Print data
PHAM NHAT HAO
2026-03-05
printer_data <- read.csv("C:/Users/hao/Desktop/BTL/XSTK/DATA XSTK/data.csv")
head(printer_data,3)## layer_height wall_thickness infill_density infill_pattern nozzle_temperature
## 1 0.02 8 90 grid 220
## 2 0.02 7 90 honeycomb 225
## 3 0.02 1 80 grid 230
## bed_temperature print_speed material fan_speed roughness tension_strenght
## 1 60 40 abs 0 25 18
## 2 65 40 abs 25 32 16
## 3 70 40 abs 50 40 8
## elongation
## 1 1.2
## 2 1.4
## 3 0.8new_DF <- printer_data[,c("layer_height","wall_thickness","infill_density",
"infill_pattern","nozzle_temperature","bed_temperature","print_speed",
"material","fan_speed","tension_strenght","elongation")]
head(new_DF,3)## layer_height wall_thickness infill_density infill_pattern nozzle_temperature
## 1 0.02 8 90 grid 220
## 2 0.02 7 90 honeycomb 225
## 3 0.02 1 80 grid 230
## bed_temperature print_speed material fan_speed tension_strenght elongation
## 1 60 40 abs 0 18 1.2
## 2 65 40 abs 25 16 1.4
## 3 70 40 abs 50 8 0.8library(questionr)
freq.na(new_DF) #in kết quả dữ liệu bị khuyết## missing %
## layer_height 0 0
## wall_thickness 0 0
## infill_density 0 0
## infill_pattern 0 0
## nozzle_temperature 0 0
## bed_temperature 0 0
## print_speed 0 0
## material 0 0
## fan_speed 0 0
## tension_strenght 0 0
## elongation 0 0new_function <- function(x){
c(n=length(x),xtb=mean(x),sd=sd(x),Q1=quantile(x,probs=0.25),
Q2=median(x),Q3=quantile(x,probs=0.75),min=min(x),max=max(x))
}
continous_data <- new_DF[,c("layer_height","wall_thickness","infill_density",
"nozzle_temperature","bed_temperature","print_speed","fan_speed","tension_strenght","elongation")]
apply(continous_data,2,new_function)## layer_height wall_thickness infill_density nozzle_temperature
## n 50.00000000 50.000000 50.00000 50.00000
## xtb 0.10600000 5.220000 53.40000 221.50000
## sd 0.06439673 2.922747 25.36348 14.82035
## Q1.25% 0.06000000 3.000000 40.00000 210.00000
## Q2 0.10000000 5.000000 50.00000 220.00000
## Q3.75% 0.15000000 7.000000 80.00000 230.00000
## min 0.02000000 1.000000 10.00000 200.00000
## max 0.20000000 10.000000 90.00000 250.00000
## bed_temperature print_speed fan_speed tension_strenght elongation
## n 50.000000 50.0000 50.00000 50.000000 50.0000000
## xtb 70.000000 64.0000 50.00000 20.080000 1.6720000
## sd 7.142857 29.6923 35.71429 8.925634 0.7881883
## Q1.25% 65.000000 40.0000 25.00000 12.000000 1.1000000
## Q2 70.000000 60.0000 50.00000 19.000000 1.5500000
## Q3.75% 75.000000 60.0000 75.00000 27.000000 2.1750000
## min 60.000000 40.0000 0.00000 4.000000 0.4000000
## max 80.000000 120.0000 100.00000 37.000000 3.3000000table(new_DF$infill_pattern)##
## grid honeycomb
## 25 25table(new_DF$material)##
## abs pla
## 25 25hist(new_DF$tension_strenght,breaks=10,col="darkred",xlab="tension strenght",main="histogram tension strenght",xlim=c(0,40),ylim=c(0,15),labels=T)
boxplot(tension_strenght~infill_pattern,new_DF,col=c("red","blue"),
main="tension_strenght and infill_pattern")
par(mfrow=c(2,4))
plot(new_DF$layer_height,new_DF$tension_strenght,xlab="layer_height",ylab="tension_strenght",main="layer height & tension strenght")
plot(new_DF$wall_thickness,new_DF$tension_strenght,xlab="wall_thickness",ylab="tension_strenght",main="wall_thickness & tension strenght")
plot(new_DF$infill_density,new_DF$tension_strenght,xlab="infill_density",ylab="tension_strenght",main="infill_density & tension strenght")
plot(new_DF$nozzle_temperature,new_DF$tension_strenght,xlab="nozzle_temperature",ylab="tension_strenght",main="nozzle_temperature & tension strenght")
plot(new_DF$bed_temperature,new_DF$tension_strenght,xlab="bed_temperature",ylab="tension_strenght",main="bed_temperature & tension strenght")
