Abstract
Statistical process control (SPC) tools are used for the investigation and identification of unnatural variations in the manufacturing, industrial, and service processes. The control chart, the basic and the most famous tool of SPC, is used for process monitoring. Generally, control charts are constructed under normality assumption of the quality characteristic of interest, but in practice, it is quite hard to hold the normality assumption. In such situations, parametric charts tend to offer more frequent false alarms and invalid out-of-control performance. To rectify these problems, non-parametric control charts are used, as these have the same in-control run length properties for all the continuous distributions and are known as in-control robust. This study intends to develop a new non-parametric exponentially weighted moving average (NPEWMA) chart based on sign statistics under a ranked set sampling scheme that is hereafter named (NPREWMA-SN). The run-length profiles of the NPREWMA-SN chart are computed using the Monte Carlo simulation method. The proposed scheme is compared with NPEWMA-SN and classical EWMA-X¯ charts, using different run length measures. The comparison reveals the in-control robustness and superiority of the proposed scheme over its competitors in detecting all kinds of shifts in the process location. A practical application related to the substrate manufacturing process is included to show the demonstration of the proposed chart.
Highlights
Quality management provides many operational and management techniques that save both time and cost to achieve the standard finished product
The IC average RL (ARL) of a chart is denoted by ARL0 and OOC is nominated as ARL1
A new NP monitoring modifications have been made in the literature, and the ranked set sampling (RSS) scheme is one of them
Summary
Quality management provides many operational and management techniques that save both time and cost to achieve the standard finished product. These techniques are used in manufacturing processes, filling processes, and in services to spot the un-natural variations that improve the quality of finished products. In semiconductor manufacturing processes, the controllable input variables are photolithography, temperature, silicon wafer, resistance, and some other process variables. The flow width of the resistance is a running process that has some quality characteristics. These quality characteristics are monitored using various statistical tools to improve and produce
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