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Background: Prospective observational studies have demonstrated that the machine learning (ML) -guided noninvasive chromosome screening (NICS) grading system, which we called the noninvasive chromosome screening-artificial intelligence (NICS-AI) grading system, can be used embryo selection. The current prospective interventional clinical study was conducted to investigate whether this NICS-AI grading system can be used as a powerful tool for embryo selection. Methods: Patients who visited our centre between October 2018 and December 2021 were recruited. Grade A and B embryos with a high probability of euploidy were transferred in the NICS group. The patients in the control group selected the embryos according to the traditional morphological grading. Finally, 90 patients in the NICS group and 161 patients in the control group were compared statistically for their clinical outcomes. Results: In the NICS group, the clinical pregnancy rate (70.0% vs. 54.0%, p < 0.001), the ongoing pregnancy rate (58.9% vs. 44.7%, p = 0.001), and the live birth rate (56.7% vs. 42.9%, p = 0.001) were significantly higher than those of the control group. When the female was ≥ 35 years old, the clinical pregnancy rate (67.7% vs. 32.1%, p < 0.001), ongoing pregnancy rate (56.5% vs. 25.0%, p = 0.001), and live birth rate (54.8% vs. 25.0%, p = 0.001) in the NICS group were significantly higher than those of the control group. Regardless of whether the patients had a previous record of early spontaneous abortion or not, the live birth rate of the NICS group was higher than that of the control group (61.0% vs. 46.9%; 57.9% vs. 34.8%; 33.3% vs. 0%) but the differences were not statistically significant. Conclusions: NICS-AI was able to improve embryo utilisation rate, and the live birth rate, especially for those ≥ 35 years old, with transfer of Grade A embryos being preferred, followed by Grade B embryos. NICS-AI can be used as an effective tool for embryo selection in the future. [ABSTRACT FROM AUTHOR]

There are abundant resources available to translators, such as online monolingual or parallel corpora, terminology and translation databases, and various tools. These resources can assist the translation process, but they can also introduce disruptions. Therefore, this article aims to demonstrate how translation courses can benefit from systematic process logs. These logs, progressively developed and discussed orally in front of the class, can be shared and commented on online by both the teacher and other students. They serve as a record of the reflective and systematic progression of translation practices. Initially created individually, they can later be co-constructed and shared on a collaborative platform. To convey implicit, intercultural, and intertextual components, information and multimodal literacy become essential for both media and literary translation. This includes visual research, explanatory sketches, and cinematographic adaptations of literary works. [ABSTRACT FROM AUTHOR]

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to develop a wearable system for providing CNC machine operators with visual and tactile perception of triaxial cutting forces, thereby assisting operators in industrial environments to enhance work efficiency and prevent mechanical failures. To achieve this goal, we successfully integrated a virtual machining tool simulator with the remote-control wearable system (RCWS). Using the 'King Path' milling parameters, we employed the simulation software developed by the AIM-HI team to calculate static and dynamic cutting forces, converting this data into vibrational commands for the RCWS to generate corresponding tactile feedback. Furthermore, we conducted extensive experiments, testing various data conversion methods, including three sampling techniques and two data compression strategies, aiming to provide accurate tactile feedback related to cutting forces under different operating conditions. [ABSTRACT FROM AUTHOR]

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. [ABSTRACT FROM AUTHOR]

With the widespread application of computer numerical control (CNC) machine tools in high-precision manufacturing, their machining accuracy has garnered significant attention. Thermal errors generated during machining processes are one of the primary factors affecting accuracy. Although thermal error compensation technologies have been extensively researched and implemented in practice to improve machine accuracy, existing methods still face limitations in the dynamic thermal behavior analysis and adaptability in practical applications. This paper delves into the thermal error compensation technologies for CNC machine tools, exploring measurement, prediction, and compensation methods. Firstly, it enhances the accuracy and efficiency of measurements by optimizing the layout of temperature measurement points through a detailed analysis of the mechanisms of thermal error generation. Secondly, it introduces a prediction framework based on digital twin technology to accurately simulate and predict the thermal behavior of machine tools. Lastly, it employs an optimized back propagation neural network (BPNN) for intelligent modeling of thermal errors, thereby improving the prediction accuracy and response speed. These studies not only aid in improving the design and operation of machine tools but also provide theoretical and technical support for high-precision machining. [ABSTRACT FROM AUTHOR]

