High Surface Quality Research Articles

Distributed surface mass balance of an avalanche-fed glacier

Abstract. Local snow redistribution processes such as avalanches can considerably impact the spatial variability of accumulation on glaciers. However, this spatial variability is difficult to quantify with traditional surface mass balance measurements or geodetic observations. Here, we leverage high-quality and high-resolution surface velocity and elevation change maps for the period 2012–2021 from Pléiades stereo images and ice thickness measurements of Argentière Glacier (France) to invert for its distributed surface mass balance. Three inversions are conducted using three different ice thickness modelling approaches, two of which are constrained by observations. The inversions all show very good agreement between inverted surface mass balance and in situ measurements (RMSE between 0.50 and 0.96 mw.e.yr-1 for the 11-year average). The detected spatial variability in surface mass balance is consistent between the modelling approaches and much higher than what is predicted from an enhanced-temperature-index model calibrated with measurements from a dense network of stakes. In particular, we find high accumulation rates at the base of steep headwalls on the left-hand side of the glacier, likely related to avalanche deposits at these locations. We calculate distributed precipitation correction factors to reconcile the outputs from the enhanced-temperature-index model with the inverted surface mass balance data. These correction factors agree with the outputs of a parametrisation of snow redistribution by avalanching, indicating an additional 60 % mass input relative to the accumulation from solid precipitation at these specific locations, which was equivalent to an additional 20 % mass accumulation at the scale of Argentière Glacier without its two smaller tributaries. Using these correction factors in a forward-modelling exercise, we show that explicitly accounting for avalanches leads to twice more ice being conserved in the Argentière catchment by 2100 in an RCP 4.5 climate scenario and to a considerably different ice thickness distribution. Our results highlight the need to better account for such spatially variable accumulation processes in glacio-hydrological models.

Double-Oxidant-Induced Slurry Reaction Mechanism and Performance on Chemical Mechanical Polishing of 4H-SiC (0001) Wafer.

Single-oxidant slurries are prevalently utilized in chemical and mechanical polishing (CMP) of 4H-SiC crystal. Nevertheless, it is a challenge to achieve a high material removal rate (MRR) and surface quality using single oxidant slurries to meet the needs of global planarization and damage-free nanoscale surface processing of SiC wafers. To solve this challenge, a novel method is proposed for SiC CMP processing using the double oxidant slurry. This slurry mainly consists of alumina (Al2O3) abrasive particles, potassium permanganate (KMnO4), potassium persulfate (K2S2O8), and deionized water. Post CMP, MRR is 1045 nm/h calculated by scratch method, and surface roughness Sa is 0.33 nm measured by 3D optical surface morphometer, with the measurement area of 868 μm × 868 μm. Both the MRR and Sa are far better than those under the single oxidant slurry condition. CMP mechanism with double-oxidant slurry conditions is elucidated by energy dispersive spectrometer and X-ray photoelectron spectrometer. Initially, the Si-C bond on the SiC wafer surface was oxidized by KMnO4, then MnO2 as the reduction product of KMnO4 can also catalyze the S2O82- to further oxidize SiC wafer surface, and eventually the oxidation layers were removed by mechanical action of nano-Al2O3 abrasive particles during CMP processing. These findings offer a pioneering approach and novel perspectives to achieve a higher MRR of 4H-SiC CMP, with potential implications for the application in high-performance SiC devices.

Lateral shear interferometry for shape accuracy measurements of 3D-printed micro-optics

