Transient characteristics analysis of linear flux-switching permanent magnet machines for precision control

A dynamical dq model is proposed for a linear flux-switching permanent magnet（LFSPM） machine which is suitable for high-precision control applications.The operation principle of the prototype machine is analyzed using the finite element method（FEM）,and the parameters,such as the back electromotive force（EMF） and the phase flux linkage,are calculated.The calculated and measured results reveal that the back EMF and the flux linkage are essentially sinusoidal,and the variation of the phase flux linkage profile of the LFSPM machine is similar to that of the linear surface permanent magnet（LSPM） machine.Based on this,a dynamical dq model and a simulation control model are proposed.The simulation results are compared with the test results obtained from a DSP-based control platform,which verifies that the model is correct and effective.Moreover,the model can be used for design optimization and control development.

Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Esophageal squamous cell carcinoma(ESCC)is the most common histological type of esophageal cancer with a poor prognosis.Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients.With the advancement of artificial intelligence(AI)technology and the proliferation of medical digital information,AI has demonstrated promising sensitivity and accuracy in assisting precise detection,treatment decision-making,and prognosis assessment of ESCC.It has become a unique opportunity to enhance comprehen-sive clinical management of ESCC in the era of precision oncology.This review examines how AI is applied to the diagnosis,treatment,and prognosis assessment of ESCC in the era of precision oncology,and analyzes the challenges and potential opportunities that AI faces in clinical translation.Through insights into future prospects,it is hoped that this review will contribute to the real-world application of AI in future clinical settings,ultimately alleviating the disease burden caused by ESCC.

From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment.

Using AI and Precision Nutrition to Support Brain Health during Aging

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical,genetic,environmental,and lifestyle factors to optimize medication management.This study investigates how artificial intelligence(AI)and machine learning(ML)can address key challenges in integrating pharmacogenomics(PGx)into psychiatric care.In this integration,AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions.AI-driven models integrating genomic,clinical,and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder.This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry,highlighting the importance of ethical considerations and the need for personalized treatment.Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care.Future research should focus on developing enhanced AI-driven predictive models,privacy-preserving data exchange,and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Experimental Study on Entropy Features in Machining Vibrations of A Thin-Walled Tubular Workpiece

In machining processes,chatter vibrations are always regarded as one of the major limitations for production quality and efficiency.Accurate and timely monitoring of chatter is helpful to maintain stable machining operations.At present,most chatter monitoring methods are based on the energy level at specified chatter frequencies or frequency bands.However,the spectral features of chatter could change during machining operations due to complexity and time-varying dynamics of the physical machining process.The purpose of this paper is to investigate the time-varying chatter features in turning of thin-walled tubular workpieces from the perspective of entropy.The airborne acoustics was selected as the source of information for machining condition monitoring.First,corresponding to the distinguishing surface topographies relevant to machining conditions,the features of the sound signal emitted during turning of the thin-walled cylindrical workpieces were extracted using the spectral analysis and wavelet packet transform,respectively.It was shown that the dominant vibration frequency as well as the energy distribution could shift with the transition of the machining status.After that,two relative entropy indicators based on the spectrum and the wavelet packet energy were constructed to identify chattering events in turning of the thin-walled tubes.The experimental results demonstrate that the proposed indicators could accurately reflect the transition of machining conditions with high sensitivity and robustness in comparison with the traditional FFT-based methods.The achievement of this study lays the foundations of the online chatter monitoring and control technique for turning of the thin-walled tubular workpieces.

Temperature Variable Optimization for Precision Machine Tool Thermal Error Compensation on Optimal Threshold

Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.

Precision Design for Machine Tool Based on Error Prediction

Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.

Error compensation on precision machine tool servo control system based on digital concave filter

It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that affects the performance of the control system. To compensate for this effect,several concave filters are utilized in the system to improve the control accuracy. The feasibility of compensating for several oscillating modalities with a single concave filter is also studied. By applying a modified Butterworth concave filter to the practical system, the maximum stable state output error remains under ±10 nm in the closed loop positioning system.

