suppressMessages(library(ggplot2))
suppressMessages(library(dplyr))
suppressMessages(library(ggcorrplot))
suppressMessages(library(ggpubr))

1 Longitudinal

1.1 Hoosier 6.0 / 18.0 - 10 LCO

dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Longitudinal")
setwd(dir_archivos)
archivo_nombre="Hoosier_N3_Longitudinal_29.txt"
dat = read.table(archivo_nombre, header=T, sep="",na.strings="")

ph1LO<- ggplot(dat, aes(x=FZ)) + geom_histogram()
ph2LO<- ggplot(dat, aes(x=P)) + geom_histogram()
ph3LO<- ggplot(dat, aes(x=IA)) + geom_histogram()
ggarrange(ph1LO,ph2LO,ph3LO)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# FZ [-800,-600]
# IA [4.05,3.95]
# P [100,90]
x = filter(dat, dat$FZ >= -800 & dat$FZ <= -600 & dat$IA >= 3.95 & dat$IA <= 4.05 & dat$P >= 96 & dat$P <= 97)
ggplot(x, aes(x=FZ), bins=30) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p3LO <- ggplot(data = x) +
  geom_point(aes(y =FX/FZ , x = SR)) +
  labs(title = "Hoosier 6.0 / 18.0 - 10 LCO") +
  theme_bw() 

p3_valueLO <- max(x$FX/abs(x$FZ))

1.2 Hoosier 20.5 x 7.0 - 13 R25B

dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Longitudinal")
setwd(dir_archivos)
archivo_nombre="Hoosier_N2_Longitudinal_42.txt"
dat = read.table(archivo_nombre, header=T, sep="",na.strings="")

ph1LO<- ggplot(dat, aes(x=FZ)) + geom_histogram()
ph2LO<- ggplot(dat, aes(x=P)) + geom_histogram()
ph3LO<- ggplot(dat, aes(x=IA)) + geom_histogram()
ggarrange(ph1LO,ph2LO,ph3LO)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# FZ [-800,-600]
# IA [4.05,3.95]
# P [100,90]
x = filter(dat, dat$FZ >= -800 & dat$FZ <= -600 & dat$IA >= 3.95 & dat$IA <= 4.05 & dat$P >= 96 & dat$P <= 97)
ggplot(x, aes(x=FZ), bins=30) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p2LO <- ggplot(data = x) +
  geom_point(aes(y =FX/FZ , x = SR)) +
  labs(title = "Hoosier 20.5 x 7.0 - 13 R25B") +
  theme_bw()

p2_valueLO <- max(x$FX/abs(x$FZ))

1.3 Hoosier 6.0 / 18.0 - 10 LCO

dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Longitudinal")
setwd(dir_archivos)
archivo_nombre="Hoosier_N1_Longitudinal_35.txt"
dat = read.table(archivo_nombre, header=T, sep="",na.strings="")

ph1LO<- ggplot(dat, aes(x=FZ)) + geom_histogram()
ph2LO<- ggplot(dat, aes(x=P)) + geom_histogram()
ph3LO<- ggplot(dat, aes(x=IA)) + geom_histogram()
ggarrange(ph1LO,ph2LO,ph3LO)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# FZ [-800,-600]
# IA [4.05,3.95]
# P [100,90]
x = filter(dat, dat$FZ >= -800 & dat$FZ <= -600 & dat$IA >= 3.95 & dat$IA <= 4.05 & dat$P >= 96 & dat$P <= 97)
ggplot(x, aes(x=FZ), bins=30) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p1LO <- ggplot(data = x) +
  geom_point(aes(y =FX/FZ , x = SR))+
  labs(title = "Hoosier 6.0 / 18.0 - 10 LCO") +
  theme_bw() 
p1_valueLO <- max(x$FX/abs(x$FZ))

2 Lateral

2.1 Hoosier 6.0 / 18.0 - 10 LCO

dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Lateral")
setwd(dir_archivos)
dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Lateral")
setwd(dir_archivos)
archivo_nombre="Hoosier_Lateral_N3_22.txt"
dat = read.table(archivo_nombre, header=T, sep="",na.strings="")

ph1LA<- ggplot(dat, aes(x=FZ)) + geom_histogram()
ph2LA<- ggplot(dat, aes(x=P)) + geom_histogram()
ph3LA<- ggplot(dat, aes(x=IA)) + geom_histogram()
ggarrange(ph1LA,ph2LA,ph3LA)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# FZ[-1000,-1250]
# IA[-0.09,0.09]
# P [80, 100]
x = filter(dat,dat$SR > -0.20 & dat$SR < 0.20 & dat$P >= 80 & dat$P <= 100 & dat$IA >= -0.09 & dat$IA <=0.09 & dat$FZ >= -1250 & dat$FZ <= -1000)
p3LA <- ggplot(data = x) +
  geom_point(aes(y =FY , x = SA)) +
  labs(title = "Hoosier 6.0 / 18.0 - 10 LCO") +
  theme_bw()
p3_valueLA <- max(x$FX/abs(x$FZ))

