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In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The static and dynamic characteristics of the spindle system significantly influence machining accuracy and efficiency. High static and dynamic stiffnesses and thermal stability are required for high-precision and high-efficiency machining. The dynamics of spindle systems are affected by centrifugal forces and thermal expansion in response to spindle rotation. A noncontact electromagnetic loading (EML) system has great potential for measuring spindle characteristics under rotational conditions. This study presents a state-of-the-art design and performance of the EML system, which is an improvement on the system used in previous studies. Detailed experimental design and signal processing are shown for static and dynamic performance measurements, including advanced hardware-in-the-loop simulation of the intermittent milling process using the developed EML system. Some of the representative results of each test are presented, and future research questions are discussed. [ABSTRACT FROM AUTHOR]

Wire Electrical Discharge Machining (WEDM) to achieve high-quality finishes by minimizing the experimental errors in the application of Metal Matrix Composites, which are often necessary with various manufacturing industries. The Al 6061 composites have been made by the fabrication the alloy through stir casting process. In this work, metallurgical and the effect of machining characteristics of Al alloy, Al 6061, through reinforcing with B4C and Fe2O3 investigated. This work an effort has been made on applying Taguchi and ANOVA methods in order to optimize the outcomes L16 orthogonal array was utilized in this investigation. The third dimension perspective to this composite has been acquired for the input settings, comprising higher composites (wt.%) and current, lower pulse on time (Ton) and pulse off time (Toff), and the output variables, which include material removal rate (MRR), electrode wear rate (EWR), and depth, with the aid of the Signal to Noise (S/N) and the small is preferable. The analysis of these trial results via S/N ratios makes it clear that the minimized pulse on and off and more current found in machining, and lower pulse on and off, optimal results parameters were also reached. [ABSTRACT FROM AUTHOR]

This paper is devoted to the shape optimization of the internal structure of an electric motor, and more precisely of the arrangement of air and ferromagnetic material inside the rotor part with the aim to increase the torque of the machine. The governing physical problem is the time-dependent, nonlinear magneto-quasi-static version of Maxwell's equations. This multiphase problem can be reformulated on a 2D section of the real cylindrical 3D configuration; however, due to the rotation of the machine, the geometry of the various material phases at play (the ferromagnetic material, the permanent magnets, air, etc.) undergoes a prescribed motion over the considered time period. This original setting raises a number of issues. From the theoretical viewpoint, we prove the well-posedness of this unusual nonlinear evolution problem featuring a moving geometry. We then calculate the shape derivative of a performance criterion depending on the shape of the ferromagnetic phase via the corresponding magneto-quasi-static potential. Our numerical frameworkto address this problem is based on a shape gradient algorithm. The nonlinear time periodic evolution problems for the magneto-quasi-static potential is solved in the time domain, with a Newton–Raphson method. The discretization features a space-time finite element method, applied on a precise, meshed representation of the space-time region of interest, which encloses a body-fitted representation of the various material phases of the motor at all the considered stages of the time period. After appraising the efficiency of our numerical framework on an academic problem, we present a quite realistic example of optimal design of the ferromagnetic phase of the rotor of an electric machine. [ABSTRACT FROM AUTHOR]

Thermal errors of a feed-drive system could influence machining inaccuracies significantly. Furthermore, the prediction model's accuracy may decrease over time due to changes in thermal characteristics after long-term operation of the machine tool. This paper employs transfer learning methods to refine a backpropagation neural network (BPNN) prediction model, enabling it to effectively adapt to data collected 1 year later. First, linear encoder and rotary encoder is proposed to measure ball-screw thermal expansion, which accelerates measurements and enhances model accuracy by minimizing the influence of heat transfer. Validation using a laser interferometer confirmed a maximum error of 1 μm. Secondly, complex experimental conditions were designed to enhance the robustness and generalizability of thermal error prediction model. Unlike previous studies focused solely on varying feedrate, this research also considers reciprocating motion area variability, revealing its significant impact on thermal error distribution. Then a BPNN model was established, achieving a prediction accuracy of 89%. However, conducting experiments a year later showed a decline in the original model's prediction accuracy, dropping from an initial 89.11 to 67.4% due to evolving thermal characteristics. To overcome the concept-shifting effect, three transfer learning techniques were applied. This paper presents a comparative analysis of three transfer learning methods such as regular transfer learning (RTL), importance weighting network (IWN), and domain-adversarial training of neural networks (DANN). The results demonstrated that while DANN improved average prediction accuracy to 86.64% with a maximum error of 7.06 μm, RTL achieved competitive results with an average prediction accuracy of 85.91% and a maximum error of 5.36 μm. This residual error suggests the RTL's potential advantage over the other two methods because the RTL method only fine-tuned the parameters from the previous model using limited data. The profiles of the thermal error only change slightly under thermal characteristics drifting. The RTL method could also prevent the imbalance in data quantity between the source and target domains. [ABSTRACT FROM AUTHOR]

The transport sector is responsible for generating approximately 30% of the total emissions, with 20% of all carbon dioxide emissions coming from road transport. Transitioning to electric transport can help reduce emissions from road traffic in urban areas. Hydrogen is one of the few energy carriers that practically does not emit carbon dioxide, and it can satisfy a significant portion of the world's transportation energy needs. Synchronous machines with permanent magnet excitation and fractional slot concentrated windings can be highlighted as promising traction motor for electric transport. These machines compared to conventional electric machines have the following advantages: increased energy efficiency, less active material consumption, and improved mass-dimension indicators. However, some machine sizes have highter noise and vibration levels due to radial forces acting between the stator and rotor. The paper presents a methodology for calculating the radial forces using the harmonic components method. The design of the magnetic system with permanent magnets placed on the surface of the cylindrical rotor is considered. The radial force generation conditions with a smooth non-magnetic gap are described. The obtained results show that in electric machines with fractional slot concentrated windings with six phase zones radial forces causing bending deformations of the stator ring and distorting the stator to an elliptical shape are predominated. In machines with fractional slot concentrated windings with three phase zones, radial forces causing one-sided attraction of the rotor to the stator are predominated. The formation of these radial forces is due to the presence of two main harmonics with high amplitude in the magnetomotive force harmonic content created by the winding. Therefore, the use of a design solution that allows reducing one of the main harmonics of the winding magnetomotive force will lead to reduction of magnetic vibrations. The obtained results can be used in the design of the considered class traction motors for hydrogen transport. [ABSTRACT FROM AUTHOR]

