High-performance Tools Research Articles

Performance improvement of Si3N4 ceramic cutting tools by tailoring of phase composition and microstructure

Silicon nitride (Si3N4) ceramic cutting tool with excellent cutting performance was successfully achieved through the control of phase composition and microstructure. The hardness of the tool with 49.7 wt% and 4.9 wt% α-Si3N4 is ~20.1 GPa and ~17.5 GPa, respectively. In continuous cutting of cast iron, the tool life significantly increases from ~1200 m to ~2400 m when α-Si3N4 content increases from 4.9 wt% to 49.7 wt%, respectively. The longer tool life is attributed to a better abrasive and adhesive wear resistance of α-Si3N4 phase. In addition, the chemical reaction between Si3N4 ceramic tool and cast iron would lead to the formation of an amorphous oxide layer on wear surface. It was noted that the higher α-Si3N4 content would lead to greater probability of chipping due to lower fracture toughness. Therefore, high-performance Si3N4 ceramic tool could be achieved using an α-Si3N4 matrix ceramic reinforced with elongated β-Si3N4 phase.

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

Current research in biometrics aims to develop high-performance tools, which would make it possible to better extract the traits specific to each individual and to grasp their discriminating characteristics. This research is based on high-level analyses of images, captured from the candidate to identify, for a better understanding and interpretation of these signals. Several biometric identification systems exist. The recognition systems based on the iris have many advantages and they are among the most reliable. In this paper, we propose a new approach based on biometric iris authentication. A new scheme was made in this work that consists of calculating a three-dimensional head pose to capture a good iris image from a video sequence which affects the identification results. From this image, we were able to locate the iris and analyse its texture by intelligent use of Meyer wavelets. Our approach was evaluated and approved through two databases CASIA Iris Distance and MiraclHB. The comparative study showed its effectiveness compared to those in the literature.

Surface-Confined Building of Au@Pt-Centered and Multi-G-Quadruplex/Hemin Wire-Surrounded Electroactive Super-nanostructures for Ultrasensitive Monitoring of Morphine.

Overuse and abuse of morphine (MOP), one of the main components of pericarpium papaveris, have attracted increasing attention in the medical field owing to its pharmacological and toxicological activity. Herein, we proposed a new electrochemical nano-biosensor for MOP detection based on surface-confined building of Au@Pt-centered and multi-G-quadruplex/hemin wire-surrounded electroactive super-nanostructures. The center Au@Pt was flower-shaped and irregularly protruded, allowing substantial loading of multiple G-quadruplex wire/hemin complexes on its surface to accomplish the assembly of electroactive super-nanostructures. Interestingly, as the super-nanostructures were closely confined on the electrode surface, a significantly amplified electrochemical signal was thus obtained in the absence of MOP. In contrast, the introduction of target MOP can induce an intense competitive effect and strongly destroy the assembly process, resulting in the reduction of the electrochemical response that is correlated with the logarithmic concentration of MOP. Under optimal conditions, the electrochemical nano-biosensor is capable of highly sensitive detection of MOP in a dynamic concentration range from 1 ppt to 500 ppb. The limit of detection is achieved as low as 0.69 ppt, and the practical application was confirmed by examining MOP from chafing dish condiments. We expect the electrochemical platform utilizing this unique nanoarchitecture to provide rational guidelines to design high-performance analytical tools.

