High-performance Tools Research Articles

Adherent HFCVD diamond on steels substrates using vanadium carbide intermediate layer

Investigations on CVD diamond deposition on steels for high performance tools development persisted over years. Literature makes out the need for an intermediate layer to improve adhesion and film quality. This work studied Thermo Diffused Vanadium Carbide (TDVC) coating deposited on AISI O1 steel by thermo reactive diffusion (TRD). Thick TDVC layers formed by varying the thermo diffusion time from 1 h to 12 h. Hot Filament Chemical Vapor Deposition reactor (HFCVD) growth conditions variation made possible diverse diamond films growth, from ultrananocrystalline to microcrystalline. X-ray diffraction, scanning electron microscopy with a field emission gun (FEG-SEM) and Raman spectroscopy were the characterization techniques used. The TDVC coating showed up as an excellent diffusional barrier and, also, a compressive stress reliever. The thicker high quality and adherent HFCVD diamond film grown over the TDVC had a thickness of 3 μm. A TDVC cross-section of 35 μm thickness could effectively mitigate diamond residual thermal compressive stress state after cooling.

ISM High-Performance Tools for Drilling of Oil and Gas Wells. Review

The publications addressing the diamond tools for drilling oil and gas vertical and controlled directional wells are reviewed. The performance of diamond drilling tools of various functionalities is assessed by drilling in soft and medium hard formations. Rock destruction tools of different designs, which are fitted with novel thermally stable and wear-resistant diamond-containing composites, are compared with similar tools of earlier models. The main factors responsible for premature tool wear in medium and hard formations at increased speeds are discussed. Some promising areas in the development and application of diamond drilling tools are identified.

PIBLUP: High-Performance Software for Large-Scale Genetic Evaluation of Animals and Plants.

Today, the rapid increase in phenotypic and genotypic information is leading to larger mixed model equations (MMEs) and rendering genetic evaluation more time-consuming. It has been demonstrated that a preconditioned conjugate gradient (PCG) algorithm via an iteration on data (IOD) technique is the most efficient method of solving MME at a low computing cost. Commonly used software applications implementing PCG by IOD merely employ functions from the Intel Math Kernel Library (MKL) to accelerate numerical computations and have not taken full advantage of the multicores or multiprocessors of computer systems to reduce the execution time. Making the most of multicore/multiprocessor systems, we propose PIBLUP, a parallel, shared memory implementation of PCG by IOD to minimize the execution time of genetic evaluation. In addition to functions in MKL, PIBLUP uses Message Passing Interface (MPI) shared memory programming to parallelize code in the entire workflow where possible. Results from the analysis of the two datasets show that the execution time was reduced by more than 80% when solving MME using PIBLUP with 16 processes in parallel, compared to a serial program using a single process. PIBLUP is a high-performance tool for users to efficiently perform genetic evaluation. PIBLUP with its user manual is available at https://github.com/huiminkang/PIBLUP.

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques.

Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.

Early detection of ulcerative colitis-associated colorectal cancer.

Colitis-associated colorectal cancer (CACC) is one of the most serious complications of inflammatory bowel disease (IBD), particularly in ulcerative colitis (UC); it accounts for approximately 15% of all-causes mortality among IBD patients. Because CACC shows a worse prognosis and higher mortality than sporadic colorectal cancer, early detection is critical. Colonoscopy is primarily recommended for surveillance and several advanced endoscopic imaging techniques are emerging. In addition, recent studies have reported on attempts to develop clinically relevant biomarkers for surveillance using various biosamples, which may become high-performance screening tools in the future, so the best approach and technique for cancer surveillance in long-standing UC patients remain under debate. This review gives a comprehensive description and summary about what progress has been made in terms of early CACC detection.

