A design is robust if the design values for selected performance characteristics (i.e. responses) are chosen to be invariant to the variations the product will experience. For a design to be acceptable, it must conform to the design specifications. However, due to the existence of variation, this conformance is satisfied probabilistically, i.e. yield. Optimal manufacturing yield design is defined as a design that maximises the probability of satisfying the design specifications. Methods to achieve robust design for a single response and to achieve yield maximization are well established. A new method of achieving high yield and robust design for multiple responses is presented using the Cp and Cpk capability indices used in on‐line quality control techniques. The proposed method is applied to a single response problem and two multiple response problems. The results showed that the proposed method is capable of producing good manufacturing yield and robust design simultaneously.

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Techniques to maximize manufacturing yield of an engineering design for mass manufactured products are well established. These techniques may be broadly grouped into geometrical and statistical techniques. The geometrical techniques suffer from the curse of dimensionality and therefore become very expensive when applied to problems with a large number of design parameters. The statistical techniques focus very much on optimizing the manufacturing yield through Monte Carlo simulations and always require a feasible starting solution to begin functioning. These could again be expensive in application. This paper seeks to address the maximization of the manufacturing yield. In doing so, it attempts to reduce the costs associated with the optimization. It also seeks to offer a technique which can begin the search with an infeasible solution and yet identify a maximal manufacturing yield region. The proposed technique is applied to a set of design problems. The results indicate that the proposed technique is capable of identifying regions of maximal manufacturing yield.