Active Vibration Isolation System for Sub microultra precision Turning Machine

Now vibration isolation of ultra precision machine tool is usually achieved through air springs systems. As far as HCM I sub micro turning machine developed by HIT, an active vibration isolation system that consists of air springs and electro magnetic actuators was presented. The primary function of air springs is to support the turning machine and to isolate the high frequency vibration. The electro magnetic actuators controlled by fuzzy neural networks isolate the low frequency vibration. The experiment indicates that active vibration isolation system isolates base vibration effectively in all the frequency range. So the vibration of the machine bed is controlled under 10 -6 g and the surface roughness is improved.

Dynamic Accuracy Design Method of Ultra-precision Machine Tool

Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable and the related research is rarely available. In light of above reasons, a DAD method of ultra-precision machine tool is proposed in this paper, which is based on the frequency domain error allocation.The basic procedure and enabling knowledge of the DAD method is introduced. The application case of DAD method in the ultra-precision flycutting machine tool for KDP crystal machining is described to show the procedure detailedly. In this case, the KDP workpiece surface has the requirements in four different spatial frequency bands, and the emphasis for this study is put on the middle-frequency band with the PSD specifications. The results of the application case basically show the feasibility of the proposed DAD method. The DAD method of ultra-precision machine tool can effectively minimize the technical risk and improve the machining reliability of the designed machine tool. This paper will play an important role in the design and manufacture of new ultra-precision machine tool.

Error compensation for precision machine tools

As one of the ways to improve the machining accuracy of machine tools, error compensation is reviewed from different view points, and the main barriers to further improvement of efficiency of error compensation are analyzed in detail and the vista of the error compensation in precision and ultra precision machines is discussed.

Active vibration control of spindle in ultra-precision turning machine

In order to minimize vibration and improve rotary precision of spindle, we apply active vibration control technique to ultra-precision turning machine based on the analysis of vibration characteristic of aerostatic bearing spindle. Using aerostatic bearing itself as actuator, the vibration of spindle is controlled by adjusting admission pressure respectively and by changing pressure distribution in the bearing. The experiments and simulations prove that this method can minimize the vibration of spindle effectively.

Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Machine intelligence for precision oncology

Despite various advances in cancer research,the incidence and mortality rates of malignant diseases have remained high.Accurate risk assessment,prevention,detection,and treatment of cancer tailored to the individual are major challenges in clinical oncology.Artificial intelligence(AI),a field of applied computer science,has shown promising potential of accelerating evolution of healthcare towards precision oncology.This article focuses on highlights of the application of data-driven machine learning(ML)and deep learning(DL)in translational research for cancer diagnosis,prognosis,treatment,and clinical outcomes.MLbased algorithms in radiological and histological images have been demonstrated to improve detection and diagnosis of cancer.DL-based prediction models in molecular or multi-omics datasets of cancer for biomarkers and targets enable drug discovery and treatment.ML approaches combining radiomics with genomics and other omics data enhance the power of AI in improving diagnosis,prognostication,and treatment of cancer.Ethical and regulatory issues involving patient confidentiality and data security impose certain limitations on practical implementation of ML in clinical oncology.However,the ultimate goal of application of AI in cancer research is to develop and implement multi-modal machine intelligence for improving clinical decision on individualized management of patients.