Interfacial microstructure and mechanical properties of synthetic diamond brazed by Ni-Cr-P filler alloy

Active brazing is a promising technique for the fabrication of high-performance diamond abrasive tools, which is especially beneficial to the manufacturing of hard-to-machine materials such as ceramics and high-strength metals. Ni-Cr based filler alloys are commonly used to fabricate diamond abrasive tools under different brazing strategies. However, in the previous studies brazing conditions are inconsistent, and the mechanisms of the interface reaction and the microstructure evolution in the synthetic diamond and Ni-Cr-P filler alloy reaction system are not well understood. In this work, the interface reaction and solidified microstructure in AISI 1045 steel and diamond grit joints brazed using a commercial Ni-Cr-P filler alloy were systematically studied at a typical brazing temperature of 950 °C. A rapid formation of both Cr3C2 and Cr7C3 compounds was observed at the very early stage of liquid-solid reaction. An anomalous delamination of Cr7C3 and formation of primary (Ni,Cr)5P2 dendrites and FeNi3 phase in near-eutectic Ni-P matrix were observed after extended brazing reaction. Moreover, the effect of brazing time on the residual thermal stresses, and hence the integrity of brazed synthetic diamond, were experimentally revealed and discussed with the implications for the fabrication of diamond abrasive tools.

An updated cable feeder tool design for robotized stator cable winding

We have previously suggested a method for robotized stator winding of cable wound electric machines and demonstrated the method successfully in full-scale experiments. The cable feeder tool used to handle the cable during the complete winding process is an essential component of this robot cell. To take the robot winding method to the next level, into an industrial product, require further developments regarding durability, independency, flexibility and implementability. In this paper, we present an updated cable feeder tool design. This tool is designed to be used in a robot cell for cable winding of the third-generation design of the Uppsala University Wave Energy Converter generator stator. In this work, three cable feeder tool prototypes have been constructed, experimentally evaluated and validated for the intended application. Key performance parameters are presented and discussed, including suggestions for further developments. We completed a durable, compact, high performance tool design, with fully integrated control into industrial robot controllers. The experimental results presented in this article are very promising and hence, the updated cable feeder tool design represents another important step towards an industrial solution for robotized stator cable winding.

A MUSTA-FORCE Algorithm for Solving Partial Differential Equations of Relativistic Hydrodynamics

Abstract Understanding event-by-event correlations and fluctuations is crucial for the comprehension of the dynamics of heavy ion collisions. Relativistic hydrodynamics is an elegant tool for modelling these phenomena; however, such simulations are time-consuming, and conventional CPU calculations are not suitable for event-by-event calculations. This work presents a feasibility study of a new hydrodynamic code that employs graphics processing units together with a general MUSTA-FORCE algorithm (Multi-Stage Riemann Algorithm – First-Order Centred Scheme) to deliver a high-performance yet universal tool for event-by-event hydrodynamic simulations. We also investigate the performance of selected slope limiters that reduce the amount of numeric oscillations and diffusion in the presence of strong discontinuities and shock waves. The numerical results are compared to the exact solutions to assess the code’s accuracy.

2D recurrent neural networks: a high-performance tool for robust visual tracking in dynamic scenes

This paper proposes a novel method for robust visual tracking of arbitrary objects, based on the combination of image-based prediction and position refinement by weighted correlation. The effectiveness of the proposed approach is demonstrated on a challenging set of dynamic video sequences, extracted from the final of triple jump at the London 2012 Summer Olympics. A comparison is made against five baseline tracking systems. The novel system shows remarkable superior performances with respect to the other methods, in all considered cases characterized by changing background, and a large variety of articulated motions. The novel architecture, from here onward named 2D Recurrent Neural Network (2D-RNN), is derived from the well-known recurrent neural network model and adopts nearest neighborhood connections between the input and context layers in order to store the temporal information content of the video. Starting from the selection of the object of interest in the first frame, neural computation is applied to predict the position of the target in each video frame. Normalized cross-correlation is then applied to refine the predicted target position. 2D-RNN ensures limited complexity, great adaptability and a very fast learning time. At the same time, it shows on the considered dataset fast execution times and very good accuracy, making this approach an excellent candidate for automated analysis of complex video streams.

VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data

BackgroundPre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files.ResultsProcessing tasks provided by VDJPipe include base composition statistics calculation, read quality statistics calculation, quality filtering, homopolymer filtering, length and nucleotide filtering, paired-read merging, barcode demultiplexing, 5′ and 3′ PCR primer matching, and duplicate reads collapsing. VDJPipe utilizes a pipeline approach whereby multiple processing steps are performed in a sequential workflow, with the output of each step passed as input to the next step automatically. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. Because VDJPipe is designed for computational efficiency, we evaluated this by comparing execution times with those of pRESTO, a widely-used pre-processing tool for immune repertoire sequencing data. We found that VDJPipe requires <10% of the run time required by pRESTO.ConclusionsVDJPipe is a high-performance tool that is optimized for pre-processing large immune repertoire sequencing data sets.