STUDYING OPPORTUNITIES OF OPTIMIZATION METAL BOND DIAMOND TOOLS PRODUCTION TECHNOLOGY

The technology for manufacturing diamond tools based on the method of two-stage free liquid-phase sintering of diamond-metal compacts developed previously by the authors was investigated. To study the possibilities of optimizing the manufacturing technology of diamond-metal composites by free sintering, a metal bond of the composition (wt.%) 51% Fe - 9% Ni - 32% Cu - 8% Sn was chosen. Studies of the physical-mechanical properties of pressed and sintered in an argon medium samples of sizes 40x5x6 mm were carried out, without prior sintering in a reducing medium and repeated cold pressing. In order to determine the optimal values of temperature and the duration of heat setting required to obtain sufficiently hard and strong samples sintered in a protective medium, we studied the effect of temperature and duration of sintering of samples obtained at various cold pressing pressures on the hardness and strength of the tool. The results of studies of the hardness and shear strength of composite materials 51Fe - 9Ni - 32Cu - 8Sn, obtained by the simplified technological scheme allow us to judge the possibilities of its application for the manufacture of diamond tools. We propose to increase the tool durability by metallizing with thermal diffusion saturation of diamond powders with titanium in a halogen-containing medium. It was found that using the optimized technology for the production of composite material 51% Fe - 9% Ni - 32% Cu - 8% Sn, which includes pressing a powder mixture and free liquid-phase sintering of preforms in a neutral medium, it is possible to obtain samples with sufficiently high physical and mechanical characteristics. Optimized technology can be recommended in the production of high-performance diamond tools.

Wear evaluation of CVD diamond coated high-performance drilling tools for machining of carbon fiber reinforced plastics (CFRP)

Wear evaluation of CVD diamond coated high-performance drilling tools for machining of carbon fiber reinforced plastics (CFRP)

Advances in grinding technology in China with application in steel roller manufacturing

Grinding of large or ultralarge components to achieve stringent quality is always challenging in China’s manufacturing industry, for example, roller for steel strip production. Compared with transverse cylindrical grinding, roller grinding requires high-precision multiaxis motion. In addition, with the increasing demand for process efficiency, the in-process measurement and process compensation become necessary for higher process reliability and productivity. This article presents the state-of-the-art and emerging technology in China’s roller grinding, which includes heavy-duty grinding machine optimization, high-performance tooling design, in-process geometrical accuracy compensation, in-site machine vision for gloss defect, and intelligent process parameter optimization strategy. Therefore, this article will shed light on the development trend of the enabling technology toward intelligent roller grinding with higher accuracy, dexterity, and efficiency.

Adapting smartphone app used in water testing, for soil nutrient analysis

Smartphone technology has now penetrated every aspect of modern life. At such high rates of access and utilization, there is today much potential for the development of smartphones as high-performing tools in a number of industries. Traditionally, smartphones have been used as e.g. point-of-care testing devices in developing countries; now a similar approach can be extended to agriculture. This paper assesses the viability of utilizing smartphones in soil analysis. An Android-based smartphone application, in conjunction with commercially available Quantofix® test strips, was employed to analyze 92 soil samples collected across Indonesia. The soils tested encompassed a wide range of different textures (with 13%, 60% and 25% of samples constituting sandy, loamy and clayey soils, respectively), soil organic matter contents (range: 0.8–19.7%) and nutrient concentrations (range for plant-available N: 0.1–137.4 mg kg−1 and P: 1.2 to 64.2 mg kg−1; on dry soil basis). The app utilizes the smartphone as a portable reflectometer, which relates the color of test strips to the concentration of particular nutrients present in the soil medium. Three mobile devices currently available on the market, representing low, mid- and high-end products, were used to test the application. The results obtained via the smartphone were compared against standard methods for determination of extractable nitrate-N and exchangeable phosphorus (Olsen-P) under laboratory conditions. The smartphone-mediated soil analysis was found to have a high degree of agreement with standard methods for nitrate-N determination (87% of samples with nitrate-N differed by less than 10 mg kg−1 from the standard method for the high-end smartphone) but not for phosphorus determination where chemical interferences to test strip colour development were noted. All three mobile devices were shown to be effective as portable reflectometers. However, color perception was found to differ amongst the devices, resulting in a consistent bias between the high-end phone and the remaining appliances. Whereas, it is essential to consider the inter-smartphone variability in readings and environmental factors such as temperature prior to the smartphone-mediated soil analysis, the smartphone-test strip combination might be employed as acceptable screening tool for soil nutrient concentration assessment to enhance crop outcomes, increasing yield, and preventing over-application of inputs, reducing consequent financial and environmental impact. Further enhancements can test the applicability of smartphone-mediated soil analysis in field conditions.

Integrating Supply and Demand Perspectives for a Large-Scale Simulation of Shared Autonomous Vehicles

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.