Fully Integrated Machine Control for Ultra Precision Machining

To meet the demands for highly advanced components with ultra precise contour accuracy and optical surface quality arising in the fields of photonics and optics, automotive, medical applications and biotechnology, consumer electronics and renewable energy, more advanced production machines and processes have to be developed. As the complexity of machine tools rises steadily, the automation of manufacture increases rapidly, processes become more integrated and cycle times have to be reduced significantly, challenges of engineering efficient machine tools with respect to these demands expand every day. Especially the manufacture of freeform geometries with non-continuous and asymmetric surfaces requires advanced diamond machining strategies involving highly dynamic axes movements with a high bandwidth and position accuracy. Ultra precision lathes additionally equipped with Slow Tool and Fast Tool systems can be regarded as state-of-the-art machines achieving the objectives of high quality optical components. The mechanical design of such ultra precision machine tools as well as the mechanical integration of additional highly dynamic axes are very well understood today. In contrast to that, neither advanced control strategies for ultra precision machining nor the control integration of additional Fast Tool systems have been sufficiently developed yet. Considering a complex machine setup as a mechatronic system, it becomes obvious that enhancements to further increase the achievable form accuracy and surface quality and at the same time decrease cycle times and error sensitivity can only be accomplished by innovative, integrated control systems. At the Fraunhofer Institute for Production Technology IPT a novel, fully integrated control approach has been developed to overcome the drawbacks of state-of-the-art machine controls for ultra precision processes. Current control systems are often realized as decentralized solutions consisting of various computational hardware components for setpoint generation, machine control, HMI （human machine interface）, Slow Tool control and Fast Tool control. While implementing such a distributed control strategy, many disadvantages arise in terms of complex communication interfaces, discontinuous safety structures, synchronization of cycle times and the machining accuracy as a whole. The novel control approach has been developed as a fully integrated machine control including standard CNC （computer numerical control） and PLC （programmable logic controller） functionality, advanced setpoint generation methods, an extended HMI as well as an FPGA （field programmable gate array）-based controller for a voice coil driven Slow Tool and a piezo driven Fast Tool axis. As the new control system has been implemented as a fully integrated platform using digital communication via EtherCAT, a continuous safety strategy could be realized, the error sensitivity and EMC susceptibility could be significantly decreased and the overall process accuracy from setpoint generation over path interpolation to axes movements could be enhanced. The novel control at the same time offers additional possibilities of automation, process integration, online data acquisition and evaluation as well as error compensation methods.

PRECISION OF HSK TOOLING SYSTEM IN HIGH SPEED MACHINING

Based on the theory of elastic mechanics and material mechanics, the orientation precision of the hohl schaft kegel（HSK） tooling system in static and dynamic states is theoretically and experimentally studied. The relation between the clamping force and the shank taper is obtained. And a proper clamping force is found to be essential to assure the axial and radial orientation precisions of the HSK tooling system in high speed machining （HSM）. Analytical results show that the reason why the HSK tooling system can keep high precision at the high rotational speed is that the actual axial clamping force keeps the two surfaces of the shank and the spindle in contact all the time.

A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.

Hardening Effect on Machined Surface for Precise Hard Cutting Process with Consideration of Tool Wear

During hard cutting process there is severe thermodynamic coupling effect between cutting tool and workpiece, which causes quenching effect on finished surfaces under certain conditions. However, material phase transformation mechanism of heat treatment in cutting process is different from the one in traditional process, which leads to changes of the formation mechanism of damaged layer on machined workpiece surface. This paper researches on the generation mechanism of damaged layer on machined surface in the process of PCBN tool hard cutting hardened steel Cr12MoV. Rules of temperature change on machined surface and subsurface are got by means of finite element simulation. In phase transformation temperature experiments rapid transformation instrument is employed, and the effect of quenching under cutting conditions on generation of damaged layer is revealed. Based on that, the phase transformation points of temperature under cutting conditions are determined. By experiment, the effects of cutting speed and tool wear on white layer thickness in damaged layer are revealed. The temperature distribution law of third deformation zone is got by establishing the numerical prediction model, and thickness of white layer in damaged layer is predicted, taking the tool wear effect into consideration. The experimental results show that the model prediction is accurate, and the establishment of prediction model provides a reference for wise selection of parameters in precise hard cutting process. For the machining process with high demanding on surface integrity, the generation of damaged layer on machined surface can be controlled precisely by using the prediction model.