Abstract ▼ AI-Helper

Classical SPC charting methods such as (equation omitted), R, S charts assume high volume manufacturing processes where at least 25 or 30 calibrate samples of size 4 or 5 each can be gathered to estimate the process parameters before on-line charting actually begines. However, for many processes, es...

Classical SPC charting methods such as (equation omitted), R, S charts assume high volume manufacturing processes where at least 25 or 30 calibrate samples of size 4 or 5 each can be gathered to estimate the process parameters before on-line charting actually begines. However, for many processes, especially in a job-shop setting, production runs are not necessarily long and charting technique are required that do not that depend upon knowing the process parameters in advance of the run. In this paper, using modifying statistics, we give a method for constructing control charts for the process mean when the measurements are from a normal distribution. In this case, consider that smaller weight being assigned to the older data as time process and properties and taking method of exponential smoothing constant$(\lambda)$ are suggested.