The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator's health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator's health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model's prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability. [ABSTRACT FROM AUTHOR]

This study explains the development of a manufacturing process that is machinable for a ceramic product. The initial process involves manufacturing a ceramic slurry, which is a mixture of high-weight percentage ceramic powders, and a polymeric binder. The ceramic slurry is then pressed to shape a solid body knwon as the green part, which can be structured using conventional machining tools. The structured green part undergoes a debinding process, the resulting body is referred to as the brown part. Finally, sintering is performed to merge the peripheral ceramic powders, and the white part is obtained. The white part exhibited approximately 18 % isotropic shrinkage in dimension and 9.4 % weight reduction compared with the initial green part. The durability of each part is determined via impact-resistant and microindentation tests. The fracture-resistant energy of the white part is determined as 0.673 J, which is an increase of approximately 500 % compared with the values of other parts. Furthermore, the elastic modulus of the white part is determined to be 14.7 GPa, representing an increase of approximately 270 %. In conclusion, we employed an innovative manufacturing process for ceramic products to address the challenges in conventional methods. [ABSTRACT FROM AUTHOR]

Ultraprecision aerostatic spindles are the key functional component of ultraprecision machining tools and precision measuring instruments. The uniformity and consistency of air film thickness are influenced by the shape errors of the ultraprecision aerostatic spindle. However, given that mass-produced rotors have different local error characteristics, existing shape error models fail to express the variation of the air film thickness accurately when the rotor rotates in the flow field. Thus, the influence of the distribution characteristics of rotor shape errors on the aerostatic bearing flow field and rotor performance cannot be accurately evaluated. In this study, a multistrategy fusion and improved sparrow search algorithm (ISSA) was proposed to optimize the backpropagation (BP) neural network, and a regression model of rotor shape error was established on the basis of the ISSA-BP algorithm. Numerous rotor surface profiles were collected by a cylindricity instrument, and the Gaussian-RK filter was applied to remove the noise and burrs from the rotor profiles. The distribution regularity of local error characteristics was statistically analyzed, and the ISSA-BP neural network was used to fit the rotor surface profiles. The shape error regression model of the rotor was established by regression analysis. Error and harmonic analyses were performed on the model and experimental measurement data. Experimental results showed that the relative error of the rotor shape error regression model in harmonic analysis was less than 1 %, and the maximum relative error of the rotor profile was lower than 0.1 %. Thus, the accuracy of the constructed rotor shape error regression model is verified. [ABSTRACT FROM AUTHOR]

Due to its inherent high hardness, strength, and plasticity, tantalum–tungsten (Ta-W) alloy poses a considerable challenge in machining, resulting in pronounced tool wear, diminished tool lifespan, and suboptimal surface quality. This study undertook experiments utilizing uncoated carbide tools, TiAlN-coated carbide tools, and AlTiN-coated carbide tools for machining Ta-2.5W alloy. The investigation delved into the intricacies of surface temperature, tool longevity, and the distinctive wear characteristics under varying coating materials and cutting parameters. Concurrently, a comprehensive exploration of the wear mechanisms affecting the tools was conducted. Among the observed wear modes, flank wear emerged as the predominant issue for turning tools. Across all three tool types, adhesive wear and diffusion wear were identified as the principal wear mechanisms, with the TiAlN-coated tools displaying a reduced level of wear compared to their AlTiN-coated counterparts. The experimental findings conclusively revealed that TiAlN-coated carbide tools exhibited an extended tool lifespan in comparison to uncoated carbide tools and AlTiN-coated carbide tools, signifying superior cutting performance. [ABSTRACT FROM AUTHOR]

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40–60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology. [ABSTRACT FROM AUTHOR]

