Journal of Quality Engineering and Management

Abstract In shock models, the system will fail as soon as the amount of damages caused by shocks exceeds its failure threshold. In this research, the design of control chart for reliability monitoring of systems with random failure threshold subjected to cumulative random shocks is discussed. In the cumulative shock model, the system fails when the cumulative amount of damage caused by the shocks exceeds the failure threshold of the system. For the analytical solution of the model related to the control chart, there are some complexities such as the existence of unknown distributions, obtaining the convolutions of the distributions and integral calculations. To overcome these problems, phase-type distribution is used for modeling. By presenting a numerical example, the results of the presented analytical model are evaluated and compared with the Monte Carlo simulation method. Also, the performance of the proposed control chart using the average run length and average time to out of control signal criteria is evaluated.

Abstract The subject of time-dependent quality indices—whose values vary over time—has attracted significant attention from quality researchers in recent years. Various methods have been developed for this purpose, among which one of the most recent approaches involves analyzing performance profiles over time. Moreover, as competition among manufactured products intensifies, product design has become a primary focus within the product life cycle for manufacturing organizations. One of the most widely used approaches in this context is robust design. The application of response surface methodology and its related optimization tools has also gained great importance in design studies. In this article, a utility function–based method is presented for the robust design optimization of time-dependent quality indices. Based on the results obtained from a pharmaceutical case study, the proposed method performs significantly better than the two existing methods.

Abstract Reliability is an essential characteristic in any application area and is defined as the probability of performing a specified task under given conditions within a predetermined period of time. In this study, the strength (capacity) of a product and the stress or load applied to it are used to model and calculate reliability.
In most cases, the imposed load cannot be controlled; however, various approaches can be employed to improve the capacity of the manufactured product. This paper investigates the effect of reducing variability in each quality characteristic of a product under specific distributions.
Analytical and numerical results indicate that decreasing the variability of the product’s quality characteristics increases its capacity and consequently enhances its reliability.