2.2 Hoosier 20.5 x 7.0 - 13 R25B

dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Lateral")
setwd(dir_archivos)
archivo_nombre="Hoosier_Lateral_N2_14.txt"
dat = read.table(archivo_nombre, header=T, sep="",na.strings="")

ph1LA<- ggplot(dat, aes(x=FZ)) + geom_histogram()
ph2LA<- ggplot(dat, aes(x=P)) + geom_histogram()
ph3LA<- ggplot(dat, aes(x=IA)) + geom_histogram()
ggarrange(ph1LA,ph2LA,ph3LA)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# FZ[-1000,-1250]
# IA[-0.09,0.09]
# P [80, 100]
x = filter(dat, dat$SR > -0.20 & dat$SR < 0.20 & dat$P >= 80 & dat$P <= 100 & dat$IA >= -0.09 & dat$IA <=0.09 & dat$FZ >= -1250 & dat$FZ <= -1000)
p2LA <- ggplot(data = x) +
  geom_point(aes(y =FY , x = SA)) +
  labs(title = "Hoosier 20.5 x 7.0 - 13 R25B") +
  theme_bw()
p2_valueLA <- max(x$FX/abs(x$FZ))

2.3 Hoosier 18.0 x 7.5 10 R25B

dir_archivos = ("C:/Users/User/Desktop/MasterIndustriales/Ingenia/Datos_Neumaticos/Lateral")
setwd(dir_archivos)
archivo_nombre="Hoosier_Lateral_N1_22.txt"
dat = read.table(archivo_nombre, header=T, sep="",na.strings="")

ph1LA<- ggplot(dat, aes(x=FZ)) + geom_histogram()
ph2LA<- ggplot(dat, aes(x=P)) + geom_histogram()
ph3LA<- ggplot(dat, aes(x=IA)) + geom_histogram()
ggarrange(ph1LA,ph2LA,ph3LA)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# FZ[-1000,-1250]
# IA[-0.09,0.09]
# P [80, 100]

x = filter(dat, dat$SR > -0.20 & dat$SR < 0.20 & dat$P >= 80 & dat$P <= 100 & dat$IA >= -1.5 & dat$IA <=2 & dat$FZ >= -1250 & dat$FZ <= -1000)
p1LA <- ggplot(data = x) +
  geom_point(aes(y =FY , x = SA)) +
  labs(title = "Hoosier 18.0 x 7.5 10 R25B") +
  theme_bw()
p1_valueLA <- max(x$FX/abs(x$FZ))

3 Comparación entre neumáticos

ggarrange(p1LO,p2LO,p3LO)

ggarrange(p1LA,p2LA,p3LA)

maxLO <-c(p1_valueLO,p2_valueLO ,p3_valueLO)
maxLA <-c(p1_valueLA,p2_valueLA ,p3_valueLA)

maxLO_LA = data.frame(cbind(maxLO,maxLA))
colnames(maxLO_LA ) = c("MAX_VALUE_SR_LONGITUDINAL","MAX_VALUE_SA_LATERAL")
rownames(maxLO_LA) = c("Hoosier 18.0 x 7.5 10 R25B","Hoosier 20.5 x 7.0 - 13 R25B","Hoosier 6.0 / 18.0 - 10 LCO")

En esta tabla se pueden observar y compar los diferentes máximos y mínimos de SR y SA en función de las caráteristicas de Fz, Ia y P establecidas. El neumático que mejor comportamiento tiene es Hoosier 18.0 x 7.5 10 R25B

maxLO_LA
##                              MAX_VALUE_SR_LONGITUDINAL MAX_VALUE_SA_LATERAL
## Hoosier 18.0 x 7.5 10 R25B                    2.561132           0.10600114
## Hoosier 20.5 x 7.0 - 13 R25B                  2.527532           0.08246950
## Hoosier 6.0 / 18.0 - 10 LCO                   1.772489           0.07965197