First, the ππ π₯ -type attribute control charts have been proposed to monitor the process mean. The main contribution of this paper is to show the good performances of the "pure ππ π₯ " (shortly ππ π 2 π₯ ) and "improved ππ π₯ " (shortly ππ π 2 π₯(πΌ) ) attribute charts to monitor the variance. The results confirmed that they are also good alternatives to π 2 control chart. To have an equal performance (in terms of speed to detect abnormal cases), the sample size needed for these charts is slightly higher than those practiced for an π 2 control chart. This is an advantage since an attribute inspection is faster and cheaper as no measurements on the examined units are taken (mainly if we are dealing with destructive experiments). A numerical example illustrates our proposal.
This paper proposes an attribute control chart named ππ π π₯(πΌ) which is used for monitoring the process mean value of a variable (π) by employing sequential sample sizes of π π , π π with π π > π π . Each item is classified as approved or not according to a discriminant limit (π€ π , π = π, π) using a GO/NO GO gauge. In the end of inspection we'll have π π and π π items classified as disapproved. Whenever π π > ππΆπΏ π π or π π > ππΆπΏ π π , the process is judged to be out of control and after adjustments in the process, the inspection is always restarted with a sample size of π π . The parameters used in the construction of the ππ π π₯(πΌ) control chart were obtained through a search for values that optimize their performance, such that can compete with the standard attribute ππ π₯ chart and the traditional π control chart. The performances were compared in terms of average run length (π΄π πΏ) in scenarios of shift sizes (πΏ) in the process mean. A numerical example illustrates its application.
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