Modern database management systems (DBMS), primarily designed as general-purpose systems, face the challenging task of efficiently handling data from diverse sources for both analytical services and online transactional processing (OLTP). The volume of data has grown significantly, with distributions ranging from linear to highly skewed, sometimes involving very slow changes or rapid, intensive updates. Recent research in this field has been significantly influenced by advances in machine learning (ML), particularly deep learning (DL), and these developments have led to the application of various ML algorithms to enhance the efficiency of different parts of the query execution engine. While previous research studies were mostly focused on identifying drawbacks to individual components, such as the query optimizer, there is a notable lack of studies examining the applicability and effectiveness of various machine learning approaches across multiple aspects of the query execution engine. This article aims to provide a systematic review of approaches that apply deep learning models at various levels within the query execution engine. We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. Furthermore, we discuss the key challenges associated with implementing deep learning algorithms in DBMS. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model's accuracy, showing minimal misclassification. [ABSTRACT FROM AUTHOR]

Nowadays, electric propulsion system implementation in vehicles is popular, and many studies and prototypes have been accomplished in this field. Aircraft are important members of the vehicle family, and Unmanned Aerial Vehicles (UAVs) are part of this family as well. Some UAVs still have conventional propulsion systems, which are less efficient and are harmful to the environment. In addition, conventional systems are vulnerable to faults in their propulsion system components. To overcome these problems, we designed a multi-phase Brushless Direct Current (BLDC) motor, to achieve fault-tolerant operation. Our designed BLDC motor was implemented in a UAV model that was created on MATLAB Simulink, based on a currently used UAV. Our design and performance analysis are shown for the BLDC motor, both standalone and as implemented in the created UAV model. The electric propulsion system performance is shown, according to the determined flight profile. We observed that the designed electric machine is capable of producing the required torque to create thrust for lifting the UAV. There are some advantages and disadvantages to using the designed electric machine in this class of UAV. This is shown in the related sections. [ABSTRACT FROM AUTHOR]

It is a known cost‐effective method to employ a DSP‐based electric machine emulator (EME) for motor control unit testing, this paper introduces a novel approach to enhance the emulator's accuracy. The inaccuracies caused by terminal voltage sampling errors in the motor control unit and voltage distortions in the inverter are addressed in this paper. An advanced equivalent voltage acquisition technique, which samples the duty cycle‐amplitude of the inverter's terminal voltage, is developed. Leveraging the acquired equivalent voltage data, a novel voltage compensation strategy that providing more accuracy in EME performance is proposed. The mathematical foundation of the EME is established using Kirchhoff's voltage and current laws. These findings are independently validated through simulations and experiments. The results provide robust evidence that the proposed equivalent voltage acquisition and compensation strategies can enhance the accuracy of the EME. [ABSTRACT FROM AUTHOR]

With the intensification of the global energy crisis and the increase in environmental awareness, energy-saving problems related to household appliances have garnered widespread attention. Here, the usage patterns of electric washing machine users and their energy-saving potential was mainly explored, so as to improve the current situation that the influencing factors of existing research behaviors were not deep enough and the energy saving potential was not specific enough. A questionnaire survey was used to gather information on 20,840 users, including individual characteristics, energy-saving awareness, and usage behavior. The study analyzed the differences in users' energy-saving awareness and behavior through a series of analysis methods, and evaluated the energy-saving and water-saving potential of electric washing machines. The results showed that user behavior such as washing mode, washing temperature, and the volume ratio of clothes significantly affected on the energy and water consumption of electric washing machines. Individual characteristics of users such as gender, age, educational background, and family income were strongly correlated with their awareness of and decisions made regarding energy conservation. Improving the energy efficiency of electric washing machines and optimizing user purchasing behavior could result in 38,787.54 GWh national energy savings potential, and 6.90 million tons of water-saving potential. This study will help manufacturers and government departments better understand consumers' usage behavior regarding electric washing machines, which could allow them to modify their market strategies and bolster the promotion and education of energy efficiency labels for electric washing machines. This also could support the nation's objectives for environmental preservation, water and energy conservation, and the sale of products with lesser energy efficiency. [ABSTRACT FROM AUTHOR]

Large‐scale energy storage solutions are crucial to ensure grid stability and reliability in the ongoing energy transition towards a low‐carbon, renewable energy based electricity supply. This article presents the evaluation of a novel low‐head pumped hydro storage system designed for coastal environments and shallow seas. The proposed system addresses some of the challenges of low‐head pumped hydro storage including the need for larger flow rates and reservoirs as well as the requirement of machinery with high efficiencies across a wide operating range to accommodate larger changes in gross head during storage cycles. It includes several units of contra‐rotating reversible pump‐turbines connected to axial‐flux motor generators within a ring dike, as well as dedicated machine‐ and grid‐side control. The technology allows for independent control of each runner, making it possible to adapt to the specific operating conditions of low‐head systems. In this work, a numerical approach is used to simulate the system's performance and dynamic behaviour under various operational conditions, including energy generation, storage, and grid support of a 1 GW system with 4 GWh of storage capacity. The potential system performance for energy balancing cycles is evaluated, and a sensitivity analysis is conducted to assess the influence of scaling the motor‐generators on performance and footprint of the plant. Additionally, the capability and limitations of the system to respond to grid demand fluctuations and provide frequency regulation services are assessed. The results demonstrate that the low‐head pumped hydro storage system is a viable large‐scale energy storage solution, capable of round‐trip efficiencies above 70% across a wide operating range. By increasing the maximum power of the electric machines, the maximum head range of the whole system is increased which correlates with a threefold increase in energy density per unit area. The dynamic simulations further show that the system can rapidly change its power output allowing it to provide frequency regulation services. Allocating 20% of its nominal power as a reserve, the new power setpoints can be reached within a maximum of 5 s independent of its initial state of charge. [ABSTRACT FROM AUTHOR]

