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Abstract This research investigates the feasibility of electroless nickel deposition on additively manufactured stainless steel samples. The prevalent additive manufacturing techniques for metal components generate a surface with rough characteristics, which can result in a higher likelihood of fatigue and the initiation of cracks or fractures in the printed part. As a result, using as-manufactured components in the final product is impractical, which requires post-processing to create a smoother surface. This study assesses chempolishing (CP) and Electropolish (EP) techniques for post-processing additively manufactured stainless steel components. CP is a purely chemical process that involves continuous anodization of the sample, resulting in oxidation-reduction. CP has a significant advantage in creating a uniform and smooth surface, irrespective of the size or geometry of the component. Conversely, EP is an electrochemical process that necessitates an electric current to facilitate polishing. EP produces an exceptionally smooth surface that reduces surface roughness to a sub-micrometer level. We observed that EP and CP techniques reduced the surface roughness’s arithmetical mean height (Ra) by up to 4 μm and 10 μm, respectively. In this study, we investigate the application of electroless nickel deposition on additively manufactured (AM) components using different surface finishing techniques, including electro-polishing (EP), chemo-polishing (CP), and as-built components. Electroless nickel plating aims to enhance the surface hardness and resistance of manufactured components to withstand harsh environmental conditions. The electroless nickel plating process is less complicated than electroplating and does not require using an electric current through the chemical bath solution for nickel deposition. For this study, we used low-phosphorus (2–5% P), medium-phosphorus (6–9% P), and high-phosphorus (10–13% P) nickel solutions. We used the L9 Taguchi design of experiments (TDOE) to optimize these Ni deposition experiments, which consider solution content, surface finish, geometry plane, and bath temperature. The pre- and post-processed surfaces of the AM parts were analyzed using the KEYENCE Digital Microscope VHX-7000 and Phenom XL Desktop SEM. We apply a machine learning-based instance segmentation technique to improve the identification of nickel deposition and surface topology of microscopic images. Our experiments show that electroless nickel deposition produces uniform Ni coating on the additively manufactured components at up to 20 μm per hour. Mechanical properties of as-built and Ni-coated AM samples were evaluated using a standard 10 N scratch test. It was found that the nickel-coated AM samples were up to two times more scratch-resistant than the as-built samples. Based on our findings, we conclude that electroless nickel plating is a robust and viable option for surface hardening and finishing AM components for various applications and operating conditions.
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Machine Learning-Enabled Quantitative Analysis of Optically Obscure Scratches on Nickel-Plated Additively Manufactured (AM) Samples
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.
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- Award ID(s):
- 1914751
- PAR ID:
- 10529251
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Materials
- Volume:
- 16
- Issue:
- 18
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 6301
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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