High-precision Processing Research Articles

Anodic Dissolution Characteristics of GH4169 Alloy in NaNO3 Solutions by Roll-Print Mask Electrochemical Machining Using the Linear Cathode.

GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the workpiece surface can enhance its properties further. Through-mask electrochemical micromachining (TMEMM) is a promising and potential processing method for nickel-based superalloys. It can effectively solve the problem that traditional processing methods are difficult to achieve large-scale, high-precision and efficiency processing of surface micro-structure. This study explores the feasibility of electrochemical machining (ECM) for GH4169 using roll-print mask electrochemical machining with a linear cathode. Electrochemical dissolution characteristics of GH4169 alloy were analyzed in various electrolyte solutions and concentrations. Key parameters including cathode sizes, applied voltage and corrosion time were studied in the roll-print mask electrochemical machining. A qualitative model for micro-pit formation on GH4169 was established. Optimal parameters were determined through experiments: 300 μm mask hole and cathode size, 10 wt% NaNO3 electrolyte, 12 V voltage, 6 s corrosion time. The results demonstrate that the micro-pits with a diameter of 402.3 μm, depth of 92.8 μm and etch factor (EF) of 1.81 show an excellent profile and localization.

A Study on the Design of High-precision Rotation Processing Unit

A Study on the Design of High-precision Rotation Processing Unit

Physics-guided neural network for grinding temperature prediction

Creep-feed grinding is a high-efficiency, high-precision grinding process widely used in the manufacturing of aviation engines. However, the workpiece burn and other quality issues caused by high processing temperature limit the yield of grinding. Therefore, the accurate model of grinding temperature has become the key to improving processing efficiency and quality. Different from the traditional physical or data-driven models, this paper attempts to combine both perspectives based on Physics-Guided Neural Networks (PGNN) to accurately predict grinding temperature with a small number of experiments. At the level of data acquisition, real grinding experiment data was obtained and a data augmentation method had been proposed. At the level of neural network structure, optimisation processes were implemented to enhance prediction performance, and a physics-guided loss function was inserted to guide network training. The experiment results shows that PGNN had better prediction accuracy than the physical model, while also mitigating the limitations of data-driven models on small sample sets. PGNN also performed better with noisy data and predictions out of the training data range, this reveals the benefits of PGNN for small sample problems in processing scenarios.

Lab on a chip (LOC) platform for drug screening against the intracellular forms of Trypanosoma cruzi

Trypanosoma cruzi is the etiological agent of Chagas disease, endemic in Latin America with recent spreading to non-endemic areas due to human migration. The low efficacy and substantial prevalence of side effects of the approved medications benznidazole (BZN) and nifurtimox observed mainly during the chronic phase of Chagas disease, when most patients receive their diagnosis, demonstrate the need to find new therapeutic compounds to address this neglected global health disease. Here, we developed a platform for screening the antitrypanosomal activity of compounds over intracellular amastigotes using microfluidics, LOC devices, and advanced image analysis algorithms. This platform allows the simultaneous testing of six different concentrations of a compound and the automatic and high-precision processing of the images obtained during the experimental phase, providing quickly and easily a multitude of statistical data for the screening of new drugs. To assess the developed platform, we analyzed the effects of BZN as a reference drug and compared the dose–response curves with those obtained with a flow cytometry-based method, obtaining similar IC50 results. Furthermore, a collection of compounds obtained by chemical synthesis from a combinatorial library was tested over intracellular amastigotes in infected Vero cells.

Deterioration micro-mechanism of graded aggregates with different gradations under vibratory compaction using X-CT testing