Abstract The technique of 3D-printed micro-optics has experienced significant advancements over the last few years [1, 2]. This progressive evolution has been marked by remarkable strides in precision and miniaturization, making it particularly attractive to applications in the field of medical endoscopy. One noteworthy application entails the fabrication of micro-lenses directly on optical fibers, thereby creating endoscopic devices with unparalleled capabilities. A significant attribute of these micro-optical systems lies in their capability for precise imaging and their adept navigation through exceptionally narrow biological structures. For instance, the application of the 3D-printed endoscopy technology has facilitated insertion into the aorta of mice, as well as severely diseased human carotid arteries [2, 3]. This unprecedented level of accessibility and adaptability holds immense promise for diagnostic and therapeutic interventions in the realm of cardiovascular medicine and beyond. In the future, it may also be conceivable for micro-lenses to serve not only as imaging tools, but also as integral components of 3D-printing processes aimed at repairing damaged human tissue with bio-materials. For micro-lens production and quality control, performance assessment is invariably necessary to assure diffraction limited imaging in the different optical applications. With confocal surface profiling, only the surface shape information of micro-optics can be obtained. However, not only the shape of 3D-printed optics is relevant for its quality, but also its refractive index distribution is crucial when determining its overall optical performance. Therefore, the measurement of the wavefront is decisive in evaluating lens performance. Lateral shear interferometry presents itself as a compact and precise method for wavefront measurements. The only essential component required in the set-up is a pair of glass plates with high surface quality. Consequently, the entire set-up can be notably streamlined, as there is no requirement to construct an interferometer reference arm. This advantage facilitates the development of a notably compact set-up, making lateral shear interferometry particularly advantageous for integration with microscope and 3D-printer, despite the requirement for more complex algorithms in analyzing lateral shear interferograms. In this paper, simulation of shear interferogram fringes corresponding to wavefronts exhibiting varying aberrations are discussed. Additionally, this research encompasses measurement and analysis of shear interferograms obtained from wave fronts transmitted through different lenses. Through meticulous examination of these experimental results, the characteristics and performance of the tested optical systems are elucidated, contributing to a deeper understanding of wavefront analysis methodologies and their applicability in optical characterization studies.

A novel green CeO2 polishing slurry and its chemical mechanical action mechanism for achieving atomic-level smoothing of fused silica glass surfaces

Pitch-based tools are integral to computer-controlled optical surfacing (CCOS) for polishing fused silica but face limitations such as low material removal rates (MRR) and challenges in achieving atomic-level smoothing while ensuring environmental sustainability. While chemical mechanical polishing (CMP) excels in processing planar surfaces, it struggles with complex freeform geometries. CCOS addresses this gap with specialized tool head designs that enable precise machining of intricate surfaces. This study introduces an eco-friendly CeO2-based polishing slurry suited for CCOS with pitch tools. By incorporating green reagents, such as polyethylene glycol (PEG) and ethylene glycol (EG), and minimizing harmful chemical usage, the slurry achieves significant breakthroughs in performance. Experimental results reveal a surface roughness (Sa) of 0.075nm, meeting atomic-level smoothing standards, alongside an MRR of 40.5 μm /h, marking substantial improvements over traditional methods. Physicochemical analyses of the CeO2 abrasives, including particle size, morphology, and Ce element content, revealed that abrasives with sharp edges and high Ce content are key factors for obtaining high surface quality and MRR. Molecular dynamics (MD) simulations highlight the synergistic effects of the components, optimizing chemical-mechanical interactions to enhance surface quality. These findings demonstrate the potential of green polishing technologies in advancing the precision machining of fused silica, particularly for complex geometries. The study provides a sustainable and high-performance solution for achieving atomic-level smoothing, paving the way for broader applications in precision optics.

Optimizing CNC turning of AISI D3 tool steel using Al₂O₃/graphene nanofluid and machine learning algorithms

Turning AISI (American Iron and Steel Institute) D3 tool steel can be challenging due to a lack of optimal process parameters and proper coolant application to achieve high surface quality and temperature control. Machine learning helps in predicting the optimal parameters, whereas nanofluids enhance cooling efficiency while preserving both the tool and the workpiece. This work intends to utilize advanced machine learning approaches to optimize process parameters with the application of hybrid nanofluids (Al2O3/graphene) during the CNC turning of AISI D3. The Response Surface Methodology (RSM), Back Propagation (BP) neural networks, and Genetic Algorithms (GA) will be utilized to model and predict optimal turning parameters to enhance surface quality and manage tool tip temperature. The experiments ranged the cutting speed, nanofluid concentration, depth of cut, and feed rate from 150 to 180 m/min, 0.3 to 0.9 wt.%, 0.5 to 0.9 mm, and 0.03-0.07 mm/rev. RSM and ANN analyses showed that cutting speed and feed rate had a significant effect on surface quality, contributing 11.5% and 10.5%, respectively, whereas the nanofluid affected tool tip temperature by 42.5%. The GA determined that the optimal cutting speed became 150 m/min, the feed rate was 0.05 mm/rev, the cutting depth was 0.6 mm, and the nanofluid concentration was 0.8%. At temperatures ranging from 23.01°C to 28.41°C, these conditions produced a desirable surface roughness of 0.16 to 0.45 μm. The findings emphasize the benefits of utilizing Al2O3/graphene nanofluid and machine learning algorithms in CNC turning to improve surface roughness and control temperature.

