An iterative statistical tolerance analysis procedure to deal with linearized behavior models

Abstract

Tolerance analysis consists of analyzing the impact of variations on the mechanism behavior due to the manufacturing process. The goal is to predict its quality level at the design stage. The technique involves computing probabilities of failure of the mechanism in a mass production process. The various analysis methods have to consider the component's variations as random variables and the worst configuration of gaps for over-constrained systems. This consideration varies in function by the type of mechanism behavior and is realized by an optimization scheme combined with a Monte Carlo simulation. To simplify the optimization step, it is necessary to linearize the mechanism behavior into several parts. This study aims at analyzing the impact of the linearization strategy on the probability of failure estimation; a highly over-constrained mechanism with two pins and five cotters is used as an illustration for this study. The purpose is to strike a balance among model error caused by the linearization, computing time, and result accuracy. In addition, an iterative procedure is proposed for the assembly requirement to provide accurate results without using the entire Monte Carlo simulation.

A novel tolerance analysis method for three-dimensional assembly

“…Statistical analysis is a more demanding way by permitting a small fraction of assemblies to not assemble or function as required, an increase in tolerances for individual dimensions may be obtained, and in turn, the manufacturing costs may be significantly reduced. 18 There exists root sum squared (RSS) method, 19 system moments, 20 Taguchi’s method, 21 the iterative method, 22 product of exponentials, 23 and Monte Carlo simulations 24–27 for statistical tolerance analysis. In particular, the Monte Carlo method is the most widely used method in statistical tolerance analysis as it allows the use of normal and non-normal distributions, as well as linear and nonlinear assembly functions.…”