Control de calidad con First samples Second sample Calibration data in D[trial] New data in D[!trial] summary statistics 0.0 0.1 0.2 0.3 0.4 0.5 Felipe de Mendiburu 1 4 7 11 15 19 23 27 31 39 47 55 63 71 79 87
cause.and.effect( cause=list( Materiales=c("Materia prima defectuosa", "Defectos de armado", "Material escalibrado"), Mano.Obra=c("Falta de capacitacion", "Falta de compromiso"),metodos=c("inspeccion deficiente", "Falta de instruccion", "Diseño indadecuado"),maquinas=c("soldador inadecuado", "Patron desequilibrado")), effect="falla el termostado", title= " Diagrama de Causa - Efecto", cex = c(1.5, 0.9, 1.5), font = c(4,1,4)) Diagrama de Causa - Efecto Materiales Mano.Obra Materia prima defectuosa Falta de capacitacion Defectos de armado Falta de compromiso Material descalibrado Falla el termostado Diseño indadecuado Falta de instruccion Patron desequilibrado Inspeccion deficiente Soldador inadecuado Metodos Maquinas
Argumentos de la funcion qqc()
x <- c(33.75, 33.05, 34, 33.8,.) qcc(x, type="xbar.one") xbar.one Chart for x summary statistics 32.5 33.0 33.5 34.0 34.5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Number of groups = 15 Center = 33.52333 StdDev = 0.4261651 = 32.24484 = 34.80183 Number beyond limits = 0 Number violating runs = 0
Excel: pistones.xls Text: pistones.txt Copy Paste En R: > pistones <- read.table( pistones.txt,header=t)
> qcc(diameter, type="xbar") xbar Chart for diameter summary statistics 73.990 73.995 74.000 74.005 74.010 74.015 74.020 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Number of groups = 40 Center = 74.0036 StdDev = 0.009992449 = 73.9902 = 74.01701 Number beyond limits = 2 Number violating runs = 3
> qcc(diameter, type="xbar") Call: qcc(data = diameter, type = "xbar") xbar chart for diameter Summary of group statistics: Min. 1st Qu. Median Mean 3rd Qu. Max. 73.99 74.00 74.00 74.00 74.01 74.02 sample size: 5 Number of groups: 40 Center of group statistics: 74.0036 Standard deviation: 0.00999245 Control limits: 73.9902 74.01701
Eliminando algunas observaciones para tener muestras con diferentes tamaños: > salen <- c(9, 10, 30, 35, 45, 64, 65, 74, 75, 85, 99, 100)
Ordenes para hacer las cartas: > salen <- c(9, 10, 30, 35, 45, 64, 65, 74, 75, 85, 99, 100) > diameter <- qcc.groups(pistones$diameter[-salen], sample[- salen]) > qcc(diameter[1:25,], type="xbar") > qcc(diameter[1:25,], type="r") > qcc(diameter[1:25,], type="s") > qcc(diameter[1:25,], type="xbar", newdata=diameter[26:40,]) > qcc(diameter[1:25,], type="r", newdata=diameter[26:40,]) > qcc(diameter[1:25,], type="s", newdata=diameter[26:40,]) Otras ordenes: > qcc(diameter[1:25,], type="xbar", newdata=diameter[26:40,], nsigmas=2) > qcc(diameter[1:25,], type="xbar", newdata=diameter[26:40,], confidence.level=0.99)
xbar Chart for diameter[1:25, ] summary statistics 73.985 73.995 74.005 74.015 1 2 3 4 5 6 7 8 9 10 12 14 16 18 20 22 24 Number of groups = 25 Center = 74.00075 StdDev = 0.01013948 is variable is variable Number beyond limits = 0 Number violating runs = 0
R Chart for diameter[1:25, ] summary statistics 0.00 0.01 0.02 0.03 0.04 0.05 1 2 3 4 5 6 7 8 9 10 12 14 16 18 20 22 24 Number of groups = 25 Center = 0.02230088 StdDev = 0.01013948 = 0 is variable Number beyond limits = 0 Number violating runs = 2
S Chart for diameter[1:25, ] summary statistics 0.000 0.005 0.010 0.015 0.020 1 2 3 4 5 6 7 8 9 10 12 14 16 18 20 22 24 Number of groups = 25 Center = 0.00938731 StdDev = 0.01013948 = 0 is variable Number beyond limits = 0 Number violating runs = 1
xbar Chart for diameter[1:25, ] and diameter[26:40, ] Calibration data in diameter[1:25, ] New data in diameter[26:40, ] summary statistics 73.99 74.00 74.01 74.02 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Number of groups = 40 Center = 74.00075 StdDev = 0.01013948 is variable is variable Number beyond limits = 3 Number violating runs = 1
summary statistics 0.00 0.01 0.02 0.03 0.04 0.05 R Chart for diameter[1:25, ] and diameter[26:40, ] Calibration data in diameter[1:25, ] New data in diameter[26:40, ] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Number of groups = 40 Center = 0.02230088 StdDev = 0.01013948 = 0 is variable Number beyond limits = 0 Number violating runs = 2