Due to the increasing variety of converters in more electric aircraft (MEA), the stability of the MEA power system has become a critical issue that receives wide concerns. As the primary power source for MEA, the wound rotor synchronous machine (WRSM) greatly affects the stability of the whole power system, and therefore accurate modelling of the WRSM serves as the basis for the analysis of system characteristics and even stability. Nevertheless, the complex structure and coupling of WRSM bring great challenges to small‐signal modelling methods. This paper introduces a novel method to model the WRSM in a modular and cascaded manner. Instead of deriving the complex transfer function, the proposed method uses a dual port to model each component. By connecting the dual port of each component from the rear stage to the front stage in a cascaded way, a dual‐port network can be built to describe the external impedance characteristics of the WRSM. Kirchhoff law can be applied to calculate the impedance by circumventing the complicated decoupling process. The proposed dual‐port network model has been validated on a hardware‐in‐loop platform. Furthermore, the output impedance characteristics of the AC port has also been analysed with the proposed model. [ABSTRACT FROM AUTHOR]

State‐of‐the‐art model‐based predictive control techniques for AC motor drives are reviewed in this paper. A plethora of MPC algorithms with vast number of complex ideas has emerged in the last decade and this work makes an attempt to present those concepts in an intuitive, comprehensive and hierarchical manner. More emphasis is laid on finite control set model predictive control (FCS‐MPC) methods, especially predictive torque control (PTC) and predictive current control (PCC) because of their emergence as the prime focus of ongoing research in energy efficient drive control. The main focus of this review is to analyse the most recent work, signpost the future research directions, identify the core challenges and consolidate the ideas into a coherent and concise reference. A comprehensive classification based on actuation signals is presented and reviewed in detail. Then, the important challenges in MPC implementation, such as computational complexity reduction and delay compensation, weighting factor selection for multi‐objective cost functions, steady state performance and ripple reduction, parameter variations/model mismatching and achieving extended prediction horizons, are surveyed and most relevant solutions are reviewed. A detailed analysis of the last five years related work is given at the end and it is concluded that the future course seems to be diverting towards voltage vector selection with optimised phase, magnitude and duty ratios. Computational burden is still one of the main hurdle towards MPC proliferation and adaptation in AC drive control at the industrial level. However, with advent of high speed and cheaper signal processors and development of efficient algorithms, MPC is rapidly becoming the control method of choice for energy‐efficient drive control. [ABSTRACT FROM AUTHOR]

The basic design parameters and operational conditions of an enhanced vibratory lapping machine are considered. The main purpose of the research is to define the influence of different machining regimes on the roughness parameters of flat surfaces of parts made of C22 steel. The experiments are carried out at different controllable conditions of the vibratory lapping process: amplitudes of vibrations of the upper lap, forced frequencies, machining durations, and lapping paste types. The obtained results are shown in the form of bar charts describing the dependencies of the surface roughness on the machining conditions mentioned above. The major scientific novelty consists in the further development of the technologies of lapping and polishing of flat surfaces using vibratory machines with an electromagnetic drive. The presented research can be used by engineers and technologists while improving existent designs of vibratory lapping- polishing machines, as well as enhancing the corresponding machining processes. [ABSTRACT FROM AUTHOR]

The widespread application of wire electrical discharge machining (WEDM) continues to be impeded by its low cutting rate, which in large part stems from constraints related to wire failure. This research therefore explored the implications of utilizing wires thicker than the industry-standard 0.25 mm diameter wire. Given that modern WEDM machines are limited to a maximum wire diameter of only 0.4 mm, a combination of numerical and experimental approaches was adopted to compute the optimal wire diameter in consideration of the competing influences of higher machining power and larger kerf width associated with thicker wires, and to project the corresponding cutting rates. The research offers new insights into phenomena underlying wire break, and underscores the significant prospects towards enhancing process performance by re-examining WEDM in terms of thick wires. • Application of wires thicker than that used in conventional wire-EDM is explored. • Wire diameter is optimized by considering volumetric removal rate and kerf width. • Wire diameter optimization entailed models for wire failure and removal rate. • Fundamental insights into phenomena underlying wire break are revealed. • Thicker wires are projected to correspond to a manyfold increase in cutting rate. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) proliferation is accelerating, characterized by the rising quantity of electric automobiles on global roadways. The electric machine is a crucial component of an EV, and the heat generated within the motor requires consideration as it impacts performance and longevity. A prevalent form of machine in EV is the in-wheel motor (IWM), which is notable for its compact size. However, it presents more significant cooling challenges. This research offers a new cooling method to cool the IWM. The system consists of wafters mounted on the housing of the IWM. Testing was conducted to determine the effect of wafters on the thermal properties and performance of IWMs. The machine used in this research is a brushless direct current (BLDC) motor featuring an outer rotor configuration and a peak power output of 1.5 kW. Testing was carried out experimentally and by simulation, and the simulation used Ansys Motor-CAD software. The research results show that applying wafers to IWM reduces the temperature of IWM components by up to 13.1%. IWM with wafters results in a torque increase of 0.14%, a power increase of 0.64%, and an efficiency improvement of 0.6% compared to IWM without wafters. [ABSTRACT FROM AUTHOR]

