In statistical process control, the control chart utilizing the idea of maximum entropy distribution density level sets has been proven to perform well for monitoring the quantity with multimodal distribution. However, it is too complicated to implement for the quantity with unimodal distribution. This article proposes a simplified method based on maximum entropy for the control chart design wh...

Control Chart Patterns (CCPs) recognition is one the most important concepts in control chart application. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this study, a fuzzy rule-based system is developed for X ̅ control charts to prioritize the control chart causes based on the accumulated e...

Statistical process control is an important and convenient tool to stabilize the quality of manufactured goods and service operations. The traditional Shewhart control chart has been used extensively for process control, which is valid under the independence assumption of consecutive observations. In real world applications, there are many types of dependent observations in which the traditiona...

Along with the widespread use of Taguchi methods in product design, deenition of the loss function has been integrated with numerous models which require quality cost estimation. In this paper, the economic-statistical design of a variable sampling X-bar control chart is extended using the Taguchi loss function to improve chart eeectiveness from a quality cost point of view. The eeectiveness of...

Identifying convergence in numerical optimization is an ever-present, difficult, and often subjective task. The statistical framework of Gaussian process surrogate model optimization provides useful measures for tracking optimization progress; however, the identification of convergence via these criteria has often provided only limited success and often requires a more subjective analysis. Here...

one of the most important problems of the designs proposed by traditional economic-statistical approaches of control charts is inefficiency in the face of uncertainty. uncertainty in the parameters of economic-statistical models may lead to failure in rapidly detecting changes in processes and impose greater costs to the organization. monitoring the machining process in an automotive industry e...

The most commonly used statistical process control charts to detect special causes are Shewhart and Cusum charts. However, the Cuscore chart is a contemporary alternative that is especially well suited for detecting special causes that can be modeled in advance based on their characteristic effect on the system. In this paper, we develop the appropriate Cuscore statistic and the required contro...

We propose a Shewhart control chart based on gauging theoretically continuous observations into multiple groups. This chart is designed to monitor the process mean and standard deviation for deviations from stability. By assuming an underlying normal distribution, we derive the optimal grouping criteria that maximizes the expected statistical information available in a sample. Control charts ba...