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Control charts are graphical plots used in production control to determine whether quality and manufacturing processes are being controlled under stable conditions. (ISO 7870-1) [1] The hourly status is arranged on the graph, and the occurrence of abnormalities is judged based on the presence of data that differs from the conventional trend or deviates from the control limit line.
The resulting plots are analyzed as for other control charts, using the rules that are deemed appropriate for the process and the desired level of control. At the least, any points above either upper control limits or below the lower control limit are marked and considered a signal of changes in the underlying process that are worth further ...
In statistical quality control, the CUSUM (or cumulative sum control chart) is a sequential analysis technique developed by E. S. Page of the University of Cambridge. It is typically used for monitoring change detection. [1] CUSUM was announced in Biometrika, in 1954, a few years after the publication of Wald's sequential probability ratio test ...
As with the ¯ and s and individuals control charts, the ¯ chart is only valid if the within-sample variability is constant. [4] Thus, the R chart is examined before the ¯ chart; if the R chart indicates the sample variability is in statistical control, then the ¯ chart is examined to determine if the sample mean is also in statistical control.
As with the ¯ and R and individuals control charts, the ¯ chart is only valid if the within-sample variability is constant. [5] Thus, the s chart is examined before the ¯ chart; if the s chart indicates the sample variability is in statistical control, then the ¯ chart is examined to determine if the sample mean is also in statistical control.
Simple example of a process control chart, tracking the etch (removal) rate of Silicon in an ICP Plasma Etcher at a microelectronics waferfab. [1] Time-series data shows the mean value and ±5% bars. A more sophisticated SPC chart may include "control limit" & "spec limit" % lines to indicate whether/what action should be taken.
There are distribution-free control charts for both Phase-I analysis and Phase-II monitoring. One of the most notable distribution-free control charts for Phase-I analysis is RS/P chart proposed by G. Capizzi and G. Masaratto. RS/P charts separately monitor location and scale parameters of a univariate process using two separate charts.
The p-chart only accommodates "pass"/"fail"-type inspection as determined by one or more go-no go gauges or tests, effectively applying the specifications to the data before they are plotted on the chart. Other types of control charts display the magnitude of the quality characteristic under study, making troubleshooting possible directly from ...