plot(new_DF$print_speed,new_DF$tension_strenght,xlab="print_speed",ylab="tension_strenght",main="print_speed & tension strenght")
plot(new_DF$fan_speed,new_DF$tension_strenght,xlab="fan_speed",ylab="tension_strenght",main="fan_speed & tension strenght")
plot(new_DF$elongation,new_DF$tension_strenght,xlab="elongation",ylab="tension_strenght",main="elongation & tension strenght")
library(corrplot)## Warning: package 'corrplot' was built under R version 4.5.3## corrplot 0.95 loadedcorrplot(cor(continous_data),method="number")
#bài toán 1 mẫu
n <- length(new_DF$tension_strenght)
xtb <- mean(new_DF$tension_strenght)
s <- sd(new_DF$tension_strenght)
data.frame(n,xtb,s)## n xtb s
## 1 50 20.08 8.925634qqnorm(new_DF$tension_strenght)
qqline(new_DF$tension_strenght)
shapiro.test(new_DF$tension_strenght) ##
## Shapiro-Wilk normality test
##
## data: new_DF$tension_strenght
## W = 0.9566, p-value = 0.06404epsilon <- qt(p=0.05/2,df=n-1,lower.tail=FALSE)*s/sqrt(n)
print(epsilon)## [1] 2.536637data.frame(u1=xtb-epsilon,u2=xtb+epsilon)## u1 u2
## 1 17.54336 22.61664#bài toán 2 mẫu KĐTB
abs_data <- subset(new_DF,material=="abs")
qqnorm(abs_data$tension_strenght)
qqline(abs_data$tension_strenght)
shapiro.test(abs_data$tension_strenght)##
## Shapiro-Wilk normality test
##
## data: abs_data$tension_strenght
## W = 0.90604, p-value = 0.02489pla_data <- subset(new_DF,material=="pla")
qqnorm(pla_data$tension_strenght)
qqline ( pla_data$tension_strenght)
shapiro.test(pla_data$tension_strenght)##
## Shapiro-Wilk normality test
##
## data: pla_data$tension_strenght
## W = 0.92466, p-value = 0.06547var.test(tension_strenght~material,new_DF,alternative="greater")##
## F test to compare two variances
##
## data: tension_strenght by material
## F = 1.3045, num df = 24, denom df = 24, p-value = 0.26
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
## 0.6575797 Inf
## sample estimates:
## ratio of variances
## 1.30448t.test(tension_strenght~material,new_DF,var.equal=T)##
## Two Sample t-test
##
## data: tension_strenght by material
## t = -2.0972, df = 48, p-value = 0.04126
## alternative hypothesis: true difference in means between group abs and group pla is not equal to 0
## 95 percent confidence interval:
## -10.028585 -0.211415
## sample estimates:
## mean in group abs mean in group pla
## 17.52 22.64#anova 1 nhân tố
new_DF$nozzle_temperature_2 <- ifelse(new_DF$nozzle_temperature < 220,"Group_1",
ifelse(new_DF$nozzle_temperature > 230 ,"Group_3","Group_2"))
Group_1 <- subset(new_DF,new_DF$nozzle_temperature_2=="Group_1")
Group_2 <- subset(new_DF,new_DF$nozzle_temperature_2=="Group_2")
Group_3 <- subset(new_DF,new_DF$nozzle_temperature_2=="Group_3")
qqnorm(Group_1$tension_strenght)
qqline(Group_1$tension_strenght)
shapiro.test(Group_1$tension_strenght)##
## Shapiro-Wilk normality test
##
## data: Group_1$tension_strenght
## W = 0.91815, p-value = 0.09128qqnorm(Group_2$tension_strenght)
qqline(Group_2$tension_strenght)
shapiro.test(Group_2$tension_strenght)##
## Shapiro-Wilk normality test
##
## data: Group_2$tension_strenght
## W = 0.95684, p-value = 0.4829qqnorm(Group_3$tension_strenght)
qqline(Group_3$tension_strenght)
shapiro.test(Group_3$tension_strenght)##
## Shapiro-Wilk normality test
##
## data: Group_3$tension_strenght
## W = 0.87204, p-value = 0.1056library(car)## Warning: package 'car' was built under R version 4.5.3## Loading required package: carData## Warning: package 'carData' was built under R version 4.5.3leveneTest(tension_strenght~as.factor(nozzle_temperature_2),data=new_DF)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.9575 0.3912
## 47ANOVA_model<-aov(tension_strenght~as.factor(nozzle_temperature_2),data=new_DF)
summary(ANOVA_model)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(nozzle_temperature_2) 2 688 343.9 5.026 0.0105 *
