Abstract
Abstract In 2D or 3D tolerance analysis, a dimension chain involves a complex propagation of random deviations from individual parts to the controlled assembly requirement. This can be calculated by setting up complex mathematical models, possibly with the help of CAD-based software tools. The paper studies the feasibility of an alternative approach, which uses measurements on assemblies of 3D printed parts to estimate the coefficients of a linearized error propagation model. The analysis is structured as a designed experiment, where each dimension is varied in two levels resulting in two different printed parts. The experimental plan consists of building assemblies from selected combinations of parts and measuring the assembly dimension on each combination. Among the possible designs of the plan, the paper compares two methods based on finite differences and least squares. The comparison includes both simulations with randomly generated dimension chains and physical tests on simple cases. The results show that a least squares plan allows a significant improvement in the accuracy on the estimation of model coefficients compared to a finite difference plan, with just a marginally higher number of assembly combinations. However, dimensional and geometric deviations in the 3D printing process are shown to play a critical role, and criteria are suggested to reduce or compensate them.
Published Version
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