Fabrication of micro holes with confined pitting corrosion by laser and electrochemical machining: pitting corrosion formation mechanisms and protection method

Laser and electrochemical hybrid machining (LECM) combines the advantages of high efficiency of laser processing and high surface quality of electrochemical machining and has been employed to process deep micro holes with high surface quality, high precision, and efficiency. However, surface pitting corrosion occurs around the entrance of the micro holes drilled by LECM, which deteriorates their surface quality and mechanical properties. This study revealed the mechanism of surface pitting corrosion formation mechanisms during LECM by characterizing surface micromorphology, chemical composition, microstructures, and surface stress. The difference between surface pitting corrosion area during LECM and the stray current corrosion during electrochemical machining was studied. Micro solid metal particles and inner microcavities were observed in micro pits. The depth of the micro pits was greater than that obtained using electrochemical machining. It has been concluded that in LECM, the surface pitting corrosion occurred owing to the enhanced stray current corrosion and the accumulation of solidified melt particles and cavitation microbubbles in the micro pits. Coaxial gas-assisted LECM was also proposed to restrict the surface pitting corrosion area. Experiments and simulations were conducted to verify the feasibility of minimizing the corrosion area using coaxial gas. The surface pitting corrosion area has been decreased by 85.1% at a coaxial gas pressure of 0.1MPa compared with that without coaxial gas assistance. Finally, the radial cooling holes with a diameter of 1.2mm and an aspect ratio of 125:1 in turbine blades with high surface quality were fabricated. This study provides a promising method to fabricate high-aspect-ratio micro-holes with high surface quality and high efficiency.

Poly(vinyl alcohol)-Assisted Exfoliation and Grafting Modification of Boron Nitride Nanosheets with a High Aspect Ratio via a Continuous Production Method for Energy Storage and Thermal Management.

Hexagonal boron nitride nanosheets (BNNS) integrating many extraordinary properties are usually combined with polymers to fabricate multifunctional dielectric materials for energy storage and thermal management. Unfortunately, the existing technologies for producing BNNS generally have the disadvantages of inefficiency and low product quality, which significantly hinder the wide application of high-value nanocomposites. Herein, a novel poly(vinyl alcohol) (PVA)-assisted pan-milling method based on the innovative solid-state shear milling (S3 M) technology is reported for the production of high-quality BNNS. The uniform three-dimensional (3D) shear force field generated by pan milling not only ensured the high aspect ratio of BNNS (the average lateral size of ∼954 nm and the average thickness of ∼3.7 nm) but also induced the mechanochemical reaction between PVA and BNNS to achieve in situ grafting modification on the BNNS surface. The simultaneous high-quality exfoliation and surface modification of BNNS positively contributed to the mechanical, dielectric, and thermal conduction properties of BNNS/PVA nanocomposites. At the BNNS content of 10 wt %, the BNNS/PVA composite film exhibited excellent robustness (Young's modulus of 5305 MPa and the tensile strength of 83 MPa), with a satisfactory breakdown strength (Eb of 134.7 MV/m) and dielectric constant (∼5 at 1000 Hz). In addition, the substantial improvement of thermal conductivity (from 0.88 to 5.59 W·m-1·K-1) guaranteed the stability of the dielectric properties of the nanocomposites at high temperatures. This pan-milling method characterized by green and efficiency is highly scalable to other two-dimensional (2D) layered materials, providing a practical strategy for the industrial fabrication of nanocomposites in elevated-temperature energy storage.