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) of grassland is critical for estimating the balance of livestock and livestock production, understanding the dynamics of climate change, and providing feedback for achieving sustainable development. The currently available LAI products have some uncertainties and need to be further improved. Previous studies proposed that integrating the physical model and machine learning (ML) has great potential for the rapid and accurate retrieval of grassland LAI. However, there are few comparative studies on LAI forecast models for different grassland cover to assess the potential of the different hybrid models. Therefore, in this study, five hybrid models based on PROSAIL and ML including deep neural network (DNN), random forest (RF), gradient boosting regression tree (GBRT), support vector machine (SVR), and artificial neural network (ANN) and five mixed models averaging are applied to compare the performance with different forecast models for grassland LAI estimation in Tianzhu County. According to the multiple training, validation, and testing, the results demonstrate that five mixed models averaging and DNN model with a complex network structure are reliable and have higher accuracy and better performance than the estimates from the other four hybrid models, except for its computational efficiency. SVR achieves the best performance in computational efficiency, which it has great potentials to deliver near-real-time operational products for grassland LAI management. Our results show that the hybrid model based on machine learning algorithm coupled with physical process model has great application potential in grassland leaf area index inversion. [ABSTRACT FROM AUTHOR]

Rainfall is changing in intensity and abundance for much of the world as a result of global climate change. Rwanda has been negatively affected by a changing climate, exacerbated by human impact on land and water resources. In most parts of the country, the rainfall pattern has changed over the last decades resulting in both enhanced flooding and water shortage/scarcity in much of the country, especially in the Capital City of Kigali and peripheries which is the main economic hub of the country with strong links to the East African region. Changes in precipitation have affected agricultural production, hydropower production, and water supplies, and has been a result of increased flash floods in the city. This study developed a new predictive model of rainfall patterns in the City of Kigali (CoK) in the Republic of Rwanda using evolutionary methodologies that apply machine learning techniques of Fuzzy Inference Systems (FIS) trained via Genetic Algorithms, Neuro Network Systems and a comparative Support Vector Machine tool, and assessment downscaled climate change combinations with predicted rainfall patterns. The models were calibrated and validated using measured rainfall data in the City of Kigali from 1991 through 2023. The model results show the developed Geno Fuzzy Inference System (GENOFIS) model performed better than the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) models. The Coefficient of Efficiency (CE), and Root Mean Square Error (RMSE) were used as diagnostic measures for model performance evaluation. Models generated with GENOFIS are therefore recommended for rainfall and related prediction patterns in the City of Kigali for climate change adaptation and resilience policy and planning. [ABSTRACT FROM AUTHOR]

A reduced kinetic method (RKM) with a first-principles collision operator is introduced in a 1D2V planar geometry and implemented in a computationally inexpensive code to investigate non-local ion heat transport in multi-species plasmas. The RKM successfully reproduces local results for multi-species ion systems and the important features expected to arise due to non-local effects on the heat flux are captured. In addition to this, novel features associated with multi-species, as opposed to single species, cases are found. Effects of non-locality on the heat flux are investigated in mass and charge symmetric and asymmetric ion mixtures with temperature, pressure, and concentration gradients. In particular, the enthalpy flux associated with diffusion is found to be insensitive to sharp pressure and concentration gradients, increasing its significance in comparison to the conductive heat flux driven by temperature gradients in non-local scenarios. The RKM code can be used for investigating other kinetic and non-local effects in a broader plasma physics context. Due to its relatively low computational cost it can also serve as a practical non-local ion heat flux closure in hydrodynamic simulations or as a training tool for machine learning surrogates. [ABSTRACT FROM AUTHOR]

The number of units in the agricultural trailer and tractor park is increasing day by day. The benches, tools and machines used in the manufacturing processes of these units accelerate the production process in terms of quality and quantity in parallel with the developing technologies. However, this intensive production processes can create vulnerabilities in terms of employee health and work safety. In this study, it is aimed to examine and evaluate the agricultural trailer manufacturing process and some environmental conditions in a case workplace. In accordance with this purpose, benches and machines used in agricultural trailer production at Çanakkale were investigated. Noise and lighting levels during the operation of these machines and benches were determined in a sample workplace. In addition, evaluations were made in order to reduce the effects of the obtained noise and lighting on the employees. When the noise measurement results obtained for machines LAeq (the level of sound pressure) values were in the range of 61.74-93.33 dB(A), Lmax (the level of measured sound pressure) values were in the range of 79.53-94.3 dB(A), and LEX (exposure level) values were in the range of 47.38-80.02 dB(A). Considering the results of the study and the noise regulation, suggestions were made to reduce the effects of noise on workers. Suggestions were presented to protect them from these physical, physiological and psychological effects and to increase their working efficiency. When the measurement data results for the machines were analyzed, it was specified that the maximal illumination value which was found in the hydraulic press machine with 951 lux at Measurement 1 before noon and 1082 lux at Measurement 2 in the afternoon. It was specified that the minimal value illumination was the gas welding machine with 107 lux at Measurement 1 before noon and the lathe with 54.4 lux at Measurement 2 in the afternoon. Based on the information determined within the scope of the study, suggestions were also presented for lighting, luminaire selection, lighting calculation and lighting conditions. [ABSTRACT FROM AUTHOR]