## Residuals 47 3216 68.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(ANOVA_model)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tension_strenght ~ as.factor(nozzle_temperature_2), data = new_DF)
##
## $`as.factor(nozzle_temperature_2)`
## diff lwr upr p adj
## Group_2-Group_1 -3.10 -9.430522 3.2305221 0.4678313
## Group_3-Group_1 -10.15 -17.903275 -2.3967255 0.0074607
## Group_3-Group_2 -7.05 -14.803275 0.7032745 0.0814970plot(TukeyHSD(ANOVA_model))
model_1<-lm(tension_strenght~layer_height+wall_thickness+infill_density+infill_pattern
+nozzle_temperature+bed_temperature+print_speed+material,data=new_DF)
summary(model_1)##
## Call:
## lm(formula = tension_strenght ~ layer_height + wall_thickness +
## infill_density + infill_pattern + nozzle_temperature + bed_temperature +
## print_speed + material, data = new_DF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4101 -3.8491 0.0338 3.9073 13.5086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 171.62809 54.21779 3.166 0.00292 **
## layer_height 55.59721 12.78771 4.348 8.87e-05 ***
## wall_thickness 1.06871 0.31942 3.346 0.00176 **
## infill_density 0.16286 0.03415 4.769 2.35e-05 ***
## infill_patternhoneycomb -1.14271 1.64581 -0.694 0.49140
## nozzle_temperature -1.04681 0.36901 -2.837 0.00705 **
## bed_temperature 1.00534 0.47426 2.120 0.04012 *
## print_speed -0.01559 0.03006 -0.519 0.60679
## materialpla -17.30508 8.51530 -2.032 0.04864 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.58 on 41 degrees of freedom
## Multiple R-squared: 0.673, Adjusted R-squared: 0.6092
## F-statistic: 10.55 on 8 and 41 DF, p-value: 6.91e-08model_2<-lm(tension_strenght~layer_height+wall_thickness+infill_density+
nozzle_temperature+bed_temperature+material,data=new_DF)
summary(model_2)##
## Call:
## lm(formula = tension_strenght ~ layer_height + wall_thickness +
## infill_density + nozzle_temperature + bed_temperature + material,
## data = new_DF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4717 -4.1695 0.0914 3.8586 13.9581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 167.02463 53.19880 3.140 0.003055 **
## layer_height 56.36695 12.44845 4.528 4.67e-05 ***
## wall_thickness 1.11169 0.28024 3.967 0.000271 ***
## infill_density 0.16643 0.03339 4.985 1.06e-05 ***
## nozzle_temperature -1.02890 0.36321 -2.833 0.006996 **
## bed_temperature 0.98346 0.46685 2.107 0.041026 *
## materialpla -17.10365 8.39202 -2.038 0.047722 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.501 on 43 degrees of freedom
## Multiple R-squared: 0.6667, Adjusted R-squared: 0.6201
## F-statistic: 14.33 on 6 and 43 DF, p-value: 6.783e-09par(mfrow=c(2,2))
plot(model_2)
shapiro.test(model_2$residuals)##
## Shapiro-Wilk normality test
##
## data: model_2$residuals
## W = 0.97458, p-value = 0.3517t.test(model_2$residuals)##
## One Sample t-test
##
## data: model_2$residuals
## t = -7.5559e-16, df = 49, p-value = 1
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.464546 1.464546
## sample estimates:
## mean of x
## -5.506598e-16library(lmtest)## Warning: package 'lmtest' was built under R version 4.5.3## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.5.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericbptest(model_2)##
## studentized Breusch-Pagan test
##
## data: model_2
## BP = 6.6773, df = 6, p-value = 0.3517dwtest(model_2)##
## Durbin-Watson test
##
## data: model_2
## DW = 1.5005, p-value = 0.01575
## alternative hypothesis: true autocorrelation is greater than 0```