This study aims to explore the compaction deterioration micro-mechanism of high-speed railway graded aggregate (HRGA) fillers and further improve the compaction quality of HRGA fillers, contributing to improving subgrade service performance. Firstly, based on the vibratory compaction experiments, the compaction deterioration macro-characteristics of HRGA fillers with different gradations were revealed from the mechanical properties (i.e., dynamic stiffness Kd and subgrade reaction modulus K20). Secondly, X-ray Computed Tomography (X-CT) testing was conducted based on the multiple typical compaction stages selected from the Kd curve. The internal microstructure (i.e., particles and voids) characteristics within HRGA fillers with different gradations were measured by high-precision image processing approaches. Finally, the evolution characteristics of Kd, K20, and microstructure were used to reveal the compaction deterioration micro-mechanism of HRGA fillers with different gradations, and further explore the relationship between filler gradation and compaction deterioration. The results indicated that compaction deterioration characteristics of HRGA fillers with different gradations could be characterized by the inflection points of Kd and K20 curves. Besides, during compaction deterioration, particle abrasion crushing and the increase in surface edges and corners of the void reduced the stability of the skeleton structure within HRGA fillers with different gradations, resulting in a gradual decrease of Kd and K20. Furthermore, the relationship between Kd, K20, and microstructure with gradation could be quantitatively characterized by quadratic functions. Hence, based on X-CT measurement, it was feasible to reveal the compaction deterioration mechanism of HRGA fillers from macro- and micro-perspectives. This study contributes to establishing a theoretical framework for revealing the compaction deterioration of high-speed railway subgrade fillers and improving the quality of vibratory compaction for on-site subgrade construction.

3DOF displacement sensor based on the self-imaging effect of optical micro-gratings

In recent years, there has been an increasing demand for a multiple degrees of freedom (DOF) measurement system with high performance and high integration. Here, we report a 3DOF displacement sensor based on the self-imaging effect of optical micro-gratings. The optical field distribution behind a micro-grating with a period of 3 µm is analyzed theoretically. The transmission properties of a double-grating structure are investigated in theory. In the experiment, 3DOF displacement measurement within a range of 1 mm is demonstrated. Using an interpolation circuit with a subdividing factor of 1000, displacement measurement with a theoretical resolution of 3 nm is realized. The experimental resolution is ∼8nm. An error within 2 µm is obtained experimentally within a range of 1 mm for 3DOF measurement. With a few optical components such as a beam splitter prism and beam expanders, the sensor shows potential in developing ultra-compact multi-DOF displacement measuring systems. Together with a nanometric resolution, the 3DOF displacement sensor has shown great potential in applications such as high-precision mechanical engineering and semiconductor processing.

3D-printed, citrate-based bioresorbable vascular scaffolds for coronary artery angioplasty

Fully bioresorbable vascular scaffolds (BVSs) aim to overcome the limitations of metallic drug-eluting stents (DESs). However, polymer-based BVSs, such as Abbott's Absorb, the only US FDA-approved BVS, have had limited use due to increased strut thickness (157 μm for Absorb), exacerbated tissue inflammation, and increased risk of major cardiac events leading to inferior clinical performance when compared to metallic DESs. Herein we report the development of a drug-eluting BVS (DE-BVS) through the innovative use of a photopolymerizable, citrate-based biomaterial and a high-precision additive manufacturing process. BVS with a clinically relevant strut thickness of 62 μm can be produced in a high-throughput manner, i.e. one BVS per minute, and controlled release of the anti-restenosis drug everolimus can be achieved by engineering the structure of polymer coatings to fabricate drug-eluting BVS. We achieved the successful deployment of BVSs and DE-BVSs in swine coronary arteries using a custom-built balloon catheter and BVS delivery system and confirmed BVS safety and efficacy regarding maintenance of vessel patency for 28 days, observing an inflammation profile for BVS and DE-BVS that was comparable to the commercial XIENCE™ DES (Abbott Vascular).

Research on slope algorithm based laser welding guidance technology for 3D vision inspection of thin-walled parts

Visual guidance technology advances quickly and so do the technical requirements for weld trajectory, which needs to fulfill performance indices for high precision, anti-interference and quick processing. In this study, the approach of getting 3D point cloud data by directly scanning the workpiece to be welded with a 3D scanner is used because it is more precise and reliable than processing gray-layer data obtained from a depth camera, turning it into a 3D point cloud. The weld boundary is retrieved by slope calculation for the butt weld after preprocessing the three-dimensional point cloud data (cut, down-sampling, and abnormal value removal). The center point is then determined. After that, a welding trajectory is produced by fitting the center point, which is transmitted back to the actuator to guide its welding work and realize the identification and tracking of the weld.