The machining accuracy of CNC machine tools is significantly affected by the thermal deformation of the feed system. The ball screw feed system is extensively used as a transmission component in precise CNC machine tools, responsible for converting rotational motion into linear motion or converting torque into repetitive axial force. This study presents a multi-physical coupling analysis model for the ball screw feed system, considering internal thermal generation, intending to reduce the influence of screw-induced thermal deformation on machining accuracy. This model utilizes the Fourier thermal conduction law and the principle of energy conservation. By performing calculations, the thermal source and thermal transfer coefficient of the ball screw feed system are determined. Moreover, the thermal characteristics of the ball screw feed system are effectively analyzed through the utilization of finite element analysis. To validate the proposed analysis model for the ball screw feed system, a dedicated test platform is designed and constructed specifically to investigate the thermal characteristics of the ball screw feed system in CNC machine tools. By selecting specific CNC machine tools as the subjects of investigation, a comprehensive study is conducted on the thermal characteristics of the ball screw feed system. The analysis entails evaluating parameters like temperature field distribution, thermal deformation, thermal stress, and thermal equilibrium state of the ball screw feed system. By comparing the simulation results from the analysis model with the experimental test results, the study yields the following findings: The maximum absolute error between the simulated and experimental temperatures at each measuring point of the feed system components is 2.4°C, with a maximum relative error of 8.7%. The maximum absolute error between the simulated and experimental temperatures at the measuring point on the lead screw is 2.0°C, with a maximum relative error of 6.8%. The thermal characteristics obtained from the steady-state thermal analysis model of the feed system exhibit a prominent level of agreement with the experimental results. The research outcomes presented in this paper provide valuable insights for the development of ball screw feed systems and offer guidance for the thermal design of machine tools. [ABSTRACT FROM AUTHOR]

Build-up-edge (BUE), high-temperature machining and tool wear (TW) are some of the problems associated with difficult-to-machine materials for high-temperature applications, contributing significantly to high-cost manufacturing and poor tool life (TL) management. A detailed review of non-traditional machining processes that ease the machinability of INCONEL®, decrease manufacturing costs and suppress assembly complications is thus of paramount significance. Progress taken within the field of INCONEL® non-conventional processes from 2016 to 2023, the most recent solutions found in the industry, and the prospects from researchers have been analysed and presented. In ensuing research, it was quickly noticeable that some techniques are yet to be intensely exploited. Non-conventional INCONEL® machining processes have characteristics that can effectively increase the mechanical properties of the produced components without tool-workpiece contact, posing significant advantages over traditional manufacturing. [ABSTRACT FROM AUTHOR]

Loaded meshing transmission performance optimization has been an increasingly significant target for the design and manufacturing of aerospace spiral bevel gears with low noise and high strength. An innovative data-driven multi-objective optimization (MOO) method is proposed for the loaded meshing transmission performances of aerospace spiral bevel gears. Data-driven tooth surface modeling is first used to obtain a curvature analysis of loaded contact points. An innovative numerical loaded tooth contact analysis (NLTCA) is applied to develop the data-driven relationships of machine tool settings with respect to loaded meshing transmission performance evaluations. Moreover, the MOO function is solved by using an achievement function approach to accurate machine tool settings output, satisfying the prescribed requirements. Finally, numerical examples are given to verify the proposed methodology. The presented approach can serve as a powerful tool to optimize the loaded meshing transmission performances with higher computational accuracy and efficiency than the conventional methods. [ABSTRACT FROM AUTHOR]