High-performance benchmarking of manufacturing processes with object-based modeling

PurposeThe purpose of this paper is to guide companies in conducting benchmarking studies of their manufacturing processes by viewing across industries, locations and products. In particular, the proposed framework can help corporate decision makers in terms of production footprint and site location studies. The level of benchmarking performance can be measured by evaluating defined benchmarking evaluation profiles.Design/methodology/approachThis paper develops a tool to operationalize value-added manufacturing processes for benchmarking evaluations. In this context, an object-oriented database structure has been developed for the business areas such as product development, manufacturing and assembly. This paper focuses on manufacturing processes. Furthermore, a framework for applying high-performance benchmarking has been developed and applied in a case study.FindingsThis paper shows that object class-oriented modeling approach can be applied to manufacturing processes. The higher the degree of independence in terms of locations, industry sectors and products, the more powerful thus a higher performance of benchmarking is achieved. The performance level of benchmarking has been defined by proving and demonstrating higher and lower performance levels. The high-performance benchmarking tool has been successfully applied to a production footprint case study.Originality/valueThis paper takes up the superiority of process benchmarking that has been the focus of numerous research papers on benchmarking techniques in the past. The potential of process benchmarking has been enhanced and operationalized as a tool. A classification logic for benchmarking evaluation profiles has been developed and integrated in the overall tool set. The model helps decision makers to configure their benchmarking studies tailored to their strategic entrepreneurial questions and to guide them to achieve a higher benchmarking performance level.

Pneumopathies interstitielles diffuses au cours de la polyarthrite rhumatoïde

Despite its severity, rheumatoid arthritis-associated interstitial lung disease is still underdiagnosed. The prevalence of this extra-articular complication has been recently reassessed by the use of high-performance imaging tools such as high-resolution thoracic CT scan. Rheumatoid associated interstitial lung disease is not similar to the other autoimmune diseases related lung involvement. The high frequency of the fibrotic pattern, the poor response to treatment and the poor prognosis are some of its characteristics. Because of a high morbidity and mortality rate, rheumatologists should especially focus on this complication. While an early detection could lead to a better follow-up and care by the pulmonologist, first pulmonary functional signs are often delayed compared to the extension of the lung damage. Finally, current studies on the impact of the rheumatoid arthritis treatments on the interstitial lung disease are still contradictory. Thus, this review will present actual data about the characteristics of rheumatoid arthritis associated interstitial lung disease but will also try to sum up the different studies about rheumatoid arthritis treatments influence on this complication.

Are the Epworth Sleepiness Scale and Stop-Bang model effective at predicting the severity of obstructive sleep apnoea (OSA); in particular OSA requiring treatment?

Obstructive sleep apnoea (OSA) is a condition characterised by repetitive upper airway collapse during sleep. The condition carries a range of health sequelae that can prove fatal in cases with co-existing risk factors for the condition, such as obesity and hypertension. Utilisation of a high-performance screening tool for OSA is thus important. A retrospective audit using the ESS and Stop-Bang scores, alongside Apnoea-Hypopnea Index values, for patients who underwent polysomnography over 1year. Multinomial logistic regression was used to compare the predictive abilities of ESS, SBM, and body mass index (BMI) for the patient outcome groups, "None" (No OSA), "Notreat" (OSA not requiring treatment) and "treat" (OSA requiring treatment). The influences of age, gender and BMI on outcome group were also assessed. 126 bariatric and 66 non-bariatric patients were included. Multinomial logistic regression failed to demonstrate predictive ability of ESS. A higher Stop-Bang score significantly increases the risk being in the "treat" group. In addition, male gender, greater age and a higher BMI each individually increase the risk of OSA requiring treatment. Stop-Bang failed to demonstrate predictive significance when age and gender were controlled for. ESS is not an appropriate screening tool for OSA. Stop-Bang, however, remains a useful screening tool, with the ability to detect patient with OSA in need of treatment. Further study may benefit the development and implementation of a concise and more specific screening tool that considers high evidence-based risk factors for OSA, including male gender, greater age and raised BMI.