Deep learning-based predicting and compensating method for the pose deviations of parallel robots

The static pose accuracy is regarded as a crucial performance indicator for parallel robots, which is inevitably affected by the geometric and nongeometric errors. However, an accurate error model for parallel robots is highly complex, especially for mathematical modeling and identifying non-geometric errors. This limitation hampers the application of parallel robots in high-precision processing and manufacturing fields. To enhance the performance of parallel robots and address the complexities in establishing their error models, a deep learning-based predicting and compensating method is investigated, which can accurately predict their pose deviations. This method ingeniously integrates the spatial feature extraction capabilities of convolutional neural networks, the bidirectional dependency capturing prowess of bilateral long short-term memory networks, and the feature significance discernment offered by squeeze-and-excitation networks. Subsequently, the pose deviations are definitely predicted by this method and are pre-compensated into nominal pose so as to improve the performance. A pose-deviation pairs dataset is established to accomplish the comparison of the investigated method with the two commonly used artificial neural network and long short-term memory on the 6-UPS parallel robot. Experiment on the 6-UPS parallel robot against traditional kinematic calibration methods demonstrate the investigated method achieves satisfactory pose accuracies, with the ability to reduce pose deviations by 88.60 %. Meanwhile, the effectiveness and general applicability of this method have also been validated by successful implementation on a 6-UCU parallel robot.

Simulation on metal-rubber contact behavior based on a reconstructed 3D topography of rough surfaces

A contact model with refined surface topography is important to the frictional wear study of rubber seal. But the traditional modeling method relies on the measurement of surface characteristics of rubber material, which increases the cost due to high-precision instruments and complex data processing. In the paper, a new modeling method on reconstructing the 3D surface topography is proposed. The coordinate cloud of the surface is generated based on the random distribution of asperity height, which is established according to the initiate parameters of the surface roughness. The metal-rubber assembly model with 3D topography of rough surfaces are built. The simulation on metal-rubber contact behavior is conducted in ANSYS. The result shows a power-exponent relation is between the real contact area and the external pressure, and three contact states of no contact, adhesion and slippage are defined to describe the dynamic distribution of real contact area. The authors proved the reliability of the topography reconstruction method by an indentation experiment on rubber material. The area ratio reflects not only the change rule of contact area under an increasing pressure, but also the difference of contact behavior of the contact pairs under different surface roughness.

An experimental study on mixed reality-based user interface for collaborative operation of high-precision process equipment

An experimental study on mixed reality-based user interface for collaborative operation of high-precision process equipment

Fabrication of Large-Area Silicon Spherical Microlens Arrays by Thermal Reflow and ICP Etching.

In this paper, we proposed an efficient and high-precision process for fabricating large-area microlens arrays using thermal reflow combined with ICP etching. When the temperature rises above the glass transition temperature, the polymer cylinder will reflow into a smooth hemisphere due to the surface tension effect. The dimensional differences generated after reflow can be corrected using etching selectivity in the following ICP etching process, which transfers the microstructure on the photoresist to the substrate. The volume variation before and after reflow, as well as the effect of etching selectivity using process parameters, such as RF power and gas flow, were explored. Due to the surface tension effect and the simultaneous molding of all microlens units, machining a 3.84 × 3.84 mm2 silicon microlens array required only 3 min of reflow and 15 min of ICP etching with an extremely low average surface roughness Sa of 1.2 nm.

Current status and applications of time-to-digital converters

Time-to-Digital Converter (TDC) is widely used to realize time interval measurement. The high-precision time measurement technique has important applications in the fields of laser ranging, particle identification, and radioactive nuclear medicine engineering. Based on the existing literature research and data, this paper studies the application areas of TDC in the present development and analyzes the future prospects of TDC applications. The research results showed that: TDC, based on signal screening, realizes time interval measurement as the ultimate purpose of building the system and, at the same time, completes the function of multi-pulse time interval measurement, which can meet the needs of more diversified measurements in the experiments. In the circuit structure, it can identify the feedback output in the all-digital phase-locked loop (ADPLL) and the reference clock phase and frequency information between the feedback output and the reference clock in an ADPLL. It is also promising for use in other areas of high-precision time measurement and processing of circuit signals.

