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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. 

Abstract This paper explores nanoscale energy sensors and absorber metamaterials that can be used in various applications, such as solar cells and infrared detectors. It is possible to gain highly efficient and adjustable energy absorption, creating absorber metamaterials at the nanoscale that enhance the performance of solar cells. These metamaterials are based on molecular spintronics devices (MSD) and magnetic tunnel junctions (MTJ). The pillar shaped MTJs are made of two ferromagnetic metals separated by an insulating barrier, such as aluminum oxide (AlOx). The manufacturing process includes photoresist spin coating on a silicon wafer, photolithography, thin film sputtering, and liftoff. Following fabrication, the top and bottom electrodes are covalently bonded by a single molecule magnet (SMM) on the exposed side edges for strong magnetic coupling that changes the magnetic properties of both ferromagnetic metals. This study has considered different thin film deposition materials, configurations, and thicknesses. Magnetic field resonance and light reflectance measurements have been performed before and after molecule attachment to understand the molecule effect on the metamaterials’ energy absorption behavior. The Electron Spin Resonance (ESR) test revealed that the devices shifted following molecule attachment in both acoustic and optical modes. Moreover, due to molecule attachment, there have been significant alterations in the MTJ’s electromagnetic wave absorption characteristics with about 49% less reflectance. This metamaterial has various potential applications in aerospace, renewable energy, sensing, imaging, and communication. It is also a cheaper alternative to traditional solar cells and can inspire the development of smart metamaterials with selective absorption and tunable response. 

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. 

The effects of inlet flow rates on the purge durations in an atomic layer deposition (ALD) process are investigated through the simulation of three-dimensional laminar multicomponent flow in viscous flow reactors. The operating pressure and temperature are 10 torr (1333 Pa) and 300 °C, respectively. Purge durations in reactors with inlet located on the top surface of the reactor are compared with those in a base reactor with one inlet at the bottom surface of the reactor. It is found that purge durations are reduced by an increase in the flow rates, but they are independent from the number of inlets if the flow rates are maintained equal among different reactors. One exception is the reactor with one inlet at the center of the top surface of the reactor, which experiences the longest purge durations, most likely due to the axisymmetric gas injection in this reactor. The acquired results will provide a better understanding about designing efficient viscous flow reactors to reduce both purge duration and gas consumption. 

In this study, pressure drop ( ) across air-cooled heat sinks (HSs) are predicted using an artificial neural network (ANN). A multilayer feed-forward ANN architecture with two hidden layers is developed. Backpropagation algorithm is used for training the network, and the accuracy of the network is evaluated by the root mean square error. The input data for training the neural network is prepared through three-dimensional simulation of air inside the channels of heat sinks using a computational fluid dynamics (CFD) approach. The developed ANN-based model in this study predicts with a high accuracy and within of the CFD-based data. The present study suggests that developing an ANN-based model with a high level of accuracy overcomes the limitations of physics-based correlations that their accuracy strongly depends on identifying and implementing key variables that affect the physics of a thermo-fluid phenomenon.