A novel impact load model for tool-changer mechanism of spindle system in machine tool

In this paper, a novel impact load model for a tool-changer mechanism of spindle system in machine tool is proposed to determine the impact load of tool-changer during unclamping tool. The tool-changer mechanism is composed of cam (frame), shift fork, headstock, and spindle system with cutter bar. In order to build the model, the working principles of the tool-changer and the spindle system structure are briefly introduced, and the load transmitting path of spindle system is analyzed. By combining the kinetostatic behavior and energy conservation law, the computational model to calculate the impact load of tool-changer mechanism is established to obtain the theoretical result. Moreover, a transmitting model of the impact load is set up to collect the experiment result in the light of bolted connection equivalent model, which is in agreement with the impact load transmitting path. With the computational model, the theoretical result of impact load can be determined based on the motion of shift fork and cutter bar system constrained by both headstock movement and cam contour curve. Using the transmitting model, the experimental result of impact load can be calculated by measuring check ring strain. From the comparison of the theoretical and experimental results, it is found that the experimental measurement agrees well with theoretical analysis. This means that the built model is valid, which can be used to predict the impact load during the unclamping tool process. This work aims to provide some fundamental analysis for both the design of the spindle system and the selection of the spindle bearings of high-performance machine tools.

Establishing tools for early diagnosis of congenital toxoplasmosis: Flow cytometric IgG avidity assay as a confirmatory test for neonatal screening

The aim of this study was to evaluate the performance of conventional serology (Q-Preven™ and ELFAVIDAS™) and flow cytometry-based serologic tools for early serologic diagnosis of congenital toxoplasmosis. The study groups included prospectively confirmed cases of congenital toxoplasmosis (TOXO=88) and age-matching non-infected controls (NI=15).The results demonstrated that all samples tested positive/indeterminate for anti-T. gondii IgM screening at birth using air-dried whole blood samples. Serum samples collected at 30–45days after birth tested positive for ELFAVIDAS™ IgG in both groups. While all NI tested negative for ELFAVIDAS™ IgM and IgA, only 78% and 36% of TOXO tested positive for IgM and IgA, respectively. Flow cytometry-based anti-T. gondii IgM, IgA and IgG reactivity displayed moderate performance with low sensitivity (47.6%, 72.6% and 75.0%, respectively). Regardless the remarkable specificity of IgG1, IgG2 and IgG3 subclasses for early diagnosis, weak or moderate specificity was observed (Se=73.9%, 60.2% and 83.0%, respectively). The analysis of IgG avidity indices (AI) demonstrated the highest performance among the flow cytometry-based methods (Se=96.6%; Sp=93.3%), underscoring the low avidity index (AI<60%) within TOXO (97.0%) in contrast with the high avidity index (AI>60%) in NI (93%). Analysis of anti-T. gondii IgG and IgG3 reactivity for mother:infant paired samples may represent a relevant complementary tests for early diagnosis. In conclusion, a feasible high-standard algorithm (Accuracy=97.1%) was proposed consisting of Q-Preven™ IgM screening at birth, followed by ELFAVIDAS™ IgM and flow cytometric IgG avidity analysis at 30–45days after birth as a high performance tool for early serological diagnosis of congenital toxoplasmosis.

Modelling the Brittle/Ductile Transition in Super-Fine Finishing of Carbides

Materials such as binderless tungsten carbide and silicon carbide have become ubiquitous in the fabrication of high-performance tooling and molding inserts. But while conventional grinding of these hard ceramics has been studied in depth, the theory underlying their super-fine finishing has been less extensively explored. In particular, the boundary in process parameters that delineates the brittle/ductile removal transition remains mostly undocumented. In this paper, we review some super-fine finishing methods for carbide materials, based on both bound and kinetic abrasive processes. The focus is then placed on modelling the interaction between material and abrasives under their respective process conditions, and deriving some useful criteria guiding the brittle/ductile transition.

