Robust design (RD) techniques, which are based on the concept of building quality into products or processes, are increasingly popular in industry primarily because of their practicality. Traditional RD principles have often been applied to situations in which the quality characteristics of interest are time-insensitive. However, when time-oriented quality characteristics are studied, censored data often occur. As a result, current RD models reported in the literature may not be effective in finding solutions based on such data. To address such practical needs, this paper develops a censored RD model. We also propose an estimation method that is closely related to the expectation-maximization algorithm and compare it with the method of maximum likelihood estimation via a numerical example. Model validation is conducted, and comparative studies are discussed for model verification.
A number of quality loss functions, most recently the Taguchi loss function, have been developed to quantify the loss due to the deviation of product performance from the desired target value. All these loss functions assume the same loss at the specified specification limits. In many real life industrial applications, however, the losses at the two different specifications limits are often not the same.Further, current loss functions assume a product should be reworked or scrapped if product performance falls outside the specification limits. It is a common practice in many industries to replace a defective item rather than spending resources to repair it, especially if considerable amount of time is required. To rectify these two potential problems, this paper proposes more realistic quality loss functions for proper applications to real-world industrial problems. This paper also carries out a comparison studies of all the loss functions it considers.
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