Ceramic-copper substrates, as high-power, load-bearing components, are widely used in new energy vehicles, electric locomotives, high-energy lasers, integrated circuits, and other fields. The service length will depend on the substrate's copper-coated surface quality, which frequently achieved by utilising an abrasive strip polishing procedure on the substrate's copper-coated surface. Precision diamond fly-cutting processing machine tools were made because of the low processing accuracy and inability to match the production line's efficiency. An analysis of the fly-cutting machining principle and the structural makeup of the ceramic-copper substrate is the first step in creating a roughness prediction model based on a tool tip trajectory. This model demonstrates that a shift in the tool tip trajectory due to spindle runout error directly impacts the machined surface's roughness. The device's structural optimisation design is derived from the above analyses and implemented using finite element software. Modal and harmonic response analysis validated the machine's gantry symmetrical structural layout, a parametric variable optimisation design optimised the machine tool's overall dimensions, and simulation validated the fly-cutterring's constituent parts. Enhancing the machine tool's stability and motion accuracy requires using the LK-G5000 laser sensor to measure the guideway's straightness. The result verified the machine tool's design index, with the Z- and Y-axes' straightness being better than 2.42 μ m / 800 mm and 2.32 μ m / 200 mm, respectively. Ultimately, the device's machining accuracy was confirmed. Experiments with flying-cut machining on a 190 × 140 mm ceramic-copper substrate yielded a roughness of Sa9.058 nm. According to the experimental results, the developed machine tool can fulfil the design specifications. [ABSTRACT FROM AUTHOR]

The dual five-axis machining machine is state-of-the-art equipment designed for producing thin-walled aerospace parts like skins and panels. It comprises two five-axis machine tools with mirrored synchronous motions: one bi-rotary machine for milling and the other machine for follow-up support. The high absolute accuracy of the five-axis machine tools themselves and the high synchronous motion accuracy between the two machines are both crucial to ensure machining accuracy. This paper proposes an efficient method to measure and identify geometric and dynamic errors of dual five-axis machine tools. Using the R-test combined with three measurement trajectories to measure the absolute motion error of two five-axis machine tools and the synchronous motion error between the two machines. The designed trajectories include the main rotary axis linkage conditions and can fully reflect the primary motion forms of the dual five-axis machine tool. The main position-independent geometric errors and rotational axis linkage dynamic errors are identified by converting quasi-static and dynamic measurement data from three different trajectories into a unified reference coordinate system. The entire process requires only one instrument installation, effectively avoiding time-consuming repetitive installations and calibration processes while reducing the impact of installation errors on results. The effectiveness of the proposed method for measuring and identifying the geometric and dynamic errors is analyzed combined with Monte Carlo simulation. Experiments were carried out on a dual five-axis mirror milling system (MMS) with a 5000 mm X-stroke. Both simulations and experiments confirmed that the proposed method is efficient in quickly identifying key error sources of the MMS. [Display omitted] • An efficient method to measure and identify geometric and dynamic errors during synchronous motion of the MMS is proposed. • A geometric and dynamic error model is introduced for identifying key errors using error sensitivity theory. • A 3-step measurement strategy that enables rapid separation of linkage errors of the MMS is introduced. • Both simulation and experiment show the proposed method could obtain key error sources of the MMS with high efficiency. [ABSTRACT FROM AUTHOR]

The error compensation method is an economical and effective method to improve the machining accuracy of machine tools. Conventional volumetric error modeling of machine tools is based on the rigid body kinematics model using 21 terms of geometric errors and assumes the geometric error of one axis is independent of other axes. However, if the error motion does not satisfy this assumption in serial machine tools, it may significantly influence the overall machine tool accuracy. This paper reveals the existence of mutual crosstalk of angular errors in coupling axes by experiments with equipping the five-degree-of-freedom (5-DOF) embedded sensor module in each axis. A new on-machine measurement method is proposed for detecting crosstalk errors of the coupling axes covering the whole coupling area. It is confirmed that the position-dependent geometric errors (PDGEs) of each axis should be modified by two-dimensional dependence. Therefore, extended from the normal volumetric error model (NVEM) that is commonly used nowadays, a novel enhanced volumetric error model (EVEM) that contains crosstalk's PDGEs is proposed. The real-time error compensation controller (RECC) embedded in CNC system kernel has been developed in collaboration with the vender by writing in the application function. The effectiveness and feasibility of the EVEM and RECC are verified by non-cutting and cutting experiments. Through comparison tests with laser interferometer on body diagonals, the accuracy improving rate of EVEM is about 10 % higher than NVEM. The real-cutting tests on the ISO10791 sample are verified by a CMM and the results show the accuracy improvement of EVEM is better than NVEM by about 20 %. • A new entire coupling plane measurement method is proposed to reveal the crosstalk error phenomenon in any stacked-up XY stages. These PDGEs are not only in relation to their own axis but also to the coupled axes. • Based on the normal volumetric error model (NVEM), the new enhanced volumetric error model (EVEM) that considers crosstalk errors is proposed. • The real-time error compensation controller (RECC) embedded in CNC system kernel for volumetric error compensation has been developed. It is a direct multi-sensor feedback compensation control system without the use of an external computer as other researches. • The effectiveness of EVEM and RECC are verified through a series of experiments. The advantage of EVEM over NVEM is also proven by comparison tests. [ABSTRACT FROM AUTHOR]