Deep Learning and Image data-based surface cracks recognition of laser nitrided Titanium alloy

Laser nitriding, a high-precision surface modification process, enhances the hardness, wear resistance and corrosion resistance of the materials. However, laser nitriding process is prone to appearance of cracks when the process is performed at high laser energy levels. Traditional techniques to detect the cracks are time consuming, costly and lack standardization. Thus, this research aims to put forth deep learning-based crack recognition for the laser nitriding of Ti–6Al–4V alloy. The process of laser nitriding has been performed by varying duty cycles, and other process parameters. The laser nitrided sample has then been processed through optical 3D surface measurements (Alicona Infinite Focus G5), creating high resolution images. The images were then pre-processed which included 2D conversion, patchification, image augmentation and subsequent removal of anomalies. After preprocessing, the investigation focused on employing robust binary classification method based on CNN models and its variants, including ResNet-50, VGG-19, VGG-16, GoogLeNet (Inception V3), and DenseNet-121, to recognize surface cracks. The performance of these models has been optimized by fine tuning different hyper parameters and it is found that CNN base model along with models having less trainable parameters like VGG-19, VGG-16 exhibit better performance with accuracy of more than 98% to recognize cracks. Through the achieved results, it is found that VGG-19 is the most preferable model for this crack recognition problem to effectively recognize the surface cracks on laser nitrided Ti–6Al–4V material, owing to its best accuracy and lesser parameters compared to complex models like ResNet-50 and Inception-V3.

Closed-loop high precision human tissue temperature measurements using a joint forward and inverse method

A closed-loop forward-inverse joint multi-layer temperature prediction method is introduced to overcome the limitations of non-contact microwave-based precise temperature measurements within human tissues. This approach merges an incoherent four-layer tissue forward model with a multi-frequency high-precision inversion algorithm. Random errors were incorporated into the forward model to construct a multi-layer human tissue dataset for performance validation and optimization of the inversion algorithm. The lack of precise temperature measurements in internal human tissues was addressed using an incoherent four-layer forward model that integrates the Pennes heat transfer equation with a fluid–solid coupling boundary modeling technique. Parameter differentiation analysis was conducted in the forward modeling step using incoherent electromagnetic transport equations. The proposed high-precision multi-frequency inversion process, added to the objective function, refines the XGBoost algorithm by assigning a penalty factor and adjusting for neighboring tissue temperature distributions. The Optuna framework was then utilized to optimize XGBoost hyper-parameter sets, resulting in the Opt-XGBoost inversion algorithm. This approach achieved a root mean square error of 0.033 °C and an average absolute error of 0.0256 °C in simulations involving forward modeling-generated data.

Removing grid artifacts from a two-dimensional crisscrossed grid in cone-beam computed tomography to enhance image quality

One-dimensional linear X-ray grids are typically used in clinical practice to reduce the number of scattered X-rays reaching the detector surface, thus improving image contrast in radiography. However, such grids can only remove scattered X-rays in one direction. Additionally, the primary challenge in using X-ray grids in digital radiography is the presence of grid artifacts, such as grid strip shadows and moiré, which can reduce image quality. To overcome these difficulties, in this study, we developed a prototype two-dimensional (2D) crisscrossed grid with a strip density of 1.724 lines/mm by adopting a high-precision sawing process to further improve its scatter radiation removal ability and implemented it in a cone-beam computed tomography (CBCT) system to enhance image quality. We propose a new software-based method called the grid artifact reduction (GAR) algorithm to efficiently eliminate grid artifacts of a 2D grid in CBCT. The GAR algorithm involves three main steps: 1) preprocessing for flat-field compensation, 2) postprocessing for GAR, and 3) CBCT reconstruction. To verify the efficacy of the proposed approach, we conducted an experiment on a CT quality control phantom (Pro-CT MK II™) and quantitatively evaluated the CBCT image quality using the contrast-to-noise ratio (CNR) and Hounsfield unit (HU) accuracy error. The CNR value measured in the CBCT image obtained using the 2D grid and proposed GAR algorithm was 3.22, which is approximately 4.4 times of the value obtained without GAR. Furthermore, the corresponding HU accuracy error was 30.47, an improvement of approximately 1.3 times. These results indicate that the proposed approach is highly efficient in eliminating scattered X-rays, thereby improving the quality of CBCT images. Consequently, high-quality CBCT images were obtained using the proposed approach (i.e., using a 2D crisscrossed grid in CBCT and the proposed GAR algorithm).

Design, fabrication, and characterization of a high-sensitivity integrated quartz vibrating beam accelerometer.

