This function takes a vector of strength values and a vector of measured thicknesses, and a nominal thickness and returns the normalized strength.

normalize_ply_thickness(strength, measured_thk, nom_thk)

## Arguments

strength

the strength to be normalized. Either a vector or a numeric

measured_thk

the measured thickness of the samples. Must be the same length as strength

nom_thk

the nominal thickness. Must be a single numeric value.

## Value

The normalized strength values

## Details

It is often necessary to normalize strength values so that variation in specimen thickness does not unnecessarily increase variation in strength. See CMH-17-1G, or other references, for information about the cases where normalization is appropriate.

Either cured ply thickness or laminate thickness may be used for measured_thk and nom_thk, as long as the same decision made for both values.

The formula applied is: $$normalized\,value = test\,value \frac{t_{measured}}{t_{nominal}}$$

If you need to normalize based on fiber volume fraction (or another method), you will first need to calculate the nominal cured ply thickness (or laminate thickness). Those calculations are outside the scope of this documentation.

## Examples

library(dplyr)

carbon.fabric.2 %>%
select(thickness, strength) %>%
mutate(normalized_strength = normalize_ply_thickness(strength,
thickness,
0.105)) %>%
#>    thickness strength normalized_strength
#> 1      0.112  142.817            152.3381
#> 2      0.113  135.901            146.2554
#> 3      0.113  132.511            142.6071
#> 4      0.112  135.586            144.6251
#> 5      0.113  125.145            134.6799
#> 6      0.113  135.203            145.5042
#> 7      0.113  128.547            138.3411
#> 8      0.113  127.709            137.4392
#> 9      0.113  127.074            136.7558
#> 10     0.114  126.879            137.7543

##    thickness strength normalized_strength
## 1      0.112  142.817            152.3381
## 2      0.113  135.901            146.2554
## 3      0.113  132.511            142.6071
## 4      0.112  135.586            144.6251
## 5      0.113  125.145            134.6799
## 6      0.113  135.203            145.5042
## 7      0.113  128.547            138.3411
## 8      0.113  127.709            137.4392
## 9      0.113  127.074            136.7558
## 10     0.114  126.879            137.7543