The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain magnetic resonance (MR) images for the dHCP neonatal dataset. However, the current implementation of the pipeline requires more than 6.5 h to process a single MRI scan, making it expensive for large-scale neuroimaging studies. In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation. We introduce a multiscale deformation network to learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted brain MRI. A fast unsupervised spherical mapping approach is integrated to minimize metric distortions between cortical surfaces and projected spheres. The entire workflow of our DL-based dHCP pipeline completes within only 24 s on a modern GPU, which is nearly 1000 times faster than the original dHCP pipeline. The qualitative assessment demonstrates that for 82.5% of the test samples, the cortical surfaces reconstructed by our DL-based pipeline achieve superior (54.2%) or equal (28.3%) surface quality compared to the original dHCP pipeline.

Investigation of anisotropic characteristics in ultra-precision grinding of sapphire crystal planes and strategies for suppression

Sapphire has been extensively utilized due to its excellent infrared transmittance. The demand for sapphire optical components has transitioned from flat geometries to more complex curved geometries. This transition necessitates the processing of sapphire from a single crystal plane to multiple crystal planes, presenting a significant challenge. To gain a comprehensive understanding of the anisotropy present in the machining of sapphire multiple crystal planes and to explore strategies for its suppression, various grinding wheels were employed for ultra-precision grinding of the sapphire A-plane, C-plane, M-plane, N-plane, and R-plane. This investigation encompassed multiple facets, including surface roughness, surface topography, residual stress, acoustic emission time-frequency characteristics, and infrared transmittance. A thorough analysis was conducted on the morphology of corrosion damage and the anisotropic mechanisms involved in sapphire ultra-precision grinding, as well as the resulting grinding quality following the implementation of suppression strategies. The findings indicate that the A-plane, C-plane, and M-plane of sapphire exhibit significant anisotropic characteristics when ground with the D25 resin bond grinding wheel, whereas the N-plane and R-plane do not display such characteristics. Furthermore, the application of the D3 ceramic bond grinding wheel effectively suppresses the anisotropy in the grinding of different sapphire crystal planes, achieving a high-quality surface finish with a ductile domain exceeding 90 %, and ensuring high uniformity in the ultra-precision grinding of various sapphire crystal planes.

Comparative study and experimental validation of cutting forces in high-speed ball-end milling for AL 7075-T6 and AL 6061-T6 alloys

This study examines the importance of precise cutting force prediction during high-speed ball-end milling of the AL 7075-T6 and AL 6061-T6 aluminum alloys, which are widely used in the aerospace and automotive industries, because to their strength and fatigue resistance. Predicting cutting forces is critical for optimizing milling conditions and creating high-quality surfaces. We have used a mechanistic model to estimate cutting forces based on experimental data. To generate the more precise predictions, the developed model takes into account tool geometry, cutting settings and material properties. We have measure the cutting forces on a CNC SOMAB DIAM 850 milling machine by using a Kistler 9257A dynamometer. We have done simulations with MATLAB software. The results reveal that the mechanistic model accurately predicts both alloys. ALU 7075-T6 produces higher cutting forces because to its increased strength. This comparative research demonstrates that the mechanistic approach is useful in forecasting cutting forces in high-speed ball-end milling of aluminum alloys, providing useful insights for improving machining operations.

Effect of laser powder bed fusion gas flow rate on microstructure and mechanical properties of 316 L stainless steel

