BS ISO 16337:2021 Application of statistical and related methods to new technology and product development process — Robust tolerance design (RTD).
Design parameter errors cause variability in product output. If the error of a design parameter has a linear effect on the product output, the output variance can be changed by resetting the tolerance of the design parameter. RTD experimentation is used to determine the contributions of the effects of errors in design parameters to the product output.
In the tolerance determination step of RTD, the change in the output variance due to resetting a tolerance is estimated, and the designer selects optimum tolerance for achieving the target output variance. The optimum tolerance can be determined by balancing the effect in quality due to a tolerance change against the cost of the tolerance change[al.
4.2 RTD experimentation
4.2.1 Data generation
RTD experimentation is used to determine the design parameters’ linear e’fects for the designed product. The relationship between the output by the product and the errors in the design parameters is investigated. The output data can be generated in three ways:
1) by using a theoretical formula,
by experimentation with an actual product;
3) by simulation experimentation.
When the theoretical relationship between the product output and the design parameters is known, the output data can be directly calculated for various combination of the design parameter values. RTD offers multi-factor design as an experimental design for generating the output data in various combinations of the level of experimental factors, as shown in case study (1) in ClauseS. ANOVA is used for analysing the dependence of the product output on the factors.
Mathematical analysis can be applied in this case. Mathematical analysis consists of using variance estimates for a system by, for example, propagating an input variance through the system via Taylor series expansions of moment generating functionsr4l.
If an actual product can he constructed, it can be used for experimentation, and the data output can he collected using the actual experiment. However, in many cases, it is difficult to set the intended levels of the errors of design parameters in an actual product because the noise levels cannot be controlled within the error distribution of the design parameters. Simulation experimentation can be used in such cases. This is why simulation experiments are often used in RTD. A simulation program can provide the product output data, as shown in case study (2) in Clause 6.