Application of Sampling Methods for statistical toler- ance analysis
نویسندگان
چکیده
Manufactured parts differ from ideal shape. Therefore tolerances are used in product development in order to constrain the admissible deviations. The impact of tolerances is analyzed in different ways. It is common to employ Monte Carlo Sampling in order to obtain a statistical result by the iterative evaluation of the functional relationship. In contrast to this, Robust Design has established a bunch of sampling methods that are computationally more efficient. The objective of the paper is therefore to outline the basics of two selected sampling methods. Implementing a case study, advantages and disadvantages of the compared methods are highlighted. A one way clutch assembly, which is known as benchmarking computer aided tolerancing, serves as case study. The functional relationship is nonlinear and therefore of special interest. A numeric comparison was performed between Monte Carlo Sampling (MCS) and Latin Hypercube Sampling (LHS) using MATLAB. It can be found from this study that Latin Hypercube Sampling is quite suitable for the use in tolerance calculation. Mean values and variances can be estimated using lesser samples compared to MCS reliably. Therefore LHS is proposed as suitable sampling strategy especially for time consuming numeric problems in tolerance analysis too.
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