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine. Author summary: The cellular circadian clock consists of an interacting set of genes that through their interactions oscillate throughout the day. This oscillator also responds to external cues so that the genes oscillate in phase with external environmental rhythms. A cell therefore uses its circadian clock to provide its genes with information about the external time. In this way it can coordinate many of the processes taking place in the cell and allocate some of these processes to specific times of the day. It is becoming increasingly clear that the quality of this timing information influences onset progression and outcome in a number of chronic diseases such as cancer. Our aim is therefore to develop a machine-learning tool that can assess how well the clock is working. We want to use this with patients and therefore, for clinical utility, it needs to work with only a single clinical sample and to produce reproducible results that can be clearly interpreted and easily compared. [ABSTRACT FROM AUTHOR]

Spontaneously occurring miniature excitatory postsynaptic currents (mEPSCs) are fundamental electrophysiological events produced by quantal vesicular transmitter release at synapses. Their analysis can provide important information regarding pre- and postsynaptic function. However, the small signal relative to recording noise requires expertise and considerable time for their identification. Furthermore, many mEPSCs smaller than ~8 pA are not well resolved (e.g., those produced at distant synapses or synapses with few receptor channels). Here, we describe an automated approach to detect mEPSCs using a machine learning-based tool. This method, which can be easily generalized to other one-dimensional signals, eliminates inter-observer bias, provides an estimate of its sensitivity and specificity and permits reliable detection of small (e.g., 5 pA) spontaneous unitary synaptic events. [ABSTRACT FROM AUTHOR]

Our objective was to establish and test a machine learning‐based screening process that would be applicable to systematic reviews in pharmaceutical sciences. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) model, a broad search strategy, and a machine learning tool (Research Screener) to identify relevant references related to y‐site compatibility of 95 intravenous drugs used in neonatal intensive care settings. Two independent reviewers conducted pilot studies, including manual screening and evaluation of Research Screener, and used the kappa‐coefficient for inter‐reviewer reliability. After initial deduplication of the search strategy results, 27 597 references were available for screening. Research Screener excluded 1735 references, including 451 duplicate titles and 1269 reports with no abstract/title, which were manually screened. The remainder (25 862) were subject to the machine learning screening process. All eligible articles for the systematic review were extracted from <10% of the references available for screening. Moderate inter‐reviewer reliability was achieved, with kappa‐coefficient ≥0.75. Overall, 324 references were subject to full‐text reading and 118 were deemed relevant for the systematic review. Our study showed that a broad search strategy to optimize the literature captured for systematic reviews can be efficiently screened by the semi‐automated machine learning tool, Research Screener. [ABSTRACT FROM AUTHOR]

This article explores the use of machine learning to analyze blink reflex data collected from patients undergoing surgery for suspected vestibular schwannoma. The study aims to determine the prognostic value of blink reflex data in preserving facial nerve function. The researchers collected EMG data from six patients and analyzed tumor volume, House-Brackmann scores, and changes in blink reflex amplitude over time. While the sample size was small, the study demonstrates the feasibility of using machine learning to interpret facial nerve monitoring data and suggests the potential for larger-scale studies in the future. [Extracted from the article]

In view of the problems that FMECA in the design stage of CNC machine tools does not take into account the complexity of the designed functional structure, does not compare the weight of risk factors, and is not easy to distinguish the harm degree of failure mode, CBWM, and DEA are applied to the study of FMECA in the design stage of CNC machine tools. Based on the complexity of functional structure in machine tool design, score the factors of each potential failure mode, form a decision unit, re‐formulate the scoring table, and subdivide the severity into machine tool hazard (M), personal hazard (P), environmental hazard (E), and buyer satisfaction (B). The factors influencing risk factors were defined as the probability of failure mode (O), machine tool hazard (M), personal hazard (P), environmental hazard (E), and buyer satisfaction (B). On the basis of the scoring of each factor, the functional structure complexity (C) is taken as the input index, and the risk factor is taken as the output index. The CBWM method is applied to determine the weight of each risk factor, and DEA method is used to calculate the efficiency of each decision making unit. Finally, a new method for calculating risk priority number RPN is proposed by integrating the score value of factors, weight value of factors, and efficiency value of fault mode, and the hazard degree of fault mode is ranked according to the risk priority coefficient. Taking a machining center spindle system as an example, the proposed method is applied to analyze the fault mode and its influence, and the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