This study presents a novel Hunter-Prey Optimization (HPO)-optimized Otsu algorithm in tool wear assessment and machining process quality control. The algorithm is explicitly tailored to address the challenges conventional image recognition methods face when identifying the unique wear patterns of elastic matrix abrasive tools. The proposed HPO-optimized Otsu algorithm was validated through machining experiments on silicon carbide workpieces, demonstrating superior performance in wear identification, image segmentation, and operational efficiency when compared to both the conventional 2-Dimensional (2D) Otsu algorithm and the Genetic Algorithm (GA)-optimized Otsu algorithm. Notably, the proposed algorithm reduced the average runtime by 36.99% and 28.39%, and decreased the mean squared error by 24.78% and 20.52%, compared to the 2D Otsu and GA-optimized Otsu algorithms, respectively. Additionally, this study investigates the influence of elastic tool wear on abrasive machining performance, offering valuable insights for assessing tool status and life expectancy, and predicting machining quality. The high level of automation, accuracy, and fast execution speed of the proposed algorithm makes it an attractive option for wear identification, with potential applications extending beyond the manufacturing industry to any sector that requires automated image analysis. Consequently, this study contributes to both the theoretical comprehension and practical application of tool wear assessment, providing significant benefits to industries striving for enhanced production efficiency and product quality. [ABSTRACT FROM AUTHOR]

Effectively and equitably allocating medical resources, particularly for minority groups, is a critical issue that warrants further investigation in rural hospitals. Machine learning techniques have gained significant traction and demonstrated strong performance across various fields in recent years. The determination of hyperparameters significantly influences the performance of machine learning models. Thus, this study employs Optuna, a framework specifically designed for optimizing the hyperparameters of machine learning models. Building on prior research, machine learning models with Optuna (MLOPTA) are introduced to forecast diseases of indigenous patients. The numerical results reveal that the designed MLOPTA system can accurately capture the occurrences of specified diseases. Therefore, the MLOPTA system offers a promising approach for disease forecasting. The disease forecasting results can serve as crucial references for allocating hospital resources. [ABSTRACT FROM AUTHOR]

This study aimed to improve small-scale cabbage harvesting, which usually depends on labour-intensive methods. To solve this issue, an electric cabbage harvester for single-row applications was designed and developed. The harvester integrates different units, such as cutting, conveying, precise cabbage pushing, propelling, power supply (lead acid batteries), storage bin, and handle. The study involved selecting appropriate driving motors, optimising transmission in each unit, and designing the power supply. Critical components, including the cutter, pusher, and propelling shafts, were designed and subjected to static and dynamic analysis using ANSYS R18.0. In the static analysis torque values of 27.52 Nm, 40.99 Nm, and 888.35 Nm were used for the cutter, pusher, and propelling shafts, respectively, with dynamic analysis assuming peak torques 1.5 times higher. Maximum equilibrium stresses in the static analysis were 14.20 MPa, 7.35 MPa, and 36.36 MPa, while the dynamic analysis recorded 22.996 MPa, 9.145 MPa, and 66.88 MPa, all below the material's yield stress. The results confirmed the safety and reliability of these components during field operations. A 2571 × 880 × 742 mm prototype was developed, with hand vibration and noise levels measured at 2.098 m/s2 and 78 dB(A), ensuring ergonomic safety. The harvester presents a promising solution for mechanising small-scale cabbage harvesting. [ABSTRACT FROM AUTHOR]

One of the main limitations to increasing productivity in machining processes is the emergence of self-excited vibrations. In the turning of titanium alloys, due to its mechanical properties, the material presents low machinability and is prone to the appearance of chatter during processing. Therefore, for this purpose, the material is machined using moderate machining parameters, aiming for parts with a better surface finish and avoiding the appearance of chatter. Therefore, this work seeks to evaluate the influence of machining parameters, cutting speed, feed rate, and cutting depth on vibration during the process and on the roughness of the parts. For this, experiments were carried out varying these parameters, and then the influence of each of them was analyzed using the Taguchi method for planning experiments and signal-to-noise for analyzing the variability of the collected data. It was found that the cutting depth and feed have an influence on the vibration during, where the greater the parameter, the greater the RMS of the signal. And the roughness of the parts is influenced by the cutting speed, in which the higher the speed, the lower the roughness of the parts. [ABSTRACT FROM AUTHOR]

This study investigates the impact of machining processes on the electrochemical behavior of Ti6AL4V alloy dental implants immersed in a simulated biological solution (SBF). Four distinct implant samples were crafted under varying machining conditions, and their electrochemical responses were assessed through potentiodynamic polarization (PDP) curves and electrochemical impedance spectroscopy (EIS) at room temperature. The exploration extends to the calculation of corrosion kinetics parameters via Tafel extrapolation and impedance analyses. Notably, the S750-Q0.01 sample exhibited the lowest corrosion current density (I_corr = 1.3166 µA/cm2) and the highest polarization resistance (Rp = 616 Ohm cm2), indicating superior corrosion resistance. In contrast, the S750-Q0.12 sample showed the highest dislocation density (1.037), signifying notable microstructural alterations. Electrochemical impedance results further revealed that samples machined at higher spindle speeds and lower cutting depths, such as S1000-Q0.01, exhibited higher resistance to corrosion with Rp values reaching 4396 Ohm cm2. Bioactivity analysis through Ca/P ratios demonstrated that the S1000-Q0.01 sample formed the most bioactive apatite layer with a Ca/P ratio close to hydroxyapatite standards. Phase analysis was carried out using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The outcomes revealed distinct corrosion behaviors in the SBF solution for machined implants under different conditions, signifying the significant influence of machining variations on microstructural changes and implant performance. [ABSTRACT FROM AUTHOR]