The inert protective gas flow rate during laser powder bed fusion (LPBF) molding is an important environmental condition. The protective gas not only prevents parts from being oxidizing, but also significantly affects spatter particle removal and particle migration on the powder bed surface. There are relatively few systematic comparative studies reported on the effect of gas flow rate on the movement of two types of powders. Therefore, in this study, three flow rate environments, low, medium and high, were designed for multi-scale experimental analysis of the quality, microstructure, mechanical properties and defects of molded samples. It was observed that the movement of the powder that dominated the sample defects was different in the three environments as the gas flow velocity was adjusted. Under low flow conditions, spatter particles are the main contributor to the creation of defects such as porosity, resulting in poor surface quality (TOP: Sq = 14.023, Side: Sq = 22.006), elevated surface oxygen content (Wt. = 1.7 %), increased grain size, and i a higher number of pores and spatter particles. Consequently, this led to the lowest density, microhardness, and mechanical properties among the three sample groups. High flow rates increased the removal of spatter particles compared to low flow rates, resulting in the highest surface quality (top: Sq = 11.528, Side: Sq = 14.051) and the lowest surface oxygen content (Wt. = 0.88 %). However, under the influence of high flow rates, surface particle migration becomes an important factor in defects, leading to an increase in grain size, porosity, and unfused particles compared to medium flow rates. This leads to a decrease in density, microhardness and elongation. This study reveals, through multi-scale experimental characterization, the mechanism by which varying air flow rates affect powder movement, and hence the microstructure and properties of parts. These findings provide new insights for device development and coupled multi-physics field computational studies.

3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting

In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction with intricate details while inheriting the high efficiency and rendering quality of 3DGS. The key insight is to incorporate an implicit signed distance field (SDF) within 3D Gaussians for surface modeling, and to enable the alignment and joint optimization of both SDF and 3D Gaussians. To achieve this, we design coupling strategies that align and associate the SDF with 3D Gaussians, allowing for unified optimization and enforcing surface constraints on the 3D Gaussians. With alignment, optimizing the 3D Gaussians provides supervisory signals for SDF learning, enabling the reconstruction of intricate details. However, this only offers sparse supervisory signals to the SDF at locations occupied by Gaussians, which is insufficient for learning a continuous SDF. Then, to address this limitation, we incorporate volumetric rendering and align the rendered geometric attributes (depth, normal) with that derived from 3DGS. In sum, these two designs allow SDF and 3DGS to be aligned, jointly optimized, and mutually boosted. Our extensive experimental results demonstrate that our 3DGSR enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS. Besides, our method competes favorably with leading surface reconstruction techniques while offering a more efficient learning process and much better rendering qualities.

A class of new tuned primal subdivision schemes with high-quality limit surface in extraordinary regions

We propose a unified tuning framework for primal subdivision schemes that are generalizations of odd-degree uniform B-spline surfaces for unstructured quadrilateral meshes of arbitrary topology. The subdivision of the resulting tuned primal subdivision (TPS) schemes is performed through efficient repeated local refinement operations. One level of subdivision of TPS-schemes is decomposed into one step of simple topological splitting plus an additional series of repeated local smoothing operations. The unified tuning framework optimizes subdivision rules for both topological splitting and smoothing operations near extraordinary vertices by minimizing the curvature fluctuation of the second-order characteristic maps of the respective TPS-scheme. To validate the limit surface quality of TPS-schemes in extraordinary regions, a mesh-independent metric is also devised to estimate local curvature variation in addition to highlight lines and direct curvature evaluation. With (p - 1)/2 steps of smoothing operations in each level of subdivision, the respective TPS-scheme is termed as a degree-p scheme that produces global C p -1 limit surfaces everywhere except at a finite number of extraordinary positions where near- G 2 continuity is achieved. The limit surface of TPS-schemes in extraordinary regions also exhibits appealing highlight lines, and the larger the number of smoothing operations applied in each level of subdivision, the better the final limit surface quality. One of the key advantages of the proposed tuning framework for TPS-schemes is that the optimization of relevant subdivision rules is performed by tuning structured operations of topological splitting and repeated smoothing that involve direct neighbors of an extraordinary vertex only without the need of tuning large subdivision stencils. All subdivision operations of TPS-schemes are local, involving one-ring of neighboring vertices only, which is efficient for high-degree surface subdivision and convenient for practical implementation. Numerical examples also validate the superiority of TPS-schemes over other state-of-the-art subdivision methods in terms of both highlight lines and various curvature measures.

Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset, without resorting to Poisson reconstruction or TSDF fusion as in previous work. We approximate the surface normal of Gaussians as the normal of the ray-Gaussian intersection plane, enabling the application of regularization that significantly enhances geometry. Furthermore, we develop an efficient geometry extraction method utilizing Marching Tetrahedra, where the tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis. Further, it compares favorably to or even outperforms, neural implicit methods in both quality and speed.

StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal

This work addresses the challenge of high-quality surface normal estimation from monocular colored inputs (i.e., images and videos), a field which has recently been revolutionized by repurposing diffusion priors. However, previous attempts still struggle with stochastic inference, conflicting with the deterministic nature of the Image2Normal task, and costly ensembling step, which slows down the estimation process. Our method, StableNormal, mitigates the stochasticity of the diffusion process by reducing inference variance, thus producing "Stable-and-Sharp" normal estimates without any additional ensembling process. StableNormal works robustly under challenging imaging conditions, such as extreme lighting, blurring, and low quality. It is also robust against transparent and reflective surfaces, as well as cluttered scenes with numerous objects. Specifically, StableNormal employs a coarse-to-fine strategy, which starts with a one-step normal estimator (YOSO) to derive an initial normal guess, that is relatively coarse but reliable, then followed by a semantic-guided refinement process (SG-DRN) that refines the normals to recover geometric details. The effectiveness of StableNormal is demonstrated through competitive performance in standard datasets such as DIODE-indoor, iBims, ScannetV2 and NYUv2, and also in various downstream tasks, such as surface reconstruction and normal enhancement. These results evidence that StableNormal retains both the "stability" and "sharpness" for accurate normal estimation. StableNormal represents a baby attempt to repurpose diffusion priors for deterministic estimation. To democratize this, code and models have been publicly available in hf.co/Stable-X.

Spectral whitening based seismic data preprocessing technique to improve the quality of surface wave's velocity spectra

A high-quality surface wave velocity spectrum, also known as a dispersion image, is paramount for any MASW survey to accurately predict subsurface earth properties. The presence of diversified noise during field acquisition and dissimilar attenuation due to mechanical and radial damping makes it challenging for any wavefield transformation technique to produce a detailed and precise velocity spectrum. Standard surface wave data preprocessing techniques, such as trace normalization and bandpass filtering, along with postprocessing techniques like frequency-wise amplitude normalization, fail to address all these issues appropriately. In this paper, we present a spectral whitening-based data preprocessing technique that can adequately eradicate most of the shortcomings associated with different wavefield transformation techniques. Instead of normalizing each trace, it normalizes the amplitude of every frequency present in the seismogram. The spectral whitening can regain the relative amplitude losses due to both radial and mechanical damping, thus improving the signal-to-noise ratio. Along with diversified field data including Love and Rayleigh wave surveys, a synthetic dataset is used to demonstrate the efficacy of the proposed technique. Furthermore, field noise is added to random traces to test the ability of the proposed technique to filter asymmetric noise. Overall, the spectral whitening procedure significantly improves the quality of the velocity spectrum and produces a sharper dispersion image with well-separated modes. The work presented here enhances our ability to interpret surface wave velocity spectra precisely and helps explore accurate properties of the subsurface earth. It can help avoid the need for repeated field tests in cases of extremely noisy data, thereby significantly reducing costs and saving time.

Controllable vertical and radial corrosion by step flow fields for fabricating large aspect ratio micro-cone arrays in through-mask electrochemical micromachining

Ionic liquid electrospray thrusters (ILET) have the advantages of high efficiency, small size and low power consumption, and are widely used in micro and nanosatellite propulsion systems. As the core component of the electrospray thruster, the height and tip diameter of the micro-cone emitter array determine the performance of the thrust system. To increase the height of the micro-cone emitter, a through-mask electrochemical micromachining (TMEMM) processing method was proposed in this study. The eddy current generated under the mask in the low-speed flow field was innovatively used to make the gas and solid products gather on the processing side wall to form a product film, which effectively reduced the radial corrosion rate and achieved higher longitudinal processing. In sequence, high-speed flow field was applied to achieve high radial corrosion rate, high contour accuracy and high surface quality. By switching the low-speed flow field and the high-speed flow field, the vertical and radial corrosion rates were controlled. Finally, a microcone array with a height of 256.2 μm and a tip diameter of 20.3 μm was fabricated.