This paper describes the design, fabrication, and characterization of a quartz vibrating beam accelerometer consisting of a metal spring-mass and quartz double-ended tuning forks (DETFs). In this approach, the inertial force of the proof mass pulls or compresses the DETFs, affecting their resonance frequency and, thus, enabling the quasi-digital measurement of acceleration. An isolation structure was specifically designed to prevent the external interference stress from transforming into the DETFs and to decrease the DETFs' thermal stress as the ambient temperature changes. A stress-free and high-precision wire-cut electrical discharge machining process was introduced to solve the fabrication problem of flexible hinges, and a femtosecond laser was used to release the proof mass, comprehensively considering the compatibility of the fabrication process and structural design. The oscillation excitation and detection of the DETFs were analyzed, and the DETFs were fabricated using a micro-electromechanical systems process. Sensor dimensions were optimized to improve sensor sensitivity. An accelerometer prototype was fabricated, and its performance was characterized. The tested scale factor was 157.28 Hz/g, and its stability was 16.54 ppm. The bias stability and 1g stability at 1h were 18 and 7.84 µg, respectively. The experimental results validated the feasibility of the sensor design.

High-precision digital light processing (DLP) printing of microstructures for microfluidics applications based on a machine learning approach

ABSTRACT Digital light processing (DLP) is renowned for its precision, but the challenge lies in the identification of optimal print parameters to minimise print errors and enhance overall print accuracy. This study introduces a groundbreaking approach by integrating 'Random Forest' (RF) models with DLP printing to construct a predictive model for printing errors to achieve unparalleled precision. We conducted experiments using common commercial resins to print, resulting in a comprehensive dataset of 690 experimental datasets. Consequently, we validated this approach by fabricating Y-type microfluidic structures with a minimum feature size printing of 2 µm and herringbone mixer structures with feature sizes of 21.2 µm. We successfully controlled the average print error below 2.3 µm. The 'RF' model was also extended from the printing of microfluidics (2.5D) to complex spatial three-dimensional (3D) Triple-Periodic Minimal Surface (TPMS) structures. We have successfully achieved the printing of TPMS-Gyroid and TPMS-Diamond structures with a minimum feature size of 20 µm, while the printing errors were all kept within 1.5 µm. This study underscores the generalizability and immense potential of integrating machine learning techniques with high-precision DLP printing, offering valuable insights for future research in the realm of high-precision and expeditious 3D printing.

Advanced pneumatic microgripper for versatile biomedical micromanipulation

Microgrippers offer better operational flexibility in biological micromanipulation. However, they face challenges in size miniaturization and environmental adaptability (especially in biological liquid environments). To overcome these challenges, a novel pneumatic microgripper is proposed with a thin-walled multi-airbag structure that directly drives the clamping jaws in motion, with a simple and compact overall construction. The microgripper has a maximum clamping range of 925 μm and can provide a clamping force exceeding 25 mN, capable of lifting loads over 300 times its weight. The microgripper is integrative and can be mass-produced with biocompatible resins in a single-step high-precision 3D printing process. The pneumatic microgripper is utilized to achieve the sorting and assisted injection of mouse oocytes (60–80 μm), assisted cutting of porcine oocytes (100–120 μm), and microscopic threading (10–15 μm) operation with two-finger cooperation. These demonstrations confirm it can operate fine manipulation of microscopic biological structures in air and liquid, and has good biocompatibility and cooperative operational ability. The developed pneumatic microgripper shows broad application prospects in biomedical applications such as microsurgery, micro biopsy, and assisted reproduction.

Large bandwidth and dynamic range current sensor based on micro-PCB Rogowski coil

A micro-PCB Rogowski coil with a volume of only 0.032 cm3 was proposed in this paper, and a magnetic flux concentration structure was introduced while ensuring the high-frequency performance of the coil to improve the measurement ability of the Rogowski coil sensor for weak current characterization. We have systematically investigated the magnetic core materials suitable for high-frequency current sensing, and the performance of core at high frequency has also been verified theoretically and experimentally. In addition, a high-precision signal processing circuit, whose main functions are amplification and integration, has also been developed. Experiments have shown that the developed PCB Rogowski coil current sensor can measure the current in the μA level with a bandwidth of 10 MHz, a sensitivity of up to 180 V A−1 and a resolution of up to 50 μA. The response time of the sensor to a step signal is within 30 ns, and it also has good waveform following ability. Compared with commercial TMR sensors, the sensor proposed in this work also has certain advantages in terms of noise.