This paper presents a new means to control the processes involving energy conversion. Electric machines fed by electronic converters provide a useful power defined by the inner product of two generalized energetic variables: effort and flow. The novelty in this paper is controlling the desired energetic variables by a Data-Driven Control (DDC) law, which comprises the effort and flow and the corresponding process control. The same desired useful power might be obtained with different controls at different efficiencies. Solving the regularization problem is based on building a knowledge database that contains the maximum efficiency points. Knowing a reasonable number of optimal efficiency operation points, an interpolation Radial Base Function (RBF) control was built. The RBF algorithm can be found by training and testing the optimal controls for any admissible operation points of the process. The control scheme developed for Permanent Magnet Synchronous Motor (PMSM) has an inner DDC loop that performs converter control based on measured speed and demanded torque by the outer loop, which handles the speed. A comparison of the DDC with the Model Predictive Control (MPC) of the PMSM highlights the advantages of the new control method: the method is free from the process nature and guarantees higher efficiency. [ABSTRACT FROM AUTHOR]

Cascade control structures with inner current and outer speed loop, usually utilizing PI controllers, are widely used for electrical drives to meet high‐quality requirements. The present paper introduces design guidelines via pole placement for achieving control gains both in continuous and discrete time preserving the original cascade control structure with the initially applied controllers. This paper also presents an additional prefilter design to eliminate the undesirable effect of the reference integrals. The paper presents closed‐form expressions for the control gains as the function of desired damping ratios, the natural angular frequency of the control loop, and machine parameters to achieve the desired system dynamics. The proposed design methodology is demonstrated on brushed DC and permanent magnet synchronous machines. [ABSTRACT FROM AUTHOR]

Plunge milling, as a recognized high-efficiency metal cutting process, is an ideal choice for roughing machining of deep groove and cavity parts. However, due to the severe cutting load of plunge milling, especially the problem of plunge milling collision, and the lack of cavity plunge milling process, its engineering application has always been unsatisfactory. In this paper, starting from the cutter design, the inward-inclined structure of cutting edge is proposed, which can effectively avoid the sharp increase in cutting load caused by plunge milling collision, and realize in situ cutter retraction. At the same time, the dislocation chip-separation structure of cutting edge is also proposed, which can effectively reduce the plastic deformation of cutting layer, and the maximum cutting force is reduced by about 55%. Based on the design of the above high-performance plunging cutter, considering the constraints of material residue and isolated corner, the plunging milling process strategies for open cavity, semi-closed cavity and closed cavity are planned. Finally, the influence law of machining parameters on cutting force is explored. The setup series of verification experiments show that the inward-inclined structure and dislocation chip-separation structure of plunge milling cutter are reasonable, and the process strategies and machining parameters of cavity plunge milling are feasible. Compared with the traditional layer milling, the machining efficiency is increased by about 3.3 times. [ABSTRACT FROM AUTHOR]

Helical milling is widely used in aerospace as a key processing technology for Carbon fiber reinforced polymer (CFRP). However, the eccentric machining characteristics lead to an unusually complex pattern of cutting force and residual stress distribution on the work-piece during helical milling processing. Based on the Hashin failure criterion, a 3D FEM model of CFRP helical milling was built for analyzing the changing law of cutting force, then the three factors and three levels orthogonal tests were used to investigate the influence of machining parameters on the axial force, radial force, and minimum principal residual stress, finally, the multi-objective optimization based on grey correlation analysis was realized. The results showed that the errors of axial force and radial force obtained by simulation and experiment were 10.68% and 12.26%, respectively. The axial force and radial force were negatively correlated to the spindle speed, positively correlated to the axial cutting depth, and uncorrelated to the feed per tooth. The minimum principal residual stress was negatively correlated to the spindle speed, positively correlated to the feed per tooth, and uncorrelated to the axial cutting depth. The degree of influence on optimization of machining parameters was: spindle speed > axial cutting depth > feed per tooth. The corresponding average grey correlation degree differences were 0.280981, 0.216859, and 0.013422, respectively. The maximum value of grey correlation degree in the orthogonal test was 0.874372, and the corresponding optimal parameters combination was the spindle speed 8000 r/min, feed per tooth 0.03 mm/z, and axial cutting depth 0.2 mm/r. [ABSTRACT FROM AUTHOR]

This study leverages bibliometric analysis through the bibliometrix R-package to dissect the expansive influence of machine learning on education, a field where machine learning's adaptability and data-processing capabilities promise to revolutionize teaching and learning methods. Despite its potential, the integration of machine learning in education requires a nuanced understanding to navigate the associated challenges and ethical considerations effectively. Our investigation spans articles from 2000 to 2023, focusing on identifying growth patterns, key contributors, and emerging trends within this interdisciplinary domain. By analyzing 970 selected articles, this study uncovers the developmental trajectory of machine learning in education, revealing significant insights into publication trends, prolific authors, influential institutions, and the geographical distribution of research. Furthermore, it highlights the journals pivotal in disseminating machine learning education research, the most cited works that shape the field, and the dynamic evolution of research themes. This bibliometric exploration not only charts the current landscape but also anticipates future directions, suggesting areas for further inquiry and potential breakthroughs. Through a detailed examination of empirical evidence and a critical analysis of machine learning applications in educational settings, this study aims to provide a foundational understanding of the field's complexities and potentials. The anticipated outcome is a comprehensive roadmap that guides researchers, educators, and policymakers towards a thoughtful integration of machine learning in education, balancing innovation with ethical stewardship. [ABSTRACT FROM AUTHOR]

This paper describes the detailed design procedure of electric machines using finite element analysis (FEA). The proposed method uses the available findings from the literature and FEA results for the design procedure. In addition to electromagnetic analysis, thermal analysis is executed to examine the capability of the designed machines for handling the load in terms of thermal limits. It allows for considering the normal and overload performance of the electric machines during design. The proposed design procedure is used for designing a 100 kW induction machine (IM) and interior permanent magnet synchronous machine (IPMSM) for a parallel hybrid electric vehicle (HEV). The differences between the performance parameters of the studied machines are discussed, and the advantages and disadvantages of each design are highlighted. The designed machines are compared with commercially available electrical machines in terms of performance and power density. The comparison demonstrates that the developed machines can offer comparable performance to other designs. [ABSTRACT FROM AUTHOR]