A Parametric Study on the Stability, Geometry and Hardness of AISI 308LSi WAAM-GMAW

Wire arc additive manufacturing (WAAM) has been established to be an efficient and cost-effective additive manufacturing technique for fabricating functional metallic parts from scratch. However, there is need to determine optimal processing condition for each material system to produce high-quality parts. In this work, a parametric study of WAAM of AISI 308LSi was performed to determine the processing condition(s) at which single tracks of high dimensional accuracy, excellent geometry, no visible crack and pore, and high hardness required for high-quality multi-track deposition can be achieved. The track geometries were investigated using a combination of optical microscopy and image processing software. The microstructure and hardness of the deposited single tracks were examined using optical microscopy and Vickers hardness tester respectively. A process map predicting the process stability of WAAM of AISI 308LSi was developed within a process window. Continuous single tracks of high dimensional accuracy were produced from a stable deposition process. The process becomes unstable whenever the wire deposition volume per unit length of track is in excess of the available heat energy per unit length of track. The wire feed rate and traverse speed significantly influence the stability and geometry of the single tracks. The processing conditions at which single tracks of low wetting angle (<90◦), high aspect ratio (>1.5), high surface quality, and high hardness (close to the as-received material) can be deposited were determined. These processing conditions were considered suitable for the fabrication, surface modification and repair of functional engineering parts made of 308LSi stainless steel.

Optimisation of chemically assisted mechanical polishing process parameters for polycrystalline diamond based on photo-Fenton reaction

Polycrystalline diamond (PCD) has ultra-high thermal conductivity and is suitable for use as a thermal substrate material for semiconductor power devices. However, the high hardness, good wear resistance and chemical inertness of PCD lead to low machining efficiency and poor machined surface quality. Chemically assisted mechanical polishing based on the photo-Fenton reaction enables efficient PCD processing and high surface quality. In this study, Box-Behnken response surface experiments were employed to investigate the influence of the photo-Fenton reaction polishing process parameters (H₂O₂ concentration, light intensity, polishing pressure and abrasive concentration) and their interactions on the polishing process. Regression equations were constructed to model the material removal rate (MRR) and surface roughness (Ra) and to optimise the process parameters. Finally, the polishing mechanism was analysed. The results demonstrated that the effects of polishing pressure, polishing pressure-abrasive concentration, abrasive concentration and H2O2 concentration on the material removal rate were statistically significant. Furthermore, the effects of polishing pressure, H2O2 concentration, abrasive concentration, polishing pressure-abrasive concentration, H2O2 concentration-light intensity and light intensity on the surface roughness were also found to be statistically significant. The optimal process parameters were identified as a light intensity of 150 mW/cm2, a H₂O₂ concentration of 15 wt%, a polishing pressure of 0.89 MPa, and an abrasive concentration of 5 wt%. These parameters yielded a material removal rate of up to 666.9 nm/h and a surface roughness of Ra 2.58 nm. The polishing mechanism of the photo-Fenton reaction on PCD can be described as follows: the photo-Fenton reaction produces •OH, which then oxidises the PCD surface to generate an oxide layer. This layer is subsequently removed by the mechanical action of the abrasive, and the oxidation and mechanical removal continue to occur on the exposed new PCD surface. It has been demonstrated that, under the condition of balance between the mechanical action and the chemical action, a better quality of the surface can be obtained. This paper offers further evidence in support of the use of the photo-Fenton reaction in the context of PCD polishing.

Optimizing the surface quality of electrochemical machined Ni-based single crystal superalloy via manipulation of phase and dendrite dissolution

The transpassive dissolution behaviors of Ni-based single crystal superalloy in 10 %NaCl, 10 %NaNO3, and mixed solution are investigated by using potentiodynamic polarization, scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. The alloy exhibits opposite yet complementary dissolution characteristics in NaCl and NaNO3 solutions. The different responses of γ/γ′ phases to Cl− and NO3− contribute to the opposite dissolution behaviors of dendrites and interdendritic regions. The surface flatness is noticeably enhanced in mixed solution due to the synchronous dissolution of the γ/γ′ phases. Our work paves the way for obtaining high-quality surface in electrochemical machining of single crystal superalloys.