## 3 Results
### Variation between Repeated Measurements  
The entire inspection was repeated three times for each specimen, which enables a characterization of the variation of measurements and, therefore, the reliability of measurements. The three repeated inspections were conducted in sequence and included the mounting and demounting of the specimen in the fixture, hence enabling the analysis of this variation in the study. In addition to the natural random variations of the measurement setup, the experiment is also prone to variations arising from the minuscule deviations from one mounting in the CMM to the next, effectively offsetting the inspection path. Figure 18 plots the three repeated inspections of HX1_Plane1 from specimen #6 from build 3 (Build3_#6_HX1_Plane1). Some minor variations are apparent in the figure, and even though the measured surface topology is close to identical, the minor deviations give rise to the three measured error values 0.062, 0.058, and 0.059 mm, respectively. The preliminary analysis indicates that there is indeed some variation between repeated measurements as introduced in Figure 18 and further detailed in Table 8. This preliminary analysis considers the measurements of cylindricity, diameter, and flatness of all the relevant features except for the 4 mm concave cylinder omitted due to residual powder not removable by pressurized air. With 7 cylindricity-, 7 diameter- and 12 flatness measurements for all 135 specimens, we obtain a total of 3510 data points for each repeated inspection. To analyze the variation between measurements, we compute the mean value of the three repeated measurements and--more importantly--the difference between the highest and the lowest value among the repeated measurements (\(\Delta\)). The characteristics of this data set are described in Table 8, where Rep 1-3 corresponds to the measured error of the first, second and third repeated inspections, respectively, Rep Mean is the mean value of the three repeated measurements and \(\Delta\) is the difference between the highest and lowest measured error. The negative values originate from deviations where the measured diameters are smaller than the nominal values. Form errors (i.e., cylindricity and flatness) may only take positive values since this is the distance between the most extreme positive and negative deviations from a perfect geometry. Figure 18: Plotted results for three repeated inspections for the flatness of Build3_#6_HX1_Plane1 of scale 500:1. Maximum and minimum points for each plot are highlighted with red circles. (**a**) 1 st inspection: 0.062 mm; (**b**) 2 nd inspection: 0.058 mm; (**c**) 3 rd inspection: 0.059 mm. Table 8 reveals a general declining trend in measured error through the repeated inspections, which can be explained by the removal of some residual powder during and between the inspections. This effect may be amplified by the fact that the probe is following the same path and might leave a trail or a slight indentation on the surface. Figure 19 contains a scatterplot where \(\Delta\) is plotted for the different characteristics, and outliers are clearly visible. These outliers contribute to a higher variance, which makes unfiltered data difficult to analyze graphically; hence, Figure 19 only includes the data points below five standard deviations (5\(\sigma\)). The width of the groups reflects the number of points. The plots of Figure 19 show a high-density of points close to zero, which indicates that the observed variations do not follow a normal distribution but rather a lognormal distribution where a higher density is observed close to zero. Figure 20 briefly explores this observation by fitting a lognormal distribution to the data and comparing this to normalized histograms of the distributions. These plots do indeed indicate that the distribution roughly follows a lognormal distribution where the measurements of diameter stand out as slightly less repeatable compared to the distributions of flatness and cylindricity.