Early Robust Design—Its Effect on Parameter and Tolerance Optimization

The development of complex products with high quality in dynamic markets requires appropriate robust design and tolerancing workflows supporting the entire product development process. Despite the large number of methods and tools available for designers and tolerance engineers, there are hardly any consistent approaches that are applicable throughout all development stages. This is mainly due to the break between the primarily qualitative approaches for the concept stage and the quantitative parameter and tolerance design activities in subsequent stages. Motivated by this, this paper bridges the gap between these two different views by contrasting the used terminology and methods. Moreover, it studies the effects of early robust design decisions with a focus on Suh’s Axiomatic Design axioms on later parameter and tolerance optimization. Since most robust design activities in concept design can be ascribed to these axioms, this allows reliable statements about the specific benefits of early robust design decisions on the entire process considering variation in product development for the first time. The presented effects on the optimization of nominal design parameters and their tolerance values are shown by means of a case study based on ski bindings.

Robust Tolerance Design for Quality

Design of a product (process) includes system design, parameter design, and tolerance design. Robust design is closely applicable to parameter design and tolerance design. The current literature on robust design has focused on parameter design while the problem of tolerance design has not been adequately covered. The tolerance design literature emphasizes the use of optimization to minimize cost while little attention has been paid to minimizing the sensitivity of tolerances to the variation of manufacturing processes. This paper discusses the application of the design of experiments (DOE) approach to tolerance synthesis to minimize manufacturing variations in a probabilistic case. The DOE approach is illustrated with an example.