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One of the most popular quality engineering tools is the process capability index (PCI), which relates the allowable spread of a process defined by engineering specifications to the natural spread of a process. The traditional PCI does not account for the loss in quality when product characteristics fail to achieve their process target value. This research, in contrast, proposes indices, namely posterior PCIs, that consider the underlying result of observations after inspection. Utilising a truncated distribution as the basis for measurement along with a target-based quality loss function for capability analyses, several posterior indices are developed in this paper. A simulation technique is implemented to compare the proposed posterior PCIs with traditional measures. Finally, the confidence interval approximations for the posterior PCIs are derived. Our results suggest using the proposed posterior indices for capability analyses when industrial processes require that non-conforming products be scrapped prior to shipping to the customer.

Within the last 20 years, the opportunities for manufacturing at the nano-level have risen substantially. Despite this growth, the industry continues to face the challenge of obtaining a significant experimental yield that is worthy of the cost to produce at the atomic level. Today, most researchers utilise response surface methodology to evaluate and improve existing techniques in nanoparticle production. While these methods are appropriate for identifying optimal factor settings in the traditional manufacturing process, they require some refinement under significantly elevated degrees of quality. This paper investigates the use of higher-order response surface designs in the improvement of nanomanufacturing processes.

Since the integration of response surface methods into process robustness studies, many researchers have suggested numerous approaches to further enhance product development. Generally, these robust design methods seek the factor settings that minimise variability and the deviation of the mean from the desired target value. In the absence of a uniform approach to modelling process variability, researchers have typically chosen the standard deviation, variance, or logarithm of the standard deviation. Each measure, however, can produce a different set of optimal factor settings, thus complicating comparison studies. The purpose of this paper is to examine the effects of variability measure selection on solutions and suggest a uniform approach.