Diagnosis of NTM active infection in lymphadenopathy patients with anti-interferon-gamma auto-antibody using inhibitory ELISA vs. indirect ELISA

The anti-interferon-gamma (IFN-gamma) autoantibody is a known cause of opportunistic non-tuberculous mycobacterial (NTM) infection in adults. Diagnosis of those patients is difficult due to the low sensitivity of bacterial culture, and because detection of the neutralizing autoantibody needs special laboratory devices. We conducted a retrospective review of indirect and inhibitory ELISA, both used for detection of anti-IFN-gamma auto-antibody in 102 patients with lymphadenopathies. We assessed hospital records of NTM isolation and/or diagnosis of NTM infection. The review revealed the compatible sensitivity and superior specificity and predictive values for inhibitory ELISA over against indirect ELISA—the latter achieving 100% specificity and positive predictive value for diagnosis of NTM infection in patients with lymphadenopathies. The results confirm functional assays that show plasma samples from NTM-infected patients with positive results by either indirect and/or inhibitory ELISA are IFN-gamma neutralizing autoantibodies. The inhibitory titer of anti-IFN-gamma auto-antibody can be used to distinguish patients with active from inactive NTM infection. Inhibitory ELISA is thus a practical, rapid, high performance tool for routine detection of anti-IFN-gamma autoantibody and NTM infection diagnosis before confirmation, enabling a timely therapeutic strategy for active infection treatment.

Enhanced carbide tool life by the electromagnetic coupling field for sustainable manufacturing

The effective extension of tool life while maintaining machining quality is an important research topic in advanced machining and sustainable manufacturing. Cemented carbide is widely used as the tool material in different manufacturing processes, and it has various forms and work ranges. However, the internal flaw in the tool material can induce a micro-crack which could result in the decrease of tool strength and toughness, and affect the tool life. Improving the tool cutting performance, slowing down the tool wear, and enhancing production efficiency are the eternal themes of cutting tool research. This research focused on a P10 cemented carbide tool. The influences of the electromagnetic coupling field (TEMCP) on the carbide tool life and the maximum of tool force are investigated. The correlation analysis between the TEMCP parameters and the tool life index is conducted using SPSS. The experiment proves that the TEMCP can significantly prolong the cemented carbide tool life, and that the magnetic intensity is a dominant factor. The TEMCP enriches the field technology theory and provides technical support for the sustainable manufacturing and research and development of a high-performance tool with important scientific meaning and research potential.

The effects of sharpened tools on tool flank wear–surface roughness and optimization of cutting parameters in milling Vanadis 4E powder metallurgic tool steel

Vanadis 4E is a powder metallurgic tool steel that provides an excellent combination of wear resistance and ductility for high-performance tools, and it is more machinable than AISI D2 tool steel. This study applied grooving on Vanadis 4E powder metallurgic tool steel with the dimensions of 80 × 105 × 35 mm, and during the milling process, investigated the effects of cutting parameters on surface roughness and tool flank wear. Using the Taguchi L9 (34) array, 9 experiments were carried out. In the experiments, uniform carbide end mills with diameters of 12 mm were used as new and sharpened (first usage, first sharpening, second sharpening), and the experiments were performed with three cutting depths (1, 1.5 and 2 mm), three cutting speeds (60, 80 and 100 m/min) and three feed rates (0.02, 0.04 and 0.06 mm/tooth). During the experiments, temperature measurements were made with a thermal camera from the cutting area, and surface roughness and tool wear measurements were made after every one experiment. The cutting parameters were optimized by using the obtained roughness and flank wear values, and the results were analyzed by signal-to-noise ratios, analysis of variance (ANOVA), three-dimensional graphs and thermal images. After optimization, three confirmation experiments were conducted by using the optimum parameters, and the values that were estimated with the Taguchi method and the results of the verification experiments were compared.

