## 5 Conclusions  
In this work, we suggested an approach to automate the computation of properties from DED-produced tracks using Python and the scikit package. The developed algorithm was tested using self-produced optical microscopy imaging from 18 Ni300 Marging steel DED depositions on H13 tool steel substrates using different parameters until optimised inputs were achieved. The approach resulted in the following assertions:  
* Track beads were successfully produced and analysed with a minimal presence of pores and defects: the optimised parameters consisted of a ratio between the laser power \(P\) and laser spot diameter \(d_{s}\) of 750 W mm\({}^{-1}\), a ratio between the scanning speed and feeding rate of 0.06 m g\({}^{-1}\), a shielding gas flow of either 25 or 30 L min\({}^{-1}\), depending on whether the application was cladding-based or component-based, carrier gas flow rate of 4 L min\({}^{-1}\) and a laser stand-off distance of 12 mm.
* A functioning tool capable of analysing single track depositions by identifying the substrate, areas above and below the substrate, the height, width, penetration and wettability angles were successfully built with an average analysis time per bead of 12.7 s. While this amount of time is considerable, it is worth mentioning that this is an improvement on the time it would take a human being to perform equal tasks, especially considering a large batch of images, such as presented in this work.
* Width and height properties were ascertained with errors of 2.71 and 4.01%, respectively.  
* The area above the substrate and area below the substrate were computed, displaying errors of 5.27 and 17.93%, respectively. The increase in error of the area below the substrate when compared to remaining outputs lies in the less pronounced contrast between the diluted zone and the substrate when compared to the contrast between the foreground and the background of the image.
* Dilution proportion values were computed with an average error of 14.15%, and with line beads whose dilution zone was smaller displayed worse accuracy.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12052755/s1](https://www.mdpi.com/article/10.3390/app12052755/s1)]([https://www.mdpi.com/article/10.3390/app12052755/s1](https://www.mdpi.com/article/10.3390/app12052755/s1)).  
Investigation, J.G.; Software, J.G., M.B.S., M.F.V., A.R.; Writing--original draft, J.G.; Conceptualization, A.J.J., J.M.R.S.T.; Methodology, M.B.S., M.F.V., A.R.; Funding Acquisition, A.d.J., A.R.; Writing--review and editing. A.d.J., J.M.R.S.T.; Supervision, J.M.R.S.T. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the funding of the PhD scholarship with the reference UI/BD/150684/2021, funded by Fundacao para a Ciencia e Tecnologia. The authors acknowledge the funding through the Add.Strength project, with reference PTDC/EME-EME/31307/2017, funded by FEDER and FCT. The authors would like to thank the support provided by Fundacao para a Ciencia e a Tecnologia of Portugal and IDMEC under LAETA-UIDB/50022/2020.  
Not applicable.  
Data Availability StatementData is made available and free to use through the supplementary data in MDPI's website.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
MAM Metallic additive manufacturing  
DED Direct energy deposition  
wDED Wire direct energy deposition  
OM Optical microscopy  
FGMs Functionally graded materials  
DLS Dynamic light scattering  
DOE Design of experiment  
SEM Scanning electron microscopy