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Electroless coating brings the advantage of providing films on the complex geometry of additively manufactured components. However, there is a knowledge gap about the impact of AM part surface and postprocessing parameters on the quality of electroless coating. This study explores the application of three solution-based surface finishing techniques on the microstructure and surface hardness of additively manufactured stainless steel components coated with electroless nickel films. Given that AM techniques for metal parts often yield surfaces with inherently rough textures and differences in properties along the different planes, we investigated their relationship with nickel coating. To mitigate the impact of surface irregularities on electroless nickel coating quality, this research evaluated the effectiveness of chemical polishing (CP) and Electropolishing (EP) as post-processing treatments for AM stainless steel. Characterization of the treated samples was conducted using the analytical Digital Microscope, Scanning Electron Microscope (SEM), and scratch tester. Additionally, the study incorporated an instant segmentation machine learning algorithm to overcome image analysis challenges. The findings indicate that EP and CP significantly improve surface smoothness, decreasing the arithmetical mean height (Ra) by as much as 4 µm and 10 µm, respectively. Furthermore, the nickel-coated AM samples demonstrated an enhancement in scratch resistance, exhibiting up to a two-fold increase in surface hardness compared to their as-built counterparts. Taguchi design of the experiment was applied to investigate the effect of process parameters. This study provides insights for developing improved surface quality and acquiring new properties via the coating process to make AM parts suitable for challenging environments and novel applications. 

In this paper, electroless nickel plating is explored for the protection of binder-jetting-based additively manufactured (AM) composite materials. Electroless nickel plating was attempted on binder-jetted composites composed of stainless steel and bronze, resulting in differences in the physicochemical properties. We investigated the impact of surface finishing, plating solution chemistry, and plating parameters to attain a wide range of surface morphologies and roughness levels. We employed the Keyence microscope to quantitatively evaluate dramatically different surface properties before and after the coating of AM composites. Scanning electron microscopy revealed a wide range of microstructural properties in relation to each combination of surface finishing and coating parameters. We studied chempolishing, plasma cleaning, and organic cleaning as the surface preparation methods prior to coating. We found that surface preparation dictated the surface roughness. Taguchi statistical analysis was performed to investigate the relative strength of experimental factors and interconnectedness among process parameters to attain optimum coating qualities. The quantitative impacts of phosphorous level, temperature, surface preparation, and time factor on the roughness of the nickel-plated surface were 17.95%, 8.2%, 50.02%, and 13.21%, respectively. On the other hand, the quantitative impacts of phosphorous level, temperature, surface preparation, and time factor on the thickness of nickel plating were 35.12%, 41.40%, 3.87%, and 18.24%, respectively. The optimum combination of the factors’ level projected the lowest roughness of Ra at 7.76 µm. The optimum combination of the factors’ level projected the maximum achievable thickness of ~149 µm. This paper provides insights into coating process for overcoming the sensitivity of AM composites in hazardous application spaces via robust coating. 

Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering.