Speaker
Description
The presence of variation is an undesirable (but natural) factor in processes. Quality improvement practitioners search constantly for efficient ways to monitor it, a primary requirement in SPC. Generally, inspections by attributes are cheaper and simpler than inspections by variables, although they present poor performance in comparison. The $S^2$ chart is widely applied in monitoring process variance, facing the need for more economical strategies that provide good performance is the motivation of this work. Many practitioners use four to six units to build the $S^2$ chart, the reduction of sample size decreases their power to detect changes in process variance. This work proposes the application of alternating inspections (by attributes and variables) using sequentially samples of size $n_a$ and $n_b$ ($n_a > n_b$). The items of sample of size $n_a$ are classified according to the $np_x$ chart procedure, using a GO / NO GO gauge and counting the number of non-approved items ($Y_{n_a}$). The items of sample of size $n_b$ are measured and calculated its sample variance $S^2_{n_b}$. If $Y_{n_a} > UCL_{n_a}$ or $S^2_{n_b} > UCL_{n_b}$ the process is judged out of control. The inspection always restarts with sample size $n_a$ (using the $np_x$ chart), otherwise, the process continues. The parameters of the proposed chart are optimized by an intensive search, in order to outperform the $S^2$ chart (in terms of $ARL_1$, for a fixed $ARL_0$), restricted to have average sample size closer to the sample used for $S^2$, from their results was possible to reduce about 10% in $ARL_1$.
| Keywords | Quality Control, Attribute and Variable Control Charts, Discriminant Limits |
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