Statistical geometric computation on tolerances for dimensioning

Xu, Songgang; Keyser, John

doi:10.1016/j.cad.2015.06.012

Cited by 13 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include the actual mating envelope [59], vectorial tolerancing [60], quantifier and virtual boundary [61], and the T-maps in progressively more detailed definitions [62][63][64][65][66]. On the opposite end, other approaches try to reduce the complexity involved by geometric tolerances by special assumptions or approaches; examples include measurements on prototypes in the study of an electrical device [67] and the definition of variables with multivariate normal distributions in an allocation strategy considering designer's preferences [68].…”

Section: Literature Reviewmentioning

confidence: 99%

Allocation of geometric tolerances in one-dimensional stackup problems

Armillotta

2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Many tolerancing problems on mechanical assemblies involve a functional requirement depending on a chain of parallel dimensions on individual parts. In these one-dimensional cases, simple methods are available for the analysis and the allocation of dimensional tolerances. However, they are difficult to extend to geometric tolerances, which must be translated into equivalent dimensional tolerances; this allows the analysis but makes the allocation generally impossible without Monte Carlo simulation and complex search strategies. To overcome this difficulty, the paper proposes a way of dealing directly with geometric tolerances in the allocation problem. This consists in expressing the functional requirement as a linear model of geometric tolerances rather than equivalent dimensional tolerances; the coefficients of the model (sensitivities) are calculated considering both the dimension chain and the standard definition of the geometric tolerances. The approach can be combined with any constrained optimization method based on sensitivities. The optimal scaling method, previously proposed for dimensional tolerances, is extended to geometric tolerances and used in two examples to demonstrate the simplicity of the overall workflow and the quality of the optimal solution.

show abstract