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Se acepta que si las observaciones (yi) de un proceso de producción no son independientes, la estructura del error se puede determinar como sugirieron Box-Jenkins (1970). Pero los métodos de B-J son off-line y requieren de cierta experiencia por parte del usuario. En este trabajo, presentamos un procedimiento basado en filtros celosía adaptativos para el control estadístico de procesos (SPC). Este SPC adaptativo puede ser completamente computerizado y aún más, realizable en hardware.
The optimum choice of subgroup size and sampling interval is considered. Assuming that the sampling cost is proportional to the total number of units sampled, the optimum procedure depends on the deviation from the target value that is considered important: this deviation is related to the process capability. In general, if the process capability increases, it is advisable to reduce the subgroup size and the sampling interval length.
Control charts, Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science, System proces control, Lattice Filters, :90 Operations research, mathematical programming::90B Operations research and management science [Classificació AMS], Adaptative SPC, Operations research, Investigació operativa, Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science
Control charts, Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science, System proces control, Lattice Filters, :90 Operations research, mathematical programming::90B Operations research and management science [Classificació AMS], Adaptative SPC, Operations research, Investigació operativa, Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science
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