Backlash estimation for industrial drive-train systems

Backlash in gearing and other transmission components is a common positioning-degrading phenomenon that develops over time in industrial machines. High-performance machine tool controls use backlash compensation algorithms to maintain accurate positioning of the tool to cope with such deadzone phenomena. As such, estimation of the magnitude of deadzones is essential. This paper addresses the generic problem of accurately estimating the width of the deadzone in a single-axis mechanical drive train. The paper suggests a scheme to estimate backlash between motor and load, employing a sliding mode observer and a nonlinear adaptive estimator. The efficacy of the approach is illustrated via simulations.

ToF-SIMS Depth Profiling of PS-b-PMMA Block Copolymers Using Arn+, C60++, and Cs+ Sputtering Ions.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a high performance tool for molecular depth profiling of polymer films, in particular when they are structured in microphases. However, a major issue is the degradation of polymer materials under ion irradiation in reactions such as cross-linking, chain breaking, or reorganization processes of polymers which have been demonstrated for materials such as polystyrene (PS) and poly(methyl methacrylate) (PMMA). This work aims at comparing ToF-SIMS molecular depth profiling of structured polymers (polystyrene (PS)-b-polymethyl methacrylate (PMMA) block copolymers (BCP)) using either ultralow energy cesium or the more recently introduced C60++ (under NO dosing and with sample cooling) and argon cluster ion beams (using Ar1500+ ions at 5 keV). The latter improved the quality of the depth profiles, especially the argon cluster ion beam, as it is characterized by a greater homogeneity for the sputter yields of PS and PMMA. No significant artifacts were observed, and this was confirmed by the comparison of depth profiles obtained from films with variable thickness, annealing time, and morphology (cylindrical blocks vs spherical blocks). Comparison to a theoretical model (hexagonal centered pattern) ensured that the ToF-SIMS depth profiles described the real morphology and may thus be a relevant characterization tool to verify the morphology of the films as a function of the deposition parameters.

Carbon fiber-reinforced polymer composite drilling via aluminum chromium nitride-coated tools: Hole quality and tool wear assessment

Drilling into carbon fiber-reinforced polymer composites presents a stiff challenge in terms of ensuring hole quality aspects such as delamination, roughness, roundness, etc. while maintaining an adequate tool life. The hardened carbon fibers in laid up form generally give rise to chips in powdered form that contribute significantly to tool wear problem. To overcome the aforementioned issues, the use of high performance tools has been reported. This paper reports on the usage of yet another novel high performing but much economical aluminum chromium nitride-coated tools that are typically employed for metallic parts. The effect of aluminum chromium nitride tools at three spindle speeds with constant feed rate was evaluated on hole quality, tool wear, and chip formation pattern of carbon fiber-reinforced polymer composite plates. The results were compared with the performance of conventional high speed steel tools taken as baseline. It was found that the coated tools help significantly improve hole quality and provide appreciable tool life at specific machining parameters.

Analysis of Levodopa in the Presence of Vitamin B6 Using Carbon Paste Electrode Modified with 1‐Butyl‐3 methylimidazolium Hexafluorophosphate and CuO Nanoparticles

AbstractA carbon paste electrode modified with 1‐butyl‐3‐methylimidazolium hexafluorophosphate ionic liquid (BMIPF6) and CuO nanoparticles (CuO/NPs) (CPE/BMIPF6/CuO/NPs) was fabricated and used for square wave voltammetric analysis of levodopa in the presence of vitamin B6. The elemental analysis, SEM and XRD methods were used for characterization of synthesized CuO nanoparticle. CPE/BMIPF6/CuO/NPs exhibited high electrical conductivity toward the electro‐oxidation of levodopa at a pH=7.0 as best experimental condition. Using CPE/BMIPF6/CuO/NPs the levodopa and vitamin B6 peaks are separated and oxidized at potentials of 0.565 V and 0.835 V, respectively; hence levodopa can be detected in the presence of vitamin B6. The electrochemical response shows a linear relationship from concentration of levodopa and vitamin B6 in the ranges of 0.06‐1000 μM and 0.1‐700.0 μM, respectively. Finally, CPE/BMIPF6/CuO/NPs were applied as high performance tool for determination of levodopa and vitamin B6 in real samples.