Electric machines form an essential part of a wide range of modern systems. When speed control is required, the use of pulse width modulation‐based inverters is generally the solution of choice. It is also usual to connect the machine to the inverter using a cable. The combination of these three elements produces the potential for voltages which exceed the dc link voltage to occur at the machine terminals. Methods for predicting the terminal voltage exist; however, these methods assume that the pulses applied to the system can be considered as isolated, discrete events. The authors highlight an issue with this assumption. When a switching event occurs, it will cause a voltage disturbance in the unswitched phases of the system due to the mutual coupling between the phases. If a second switching event occurs within a short time of this event the resultant voltage will interact with the previous switching event resulting in a higher terminal voltage than would be the case for an isolated event. This effect can be problematic for insulation design if it is not considered. This issue is demonstrated, with the worst‐case scenarios identified and potential methods of reducing terminal voltage being proposed. [ABSTRACT FROM AUTHOR]

In this study, cryogenic‐temperature formal machining technique was used to process solution‐treated aluminum 7075 alloys, the mechanical properties, morphology, and microstructure of machined surface and produced chips are investigated. Results show machining temperature has a huge influence on chip and machined surface morphology, cryogenic‐temperature machining chips and machined surfaces possess better surface integrity, chips are continuous and smoother, and machined surfaces are flatter. In contrast, room‐temperature formal machining chips exhibit serrated cracks on their free surface and the machined surface produces more serious scaly spines phenomenon. Both cryogenic‐temperature and room‐temperature samples experience severe deformation, cryogenic‐temperature machining alumium 7075 alloys max microhardness has enhanced from 98 HV 0.1 to 174 HV 0.1, and cryogenic‐temperature samples' microhardness is higher than corresponding room‐temperature samples' microhardness among all machining parameters. Cryogenic‐temperature can effectively suppress dynamic recovery thus chips could store more dislocations and then possess smaller ultrafine‐grained structures, accounting for higher microhardness. Besides cryogenic‐temperature inhibits precipitation of solute cluster and second phase particles and plays a lubrication effect at tool‐chip interface, thus cryogenic‐temperature machining aluminum 7075 alloys could obtain superior machined surface quality/chip morphology to improve processability. (*: Equal contributi) [ABSTRACT FROM AUTHOR]

Robotic machining is becoming increasingly popular in modern manufacturing owing to its cost and flexibility advantages. Compared with serial robots, a hexapod-based industrial robotic work cell has a much smaller footprint for equivalent rigidity. Thus, a comprehensive understanding of the hexapod's positioning performance under machining and non-machining conditions is crucial to attain the desired robotic machining outcomes of the developed hexapod machining cell, which consists of two Fanuc F-200iB hexapods. The ballbar was chosen as the primary tool to measure the positioning performance, and a novel method based on the ballbar was proposed to facilitate measurement in various machining states. In the no-load running scenario, the positioning performance of the hexapod ranged from 33.8 µm to 99.3 µm (circularity) across 30 positions while considering thermal drift and feeding speeds. During the machining setup without cooling, the positioning performance of the hexapod machining cell demonstrated variations influenced by different machining parameters. Notably, spindle speed was identified as the most impactful factor, followed closely by feeding speed and cutting depth. By leveraging these findings, we enhanced the positioning accuracy of the robotic machining system through compensation, ultimately resulting in improved machining quality for future work. [ABSTRACT FROM AUTHOR]

Investigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller. [ABSTRACT FROM AUTHOR]

Wire-electrical discharge machining (wire-EDM) is gaining wider acceptance for producing components of Al-matrix composites (Al-MCs) that are hard to machine by traditional methodologies. The related research is primarily limited to ex-situ Al-MCs commonly reinforced with ceramic particles; however, Al-MCs reinforced with in-situ ordered intermetallics have evolved as superior composites nowadays. This research has focused on wire-EDM of in-situ Al/Al3Fe composites developed by the reactive stir-casting route. The influence of three machining variables (pulse-on-time, servo voltage, and peak-current) and one material parameter (vol% of reinforcement) have been studied following the L27 Taguchi design. The integrity of the machined surface has been characterized via measurements of surface roughness (SR) and the alteration of surface chemistry (ASC, ΣCu + Zn + O), in addition to the evaluation of kerf width (KW) as a machining performance indicator. It has been established that all four control factors are significant for KW, while ASC is influenced by all factors except vol% of reinforcement; however, only pulse-on-time is substantial for SR. Analytical models of individual responses are developed while the desirability approach helps to accomplish the multi-response optimization; several confirmation experiments establish the authenticity of these predictions with an error < 8%. Characterizations of machined surfaces and wire electrodes by FESEM and EDS techniques reveal that the surface integrity of in-situ Al/Al3Fe composites varies significantly with machining conditions. [ABSTRACT FROM AUTHOR]

In this article, to enhance the power output capability and system efficiency during charging, an approach to motor winding optimization for HEFS machine-based integrated on-board charging systems is proposed and analyzed. Firstly, the design process of the HEFS motor and the power electromagnetic components are analyzed to find the entry point for the integrated system optimization. Then, the comprehensive optimization process for the winding parameters of the HEFS motors in the integrated charging system is presented. Moreover, an integrated system based on a five-phase HEFS motor is used as an example for simulation verification. Finally, an experimental platform is built based on a 5 kW five-phase HEFS prototype. The experimental results are consistent with the simulation results. Therefore, the proposed comprehensive optimization method for windings can make the motor more suitable for building an integrated on-board charging system without affecting the electromagnetic performance of the motor. The optimization results indicate an improvement in output power and efficiency of the system in charging mode. [ABSTRACT FROM AUTHOR]