As the key component of a five-axis CNC planer-type milling machine, the integral mechanical property of the A/C swing angle milling head directly affects the machining accuracy and stability of the milling machine. Taking the mechanical A/C swing-angle milling head of a five-axis numerical-control gantry milling machine as the research object, the stress deformation characteristics and natural frequency of the swing-angle milling head under actual working conditions were studied using finite-element analysis. Based on the analytical results, it was determined that the cardan frame, with its large mass proportion and strong rigidity of the whole milling head, is the object to be optimized. The topological optimization of the cardan frame, in which achieving the minimum flexibility was the optimization objective, was carried out to determine the quality reduction area. By comparing the simulation results of the cardan frames of three different rib plate structures, it was shown that the cardan frame performance of the ten-type rib plate structure was optimal. The analytical results showed that, when the cardan frame met the design requirements for stiffness and strength, the mass after optimization was reduced by 13.67% compared with the mass before optimization, the first-order natural frequency was increased by 7.9%, and the maximum response amplitude was reduced in all directions to avoid resonance, which was beneficial to the improvement of the dynamic characteristics of the whole machine. At the same time, the rationality and effectiveness of the lightweight design method of the cardan frame were verified, which has strong engineering practicality. The research results provide an important theoretical basis for the optimization of other machine tool gimbals and have important practical significance and application value. [ABSTRACT FROM AUTHOR]

We explore the relationship between the dual of a weighted minimum-energy control problem, a special case of linear-quadratic optimal control problems, and the Douglas--Rachford (DR) algorithm. We obtain an expression for the fixed point of the DR operator as applied to solving the optimal control problem, which in turn devises a certificate of optimality that can be employed for numerical verification. The fixed point and the optimality check are illustrated in two example optimal control problems. [ABSTRACT FROM AUTHOR]

Double-sided lapping is currently the mainstream manufacturing method for obtaining plane-parallel optical workpieces with high surface shape accuracy, and the fixed abrasive lapping pad is the main machining tool. However, the effect of pad surface morphology on the workpiece surface shape has rarely been studied, and its formation mechanism has not yet been revealed. In this paper, based on probabilistic statistics and contact mechanics, an analytical model that takes into account the pad surface morphology for predicting the workpiece surface shape evolution is developed. Notably, this model considers the cross-scale relationship between the formation of lapped surface shape and the scratching process of a single abrasive grain in material removal for the first time. Furthermore, the real-time dynamical contact relationship between the workpieces and the measured actual pad surface morphology is also considered. The surface shape and peak-to-valley value of the workpiece under different parameters were simulated and analyzed, and the influence mechanism of the local profile and macro shape of the lapping pad on the lapped workpiece surface shape are clarified. Moreover, the double-sided lapping trial experiments were performed to validate the theoretical model, and the experimental results demonstrated good agreement with the simulation results. The maximum error of the surface shape evolution prediction model is less than 14.6 %. This result verifies the correctness and validity of the established model. This work not only provides new ideas for controlling the workpiece surface but also forms the theoretical basis for understanding the double-sided lapping process. • A new mathematical model is established considering the pad surface morphology. • The cross-scale connection and real-time dynamical change for MRD are considered for the first time. • The influence mechanism of the pad surface morphology on the workpiece surface shape are clarified. • The M-shaped pad produce convex shape, while the G-shaped pad produces concave shape. [ABSTRACT FROM AUTHOR]