S Chart for diameter[1:25, ] and diameter[26:40, ] Calibration data in diameter[1:25, ] New data in diameter[26:40, ] summary statistics 0.000 0.005 0.010 0.015 0.020 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Number of groups = 40 Center = 0.00938731 StdDev = 0.01013948 = 0 is variable Number beyond limits = 0 Number violating runs = 1
ATRIBUTOS
p Chart for D[trial] summary statistics 0.1 0.2 0.3 0.4 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Number of groups = 30 Center = 0.2313333 StdDev = 0.421685 = 0.05242755 = 0.4102391 Number beyond limits = 2 Number violating runs = 0
p Chart for D[inc] summary statistics 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 1 2 3 4 5 6 7 8 9 11 13 15 17 19 21 23 25 27 Number of groups = 28 Center = 0.215 StdDev = 0.4108223 = 0.04070284 = 0.3892972 Number beyond limits = 1 Number violating runs = 1
summary statistics 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 Calibration data in D[inc] p Chart for D[inc] and D[!trial] New data in D[!trial] 1 3 5 7 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 Number of groups = 52 Center = 0.215 StdDev = 0.4108223 = 0.04070284 = 0.3892972 Number beyond limits = 2 Number violating runs = 2
> q1 <- qcc(d[inc], sizes=size[inc], type="c") > c Chart for D[inc] summary statistics 0 2 4 6 8 10 12 31 33 35 37 39 41 43 46 48 50 52 55 57 59 Number of groups = 28 Center = 5.714286 StdDev = 2.390457 = 0 = 12.88566 Number beyond limits = 0 Number violating runs = 0
Carta U Datos de clase
> attach(datos) > qcc(defectos,unidades, type="u") u Chart for Defectos summary statistics 0.0 0.5 1.0 1.5 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Number of groups = 30 Center = 0.4235925 StdDev = 2.594075 is variable is variable Number beyond limits = 1 Number violating runs = 1
OC curves for xbar chart Prob. type II error 0.0 0.2 0.4 0.6 0.8 1.0 n = 5 n = 1 n = 10 n = 15 n = 20 0 1 2 3 4 5 Process shift (std.dev)
contact num. price code supplier code part num. schedule date Pareto Chart for defect Error frequency 0 50 100 150 200 250 300 0% 25% 50% 75% 100% Cumulative Percentage
q <- qcc(diameter[1:25,], type="xbar", nsigmas=3, plot=false) process.capability(q, spec.limits=c(73.95,74.05)) Process Capability Analysis for diameter[1:25, ] LSL Target USL 73.94 73.96 73.98 74.00 74.02 74.04 74.06 Number of obs = 125Target = 74 Center = 74.00305 LSL = 73.95 StdDev = 0.01171394USL = 74.05 Cp = 1.42 Cp_l = 1.51 Cp_u = 1.34 Cp_k = 1.34 Cpm = 1.38 Exp<LSL 0% Exp>USL 0% Obs<LSL 0% Obs>USL 0%
> q <- qcc(diameter[1:25,], type="xbar", nsigmas=3, plot=false) > process.capability.sixpack(q, spec.limits=c(73.95,74.05)) summary statistics 73.990 74.010 xbar Chart for diameter[1:25, ] 1 3 5 7 9 11 14 17 20 23 Process Capability Analysis for diameter[1:25, ] LSL Target USL 73.94 74.00 74.06 summary statistics 0.00 0.03 R Chart for diameter[1:25, ] 1 3 5 7 9 11 14 17 20 23 Sample Quantiles 73.99 74.02 Normal Q-Q Plot -2 0 1 2 Theoretical Quantiles diameter[1:25, ] 73.99 74.02 Run chart Capability plot Center = 74.00282 StdDev = 0.01142625 Target = 74 Process tolerance Cp = 1.46 Cp_k = 1.38 Specification limits Cpm = 1.42 5 10 15 20 25 73.85 73.95 74.05
> q <- qcc(diameter[1:25,], type="xbar", nsigmas=3, plot=false) > cusum(q) Cusum Chart for diameter[1:25, ] Cumulative Sum Below Target Above Target -5-4 -3-2 -1 0 1 2 3 4 5 UDB LDB 1 2 3 4 5 6 7 8 9 11 13 15 17 19 21 23 25 Number of groups = 25 Target = 74.00282 StdDev = 0.01142625 Decision boundaries (std. err.) = 5 Shift detection (std. err.) = 1 No. of points beyond boundaries = 0
> q <- qcc(diameter[1:25,], type="xbar", nsigmas=3, plot=false) > process.capability.sixpack(q, spec.limits=c(73.95,74.05)) summary statistics 73.990 74.010 xbar Chart for diameter[1:25, ] 1 3 5 7 9 11 14 17 20 23 Process Capability Analysis for diameter[1:25, ] LSL Target USL 73.94 74.00 74.06 summary statistics 0.00 0.03 R Chart for diameter[1:25, ] 1 3 5 7 9 11 14 17 20 23 Sample Quantiles 73.99 74.02 Normal Q-Q Plot -2 0 1 2 Theoretical Quantiles diameter[1:25, ] 73.99 74.02 Run chart Capability plot Center = 74.00282 StdDev = 0.01142625 Target = 74 Process tolerance Cp = 1.46 Cp_k = 1.38 Specification limits Cpm = 1.42 5 10 15 20 25 73.85 73.95 74.05
> q <- qcc(diameter[1:25,], type="xbar", nsigmas=3, plot=false) > ewma(q, lambda=0.2) EWMA Chart for diameter[1:25, ] Summary Statistics 73.995 74.000 74.005 74.010 1 2 3 4 5 6 7 8 9 11 13 15 17 19 21 23 25 Number of groups = 25 Target = 74.00282 StdDev = 0.01142625 Smoothing parameter = 0.2 Control limits at 3*sigma