The rake angles on both sides of the cutting edges of the hourglass worm gear hob significantly influence its cutting performance, which, in turn, plays a decisive role in the surface quality of the machined worm wheel. To balance the rake angles along the tooth height direction of each hob tooth and enhance the overall cutting performance of the hob, this paper proposes a method that utilizes a rotating paraboloid surface to generate the helical rake face of the hourglass worm gear hob. First, the conjugate condition equations for the rake face generated by the rotating paraboloid surface are derived. A mathematical model for the helical rake face of planar double-enveloping hourglass worm gear hob is established. This study explores the influence of two machining parameters on the rake angle, specifically the milling drive ratio coefficient k and the geometric parameter of a parabolic milling cutter p. Through a systematic analysis of the variations in rake angle at the dividing toroidal surface and along the tooth height direction, the optimal parameter values were identified as k = 0.9115 and p = 0.6834. The results show that, after optimization, the hob rake angle range is around ±4.7°, with a maximum rake angle difference of 6.3072° along the tooth height direction, and the rake angles on both sides of the teeth are more balanced. The structure of the rake face is more reasonable, reflecting the feasibility of rotating paraboloid tools for forming tools in the machining of complex surfaces. [ABSTRACT FROM AUTHOR]

The sequential direct start-up of four asynchronous machines (AM) with a capacity of 8 MW each from a 6 kV electrical network is considered. The start-up transient modes that arise in the network as a result of such an AM start-up are investigated, as well as the possible consequences for the network. To facilitate the direct start of AM with a capacity of 8 MW, it is proposed to use a conditional twelve-phase controlled compensating converter, which limits the amount of starting over currents in the network. References 10, figures 6. [ABSTRACT FROM AUTHOR]

This paper presents a method aimed at improving comprehension of AC electric machine principles by facilitating the learning of the Rotating Magnetic Field (RMF) through visualization tools provided by Finite Element Method (FEM) software. First, traditional methods used in textbooks to explain RMF in electric machines are reviewed, with an analysis of various instructional strategies. Acknowledging the limitations of these conventional approaches and the inherent complexity of RMF comprehension, a novel visualization method is proposed. Understanding RMFs in electric machines is fundamental for electrical engineers due to their crucial role in electric machine design and optimization. While textbooks typically rely on mathematical explanations and simple sketches, advancements in computer and software technology offer opportunities to utilize finite element tools for enhanced comprehension. Through dynamic animations and interactive simulations, emphasis is placed on prioritizing conceptual understanding over mathematical descriptions. Furthermore, this paper explores the development of pre‐simulation models in FEM tools to facilitate RMF learning in AC electric machines, and the process of creating a model to teach rotating magnetic fields in electric machines is carefully outlined. This approach holds promise for engineers seeking a deeper understanding of electric machines and can also be utilized by educators to enhance their teaching methodologies. [ABSTRACT FROM AUTHOR]

This study aimed to improve small-scale cabbage harvesting, which usually depends on labour-intensive methods. To solve this issue, an electric cabbage harvester for single-row applications was designed and developed. The harvester integrates different units, such as cutting, conveying, precise cabbage pushing, propelling, power supply (lead acid batteries), storage bin, and handle. The study involved selecting appropriate driving motors, optimising transmission in each unit, and designing the power supply. Critical components, including the cutter, pusher, and propelling shafts, were designed and subjected to static and dynamic analysis using ANSYS R18.0. In the static analysis torque values of 27.52 Nm, 40.99 Nm, and 888.35 Nm were used for the cutter, pusher, and propelling shafts, respectively, with dynamic analysis assuming peak torques 1.5 times higher. Maximum equilibrium stresses in the static analysis were 14.20 MPa, 7.35 MPa, and 36.36 MPa, while the dynamic analysis recorded 22.996 MPa, 9.145 MPa, and 66.88 MPa, all below the material’s yield stress. The results confirmed the safety and reliability of these components during field operations. A 2571 × 880 × 742 mm prototype was developed, with hand vibration and noise levels measured at 2.098 m/s2 and 78 dB(A), ensuring ergonomic safety. The harvester presents a promising solution for mechanising small-scale cabbage harvesting.

Abstract Electric machines form an essential part of a wide range of modern systems. When speed control is required, the use of pulse width modulation‐based inverters is generally the solution of choice. It is also usual to connect the machine to the inverter using a cable. The combination of these three elements produces the potential for voltages which exceed the dc link voltage to occur at the machine terminals. Methods for predicting the terminal voltage exist; however, these methods assume that the pulses applied to the system can be considered as isolated, discrete events. The authors highlight an issue with this assumption. When a switching event occurs, it will cause a voltage disturbance in the unswitched phases of the system due to the mutual coupling between the phases. If a second switching event occurs within a short time of this event the resultant voltage will interact with the previous switching event resulting in a higher terminal voltage than would be the case for an isolated event. This effect can be problematic for insulation design if it is not considered. This issue is demonstrated, with the worst‐case scenarios identified and potential methods of reducing terminal voltage being proposed.

Abstract Cascade control structures with inner current and outer speed loop, usually utilizing PI controllers, are widely used for electrical drives to meet high‐quality requirements. The present paper introduces design guidelines via pole placement for achieving control gains both in continuous and discrete time preserving the original cascade control structure with the initially applied controllers. This paper also presents an additional prefilter design to eliminate the undesirable effect of the reference integrals. The paper presents closed‐form expressions for the control gains as the function of desired damping ratios, the natural angular frequency of the control loop, and machine parameters to achieve the desired system dynamics. The proposed design methodology is demonstrated on brushed DC and permanent magnet synchronous machines.

Abstract The authors propose an analytical modelling approach for the linear transverse flux permanent magnet motor using the magnetic equivalent circuit method. The main focus of this study is to predict the phase flux‐linkage characteristic of the motor. Essential equations required for implementation of the model and how to solve it are described clearly so that someone can use it easily. A typical motor is selected to apply the proposed model and simulation results, including static characteristics of flux‐linkage and thrust, are presented. To validate the developed analytical model, the discussed motor is also analysed with 3D finite element method using MAXWELL software and the obtained simulation results are compared to each other.