Optimal tolerance allocation (OTA) is one of the most effective ways to improve the machining precision of machine tools. However, the existing OTA methods have the problem that tolerance-cost models are particularly vulnerable to subjective factors, which lead to tolerance allocation scheme with poor applicability and reliability. For this reason, a novel OTA method that simultaneously considers volumetric error and processing simplicity index (PSI) is proposed in this paper. Based on the multi-body system theory and homogeneous transform matrix, the volumetric error prediction model based on tolerance of machine tools' key parts is firstly established by taking the geometric error as a bridge between tolerance and volumetric error of machine tools. Then, the weight coefficient of each tolerance parameter is calculated based on sensitivity analysis method. Moreover, PSI, which denotes the degree of processing simplicity to reach the required tolerance value, is innovatively proposed to replace manufacturing cost. And the tolerance-PSI model is established to replace tolerance-cost model. Then, an OTA model is established by minimizing volumetric error and maximizing PSI. The NSGA-II is employed to obtain OTA scheme. Finally, simulations and experiments are conducted to validate the feasibility and effectiveness of the presented method. • The tolerance-volumetric error model is established to predict the volumetric error of machine tools in early design stages. • The tolerance-processing simplicity index model is innovatively proposed to replace the tolerance-cost model. • A novel optimal tolerance allocation method is developed and verified by the simulation and experiment. [ABSTRACT FROM AUTHOR]

This paper presents a method for workshop-suited identification of thermo-elastic errors of three-axis machine tools (MTs) using on-machine measurement. For this purpose, a new design for a three dimensional artifact is presented. Using the artifact, it is possible to identify all relevant geometric errors of three-axis portal milling machines in table design. The method presented is therefore suitable for the one-time identification of geometric errors caused by manufacturing inaccuracies, assembly-related deviations and wear, and for the identification of thermally induced geometry errors over a long-term period. Since the artifact is scalable in size, it can be easily adapted to different machine sizes. The presented method thus represents a universal and inexpensive possibility to measure geometric and especially thermo-elastic errors. Within the scope of this paper, an error model was established for the investigated machine type and an artifact design was derived on the basis of this model. The suitability of the artifact for identifying thermo-elastic errors has also been investigated experimentally. For this purpose, an artifact made of thermo-invariant material (Invar) was produced. This artifact was measured cyclically on a MT with three linear axes, which was exposed to a controlled thermal load in a temperature chamber. The positions of the geometric features were collected using a 3D touch trigger probe. In addition, the ambient temperatures were recorded. The results of this measurement are discussed in this paper. • A novel method for workshop-suited identification of thermo-elastic errors of three-axis machine tools is presented. • A kinematic error model is derived. • A procedure for errors identification using the artifact and a 3D touch trigger probe is developed. • Measurement uncertainty analysis. • Experimental investigation of the suitability of the method for identifying thermo-elastic errors. [ABSTRACT FROM AUTHOR]

Multi-axis ultra-precision milling is a powerful technology which enables the manufacturing of various complicated curved surfaces. However, in the fabrication of an undetermined and random micro-structured surface, the conventional manufacturing mode from a given geometry model to the final surface machining is no longer applicable. Therefore, this work presents an integrative numerical control (NC) code generation method for fabricating randomly distributed V grooves on the great circles of a spherical surface firstly, in which the tool path generation is integrated into the geometry design process. The randomness of the V grooves refers to the geometric feature that the normal vectors of all the planes of the great circles are randomly generated, which are uniformly distributed towards the corresponding points on a unit sphere. Besides, the singular movements in five-axis milling of the randomly distributed V grooves on the great circles are analyzed. It is revealed that there are two distinct mechanisms of the singularity problem. One is the singular configuration of the machine tool, and the other is the discretization of the generated tool path. Subsequently, a general post-processing algorithm is contributed to solve the singularity problem, which enables the generation of NC code with error control. Furthermore, in five-axis milling of the randomly distributed V grooves on the great circles, the position error of tool virtual center can also significantly influence the machining accuracy. Therefore, a two-point calibration method is proposed to accurately determine the position of tool virtual center. Finally, a high-precision random V groove-structured sphere is machined by using the proposed NC code generation method and the tool virtual center calibration method. The maximum depth error of the V grooves is controlled less than 3.3 μm. The proposed methods in this work enable five-axis milling of high-precision random micro-structures on a spherical surface. [Display omitted] • A method is proposed for tool path generation of random V grooves on a sphere. • Two mechanisms of singularity problem in five-axis milling are revealed. • A novel method with error control is developed to solve the singularity problem. • High precision random V grooves are successfully machined on a spherical surface. [ABSTRACT FROM AUTHOR]