Immuno-phenotyping of tumor-specific CD8 T cells using high-dimensional mass cytometry

Abstract There is strong evidence that tumor-specific T cells play a central role in tumor rejection. immunoSCAPE leverages the high-dimensional immune profiling capabilities of CyTOF for the identification of antigen-specific T cells to support the development of immunotherapy strategies. By applying CyTOF in conjunction with combinatorial peptide-MHC tetramer staining and high-performance dimensional analysis tools, we broadly and sensitively map T cell reactivity against MHC-class I epitopes while concurrently performing in-depth characterization of these rare antigen-specific T cells. Here, we have applied our unique capabilities to detect and characterize circulating neoantigen-specific T cells in lung carcinoma patient undergoing atezolizumab treatment. Out of 782 neoantigen candidates, T cell reactivity against 13 unique neoantigens was detected within patients who responded to atezolizumab treatment, while T cells within non-responder patients recognized 7 neoantigens. Additionally, neoantigen-specific CD8 T cells from treatment responders exhibit a differentiated effector phenotype similar to CMV or EBV-specific T cells, whereas neoantigen-specific CD8 T cells from non-responders display a memory-like phenotype, suggesting that the properties of tumor-reactive T cells may be associated with clinical response to anti-PD-L1 treatment. By providing insight into the nature and function of tumor-specific T cells, immunoSCAPE’s unique high-dimensional immune profiling platform is a valuable tool to guide the development of novel immunotherapy strategies through assessment of drug biological activity, definition of mechanism of action, and identification of biomarkers of patient response.

Out-of-the-black-box: Standardisation of the EliSpot Method for immunomonitoring in personalized Immunosupression after solid organ tranplantation

Abstract Individual Immunotherapy for transplantation patients has been moved more and more into the focus of transplant centers, as individualized therapy approaches enable to minimize long-term side effects and increase the quality of life for the patients. The Enzyme-Linked ImmunoSpot assay (EliSpot) has become a powerful tool in solid organ and stem cell transplantation. In an European multicenter study, the Bio-DrIM project c, the use of the EliSpot has been extensively evaluated with regard to monitoring of viral (Epstein-Barr Virus, EBV; Human Cytomegalovirus, HCMV and BK Virus, BKV) reactivation during immunosuppressive therapy after kidney transplantation. The reference institute established SOPs and trained the attending sites. Additionally, EliSpot Reader Systems and EliSpot kits were extensively technical validated to guarantee that all centers reach the same results. A total of 21 plates with 1983 wells were counted. Linear regression of all 1983 pairs of values lead to a linear regression line with the equation y=1.026*x+0,06888 and a Pearson correlation coefficient R2=9988, demonstrating the high concordance of each reader system. To further demonstrate the extraordinary high standardization of the procedure, Inter- and Intra-Assay evaluation were performed. In the set of analyzed results the CV % varied under 20 %, which is far below cellular assays specified by the FDA with 25 %. In conclusion, the combination of the EliSpot method with the EliSpot Reader System is a high-performance tool for diagnostics and monitoring of immunosuppression. Based on the high standardization potential, this state-of the-art technology will play an important role in diagnostic validation of next generation personalized therapy.

BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery. Concept embeddings—which involve the learning of vector representations of concepts using machine learning models—have been employed to capture the semantics of concepts. To develop concept embeddings, named-entity recognition (NER) tools are first used to identify and normalize concepts from the literature, and then different machine learning models are used to train the embeddings. Despite multiple attempts, existing biomedical concept embeddings generally suffer from suboptimal NER tools, small-scale evaluation, and limited availability. In response, we employed high-performance machine learning-based NER tools for concept recognition and trained our concept embeddings, BioConceptVec, via four different machine learning models on ~30 million PubMed abstracts. BioConceptVec covers over 400,000 biomedical concepts mentioned in the literature and is of the largest among the publicly available biomedical concept embeddings to date. To evaluate the validity and utility of BioConceptVec, we respectively performed two intrinsic evaluations (identifying related concepts based on drug-gene and gene-gene interactions) and two extrinsic evaluations (protein-protein interaction prediction and drug-drug interaction extraction), collectively using over 25 million instances from nine independent datasets (17 million instances from six intrinsic evaluation tasks and 8 million instances from three extrinsic evaluation tasks), which is, by far, the most comprehensive to our best knowledge. The intrinsic evaluation results demonstrate that BioConceptVec consistently has, by a large margin, better performance than existing concept embeddings in identifying similar and related concepts. More importantly, the extrinsic evaluation results demonstrate that using BioConceptVec with advanced deep learning models can significantly improve performance in downstream bioinformatics studies and biomedical text-mining applications. Our BioConceptVec embeddings and benchmarking datasets are publicly available at https://github.com/ncbi-nlp/BioConceptVec.