Background: In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers. Objective: We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF. Methods: This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort. Results: Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668–0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652–0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups. Conclusions: The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy. [ABSTRACT FROM AUTHOR]

The authors propose an analytical modelling approach for the linear transverse flux permanent magnet motor using the magnetic equivalent circuit method. The main focus of this study is to predict the phase flux‐linkage characteristic of the motor. Essential equations required for implementation of the model and how to solve it are described clearly so that someone can use it easily. A typical motor is selected to apply the proposed model and simulation results, including static characteristics of flux‐linkage and thrust, are presented. To validate the developed analytical model, the discussed motor is also analysed with 3D finite element method using MAXWELL software and the obtained simulation results are compared to each other. [ABSTRACT FROM AUTHOR]

The PISA 2022 literacy assessment highlights a significant decline in math performance among most OECD countries, with the magnitude of this decline being approximately three times that of the previous round. Remarkably, Hong Kong, Macao, Taipei, Singapore, Japan, and Korea ranked in the top six among all participating countries or economies, with Taipei, Singapore, Japan, and Korea also demonstrating improved performance. Given the widespread concern about the factors influencing secondary-school students' mathematical literacy, this paper adopts machine learning and the SHapley Additive exPlanations (SHAP) method to analyze 34,968 samples and 151 features from six East Asian education systems within the PISA 2022 dataset, aiming to pinpoint the crucial factors that affect middle-school students' mathematical literacy. First, the XGBoost model has the highest prediction accuracy for math literacy performance. Second, 15 variables were identified as significant predictors of mathematical literacy across the student population, particularly variables such as mathematics self-efficacy (MATHEFF) and expected occupational status (BSMJ). Third, mathematics self-efficacy was determined to be the most influential factor. Fourth, the factors influencing mathematical literacy vary among individual students, including the key influencing factors, the direction (positive or negative) of their impact, and the extent of this influence. Finally, based on our findings, four recommendations are proffered to enhance the mathematical literacy performance of secondary-school students. [ABSTRACT FROM AUTHOR]

Energy transition and decarbonization present significant challenges to transportation. Electric machines, such as motors and generators, are increasingly replacing internal combustion engines to reduce greenhouse gas emissions. This study focuses on enhancing the energy efficiency of electric machines used in vehicles, which are predominantly powered by batteries with limited energy capacity. By investigating various control strategies, the aim is to minimize energy losses and improve overall vehicle performance. This research examines two types of electric motors: Permanent Magnet Synchronous Motor (PMSM) and Induction Motor (IM). Real-time loss measurements were conducted during simulated driving cycles, including acceleration, constant speed, and braking phases, to mimic typical driving behavior. The simulation utilized characteristics from commercial vehicles, specifically the Renault Zoé and Bombardier eCommander, to assess the controls under different configurations. This study employed the Energetic Macroscopic Representation (EMR) formalism to standardize the analysis across different motors and controls. The results demonstrate significant loss reductions. The controls investigated in this study effectively reduce energy losses in electric motors, supporting their applicability in the automotive industry. [ABSTRACT FROM AUTHOR]

Due to the anisotropic characteristic of carbon fiber-reinforced silicon carbide ceramics, the fiber orientation angle significantly affects the grinding force. Therefore, it is important to study the influence rule of different fiber orientations on the grinding force of 2.5D-Cf/SiC composites. To study the comprehensive influence of machine tool parameters and the anisotropy of carbon fiber-reinforced ceramic matrix composites on the grinding force, two-dimensional ultrasonic plane grinding was studied by orthogonal test and single-factor experiment. Based on the multi-exponential fitting analysis method of multiple linear regression equation, the empirical equations of power exponential grinding force prediction model of 2D ultrasonic-assisted grinding and conventional grinding 2.5D-Cf/SiC composites at 0°, 45°, and 90° fiber orientation and considering fiber orientation and ultrasonic amplitude were established, respectively. To verify the empirical formula model in predicting the grinding force of 2.5D-Cf/SiC composites under various fiber orientation angles, the regression equation and regression coefficient of the model were examined. The influence of 2.5D-Cf/SiC grinding parameters on the grinding force was analyzed. The parameters of the grinding force model were optimized based on range analysis and variance analysis, and the optimal process parameter combination was obtained. The results show that the grinding force is negatively correlated with the linear speed and positively correlated with the feed speed and grinding depth within the range of experimental parameters. The maximum reduction of the normal grinding force is 29.78% when the line speed is 10.48 m/s, the feed speed is 100 mm/min, the grinding depth is 50 μm, and along the 45° fiber direction. The optimal grinding parameter combination is a line speed of 23.60 m/s, feed speed of 5 mm/min, and grinding depth of 10 μm along the 0° fiber orientation. [ABSTRACT FROM AUTHOR]

Thermal error is a critical factor influencing the machining accuracy of CNC machine tools, so it is essential to comprehensively model and compensate for thermal errors in CNC machine tools. This paper proposes a deep attentional residual network thermal error prediction model driven by thermal image inputs. In contrast to traditional models that solely rely on temperature data, the proposed model utilizes thermal image data as a key input parameter and incorporates temperature data from sensitive points to fully represent the machine's temperature distribution. Furthermore, the attention mechanism is used to optimize the hyperparameters and network structure of the residual network model. Transfer learning is employed to improve training efficiency, reduce data requirements, and enhance the model's transferability. The optimized model achieves a prediction accuracy of 99.5% and converges more quickly. Finally, thermal error compensation experiments are conducted on the platform of the Siemens 840D system with an average effect of more than 70%. The proposed thermal error compensation method is effective and provides a foundation for precision machining. [ABSTRACT FROM AUTHOR]