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Materials Science in Additive Manufacturing Data imputation strategies of PBF Ti64

Figure 5. Graphs comparing the distributions of the observed (red) and imputed (green) energy density values. Top: Boxplot of the observed and imputed
energy density values. Middle: Kernel density plot (line) with histogram (bars) of the observed and imputed energy density values. Bottom: Cumulative
distribution plot of the observed and imputed energy density values.

Figure 6. Graphs comparing the distributions of the observed and imputed values of exposure duration, laser focus, point distance, and Young’s modulus
for multivariate imputation by chained equations-imputed dataset, where RF is relative frequency and P is probability.

The variables exposure duration and point distance literature that provided the values for laser focus did not
are only applicable to pulse wave laser for SLM. Since state the laser spot size, and since there are several different
the dataset contains data from both pulse wave laser and ways to define the beam diameter , it is difficult to obtain
[39]
continuous wave laser, the imputed values for these two the correct relationship between laser focus and laser spot
variables would be for continuous wave laser parameter for each observed value of laser focus. This could be a
sets, which would not be relevant. contributing factor, together with the limited data for laser
According to the literature , laser focus is a parameter focus, which led to the imputed values deviating from the
[38]
that determines the laser spot size. However, the original original distribution.

Volume 2 Issue 1 (2023) 7 https://doi.org/10.36922/msam.50