the basis for product performance analysis. Therefore, taking the widely used shaft-hole fit as an example, a pose decoupling model of the axis TF was proposed based on an area coordinate system. This model realized decoupling analysis of any pose of the axis TF within the tolerance domain. As proposed by the authors, by combining a tolerance analysis model based on tracking local coordinate systems, ultimate pose analysis of the closed-loop system, namely the target feature, as well as statistical analysis could be further implemented. This method contributed to analysis of true product performance with arbitrary error in the product design phase from the angle of tolerance, therefore, shortening the product research and development cycle. This method is demonstrated through applying it to a real-life example.
Among current approaches to tolerance analysis in three-dimensional space, the Matrix method based on rigid body kinemat ics is widely used. However, this method ignores the impact a tolerance feature (TF) may get fro m the variat ions of TFs before it , therefore showing its demerits when applied to long and comp lex dimension chains. For this reason, this paper puts forward a new tolerance analysis model where t wo local coordinate systems are built at each TF: a nominal one based on the datum of cu rrent TF and a tracking one based on the variations of the prior TFs. Then the variations of all TFs along the dimension chain are accumulated. Co mpared with the Matrix method, the new approach generates results with more accuracy and higher efficiency.
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