NoisePy: A New High-Performance Python Tool for Ambient-Noise Seismology

AbstractThe fast-growing interests in high spatial resolution of seismic imaging and high temporal resolution of seismic monitoring pose great challenges for fast, efficient, and stable data processing in ambient-noise seismology. This coincides with the explosion of available seismic data in the last few years. However, the current computational landscape of ambient seismic field seismology remains highly heterogeneous, with individual researchers building their own homegrown codes. Here, we present NoisePy—a new high-performance python tool designed specifically for large-scale ambient-noise seismology. NoisePy provides most of the processing techniques for the ambient field data and the correlations found in the literature, along with parallel download routines, dispersion analysis, and monitoring functions. NoisePy takes advantage of adaptable seismic data format, a parallel input and output enabled HDF5 data format designed for seismology, for a structured organization of the cross-correlation data. The parallel computing of NoisePy is performed using Message Passing Interface and shows a strong scaling with the number of cores, which is well suited for embarrassingly parallel problems. NoisePy also uses a small memory overhead and stable memory usage. Benchmark comparisons with the latest version of MSNoise demonstrate about four-time improvement in compute time of the cross correlations, which is the slowest step of ambient-noise seismology. NoisePy is suitable for ambient-noise seismology of various data sizes, and it has been tested successfully at handling data of size ranging from a few GBs to several tens of TBs.

Rate of Information Flow Across Layered Neuro-Spike Network in the Spinal Cord.

Spinal Cord Injury (SCI) is a severe condition that can result in loss of motor and sensory functions by disrupting communication among neurons, i.e., neuro-spike communication. Future information and communication technology (ICT) based treatment techniques for SCI are expected to rely on nano networks, deployed inside the body. In this respect, modeling neuro-spike communication channels in the spinal cord and revealing the relationship between channel metrics and SCI are required to realize these treatment techniques and diagnosis tools such as replacement neural implants, high-performance diagnosis tools, which are based on ICT metrics instead of large medical data. Therefore, in this study, we focus on a spinal cord network, namely the descending spinal cord pathway, which is responsible for the transmission of brain motor signals to the spinal cord. We aim to analyze the rate of motor information flow to the corresponding muscle. To this end, we model the spinal cord motor network as a layered network consisting of a cascade of two independent neuro-spike channels, which are brain-spinal cord network and spinal cord interneuron-spinal cord motoneuron network. We derive upper and lower bounds for the total rate across the brain-spinal cord network and interneuron-spinal cord network. Our evaluations demonstrate that the total rate in the case of upper motor neuron syndrome (UMNS), which manifests itself with muscle weakness, approaches zero, where the brain-spinal cord network becomes a bottleneck. In lower motor neuron syndrome (LMNS), which results in muscle atrophy, the total rate again approaches zero with the loss of spinal cord motoneurons (MN).

Abstract A8: Immuno-phenotyping of tumor-specific CD8 T cells using high-dimensional mass cytometry

Abstract There is strong evidence that tumor-specific T cells play a central role in tumor rejection. However, few tools can simultaneously detect rare antigen-specific T cells and deeply characterize their function and phenotype. immunoSCAPE leverages the high-dimensional immune profiling capabilities of mass cytometry combined with a unique technology for the identification of antigen-specific T cells to support the development of immunotherapy strategies. By applying cytometry by time of flight in conjunction with combinatorial peptide-MHC tetramer staining and high-performance dimensional analysis tools, we broadly and sensitively map T-cell reactivity against MHC-class I epitopes while concurrently performing in-depth characterization of these rare antigen-specific T cells. Here, we have applied our unique capabilities to detect and characterize circulating neoantigen-specific T cells in lung carcinoma patients undergoing atezolizumab treatment. Out of 782 neoantigen candidates, T-cell reactivity against 13 unique neoantigens was detected within patients who responded to atezolizumab treatment, while T cells within nonresponder patients recognized 7 neoantigens. Additionally, neoantigen-specific CD8 T cells from treatment responders exhibit a differentiated effector phenotype similar to CMV or EBV-specific T cells, whereas neoantigen-specific CD8 T cells from nonresponders display a memory-like phenotype, suggesting that the properties of tumor-reactive T cells may be associated with clinical response to anti-PD-L1 treatment. By providing insight into the nature and function of tumor-specific T cells, immunoSCAPE’s unique high-dimensional immune profiling platform is a valuable tool to guide the development of novel immunotherapy strategies through assessment of drug biologic activity, definition of mechanism of action, and identification of biomarkers of patient response. Citation Format: David Roumanes, Michael Fehlings, Mahesh Yadav, Alessandra Nardin. Immuno-phenotyping of tumor-specific CD8 T cells using high-dimensional mass cytometry [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A8.

Multiple performance characteristics optimization in end milling of thin-walled parts using desirability function

With the development of high-performance CNC machine tools, milling has been established as one of the main means of machining thin-walled parts. Thus, the selection of process parameters for milling operations is an important issue in end milling of thin-walled parts to assure product quality and increase productivity. The current study explores three machining parameters, namely wall thickness, feed, and machining strategies, that influence dimensional and form errors, surface roughness, and machining time milling of 7075-T6 aluminum alloy thin-walled parts. The effects of machining parameters on each of the response variables were analyzed using graphs of the main effects and three-dimensional surface plots. Analysis of the results show that the most influential factor for wall thickness deviation, dimensions deviation, perpendicularity deviation, flatness deviation, surface roughness of inner walls, surface roughness of outer walls, and surface roughness of reference plane was machining strategy, while feed is the most influential parameter affecting the machined time, followed by the machining strategy. The desirability concept has been used for simultaneous optimization in terms of machining parameters of the thin-walled parts machining process. Finally, a confirmation test with the optimal parameter settings was carried out to validate the results.

Factors determining the relevance of creation research infrastructure for the synthesis of new materials in the framework of the implementation of the priorities of scientific and technological development of Russia

In the modern world, knowledge and high technologies determine the effectiveness of the economy, can radically improve the quality of life of people, modernize infrastructure and public administration, and ensure law and order and security. The creation of a research infrastructure based on a high-performance hybrid cluster enabled detailed calculations of complex phenomena and processes without full-scale experiments. It has become possible to most efficiently apply modern methods of multiscale computer modeling when developing prototypes of new materials with desired properties for their further synthesis. Such approaches can significantly reduce the cost and speed up the development of modern technologies for producing new semiconductor materials for nanoelectronics, composite materials for the aerospace industry and others. Thus, the use of multiscale modeling methods in combination with the use of high-performance software tools made it possible to create a computer model of a nanoscale heterostructure, develop tools for predictive computer modeling of the physical structure of nanoelectronic devices, the neuromorphic architecture of multilevel memory devices, defect formation in composite materials, and others.

Dynamic Liquid Membrane Electrochemical Modification of Carbon Nanotube Fiber for Electrochemical Microfabrication.

Carbon nanotube fibers (CNFs) are a promising material for use as lightweight, high-strength, electrically conducting tool cathodes in wire electrochemical micromachining (WECMM) in which a high-performance tool cathode is crucial for optimal processing performance. However, the outstanding advantages of pristine CNFs, such as fiber strength, electrical conductivity, and hydrophilic surface, have so far remained underutilized as tool cathodes in WECMM. Herein, electrochemical modification using a dynamic liquid membrane is proposed as an effective online method for functionalizing CNFs prior to WECMM. The proposed method not only improves the assembly accuracy and efficiency but also avoids unnecessary damage to the modified CNF during installation. The introduced functional groups (-OH and -COOH) effectively improved the electrical conductivity and hydrophilicity of CNFs. The influences of H2O2 concentration, applied voltage, and anodization time on the surface modification process were examined experimentally. The use of a pulsed voltage was further proposed to prevent the loss of fiber strength due to over-anodization. Finally, the use of modified CNF electrodes with good surface morphology, strength, and conductivity in WECMM was demonstrated to afford superior machining stability, efficiency, and accuracy as well as improved surface quality compared with the conventional tool cathodes.