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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]

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]

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 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 framework to 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]

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]

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]

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]

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]

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Titanium and its new-generation alloys are widely employed in high-end applications due to their special properties. Modern industries frequently use micro – Electro Discharge Machining (μ-EDM), a non-conventional machining technique that is especially useful for processing hard to machine materials like titanium alloys. μ-EDM can produce intricate shapes with excellent dimensional precision. The main emphasis of this work is an experimental analysis of the μ-EDM of TNZT alloy (Ti-35Nb-7Zr-5Ta) employing a tungsten carbide (WC) electrode. The results showed that with the increase in capacitance (C) 10nF to 100nF at Voltage (V) 80 V, the machining performance parameters such as material removal rate (MRR), overcut (OC), crater area, surface microhardness, and surface roughness increase by 6.38, 1.52, 4.66, 1.07, and 3.29 times, respectively. However, the circularity error of the µ-hole increases by 1.81 times. This study intends to show how the two key machining factors (C & V) influence significant machining performance parameters. [ABSTRACT FROM AUTHOR]

Tool wear management and real-time machining quality monitoring are pivotal components of realizing smart manufacturing objectives, as they directly influence machining precision and productivity. Traditionally, measuring and analyzing cutting force fluctuations in the time domain has been employed to diagnose tool wear effects. This study introduces a novel, indirect approach that leverages spindle-load current variations as a proxy for cutting force analysis. Compared to conventional methods relying on machining vibration or direct cutting force measurement, this technique provides a safer, simpler, and more cost-effective means of data aquisition, with reduced computational demands. Consequently, it is ideally suited for real-time monitoring and long-term analyses such as tool-life prediction and surface-roughness evolution induced by tool wear. An intelligent tool wear monitoring system was developed based on spindle-load current data. The system employs extensive cutting experiments to identify and analyze the correlation between tool wear and spindle-load current signal patterns. By establishing a tool wear near-end-of-life threshold, the system enables intelligent monitoring using C#. Experimental validation under both roughing and finishing conditions demonstrated the system's exceptional diagnostic accuracy and reliability. The results demonstrate that the current ratio threshold value has good universality in different materials, indicating that monitoring the machining current ratio to estimate the degree of tool wear is a feasible research direction, and that the average error between the experimental surface-roughness measurement value and the predicted value was 10%. [ABSTRACT FROM AUTHOR]

Annealing treatment in a mild magnetic field has been investigated on the mechanical and magnetic properties of the FeCo-2V alloy. Samples were annealed at 750 °C and 880 °C for various holding times (3, 6, or 9 hours) with an external magnetic field (0, 3300 or 5500 A/m). The mechanical strength, magnetic properties, and grain size of annealed samples were measured. The results show that the magnetic-field-annealing (MFA) treatment retards the grain growth, and the small grains result in an increase in the mechanical strength. Moreover, there is a significant improvement in the magnetic properties. The unusual correlation between the small grains and improved magnetic properties is explained through grain shape anisotropy. Moreover, MFA is found to be more effective in improving the magnetic properties of FeCo-2V alloy than extending the holding time. The 880-6-5500 samples exhibit a high maximum permeability of about 35000, the lowest core loss and coercivity with 34.4 W/kg and 85.9 A/m, respectively at 2.0 T/400 Hz. The finding reveals a practical route to improve the mechanical and magnetic performance of electric machines by annealing under a weak magnetic field. [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.

One potential eco-friendly variant of electro-discharge machining is dry electro-discharge machining (EDM), in which the liquid hydrocarbon–based dielectric is substituted with a gaseous medium. The primary challenge associated with dry EDM is the excessive dissipation of heat within the machining gap, which restricts its utilisation only to a microscale machining. Consequently, further modifications to the underlying mechanism of the process are being undertaken with the aim of efficient industrialising it on a larger scale. In the present study, a novel approach is proposed to enhance the efficiency of dry-EDM process while using carbon dioxide as a gaseous medium together with introducing additional external workpiece cooling with deionised water. A series of experiments were conducted to determinate the impact of external workpiece cooling with deionised water and the main machining parameters, namely pulse-on time and current intensity gas pressure, on the material removal rate, working electrode wear, and surface integrity of Inconel 625 during EDM in milling kinematics. The results demonstrated that, under the same machining parameters, the wear of the working electrode, the surface roughness, and the thickness of the recast layer were significantly reduced in the EDM with external workpiece cooling in comparison to the dry-EDM process without water cooling. Furthermore, the EDM with coolant exhibited superior performance in comparison to the dry-EDM process due to the fact that there were fewer changes in the surface morphology and chemical composition of the machined material. [ABSTRACT FROM AUTHOR]

Tolerance allocation is a design task with a strong potential impact on manufacturing choices. In practice, however, it is often carried out with simple heuristics rather than with an optimization approach like those available in research literature. One reason could be the difficulty in predicting the economic benefits resulting from optimization. To allow for such considerations, the paper proposes a procedure to estimate the cost reduction that optimization allows compared to three traditional allocation methods (equal tolerances, precision factor, proportional to nominal). The chosen optimization method is based on the closed-form solution of a problem of cost minimization with a stackup constraint, using the extended reciprocal power cost-tolerance function. Compared to other methods, it provides analytical expressions of both the allocated tolerances and the associated costs. When applied to specific cases, these help recognize the conditions in which optimization allows a significant reduction in manufacturing costs. The results show that this occurs when the features of the same dimension chain have very different properties regarding a set of design variables with particular influence on the amount of machining required. [ABSTRACT FROM AUTHOR]

Thin-walled carbon/carbon (C/C) composites are a typical difficult-to-machine material due to the characteristics of thin walls, low stiffness, and high strength. In this paper, three ultrasonic-assisted machining methods and surface morphology of C/C composite thin-walled small holes were studied. The influence of machining parameters on average axial force and hole surface morphology was explored by single-factor experiments and orthogonal experiments. A quantitative comparison was conducted to evaluate the machining defects of drilling, reaming, and grinding. The results of single-factor experiments showed that the surface quality of drilling initially improved and then deteriorated with the increase of spindle speed. Additionally, increasing feed rate resulted in a deterioration in surface quality, while increasing amplitude to a certain extent led to an improvement. In orthogonal experiments, the effect of ultrasonic amplitude on the average axial force of reaming and grinding was greater than that of drilling. In terms of surface quality, the minimum evaluation factor Qc of the grinding, reaming, and drilling could reach 1.0137, 1.0198, and 1.0253, respectively. Further optimized by a multivariate optimization function, the optimal parameters of grinding were obtained as the speed of 6200 r/min, feed rate of 40 mm/min, and amplitude of 8 μm and the minimum values of Qc could reach 1.0128. [ABSTRACT FROM AUTHOR]

Electro-chemical machining (ECM) can safely be categorized as being a unique unconventional machining technique, in which the role of thermal defects are not involved. The NiTiCo shape memory alloy has high yield strength coupled with good resistance to aggressive aqueous environments, which makes it a potential candidate for use in several bio-medical applications. This research concentrates on electro-chemical machining of a NiTiCo alloy. Taguchi's Design of Experiment (DOE) containing three-parameters and three-factors was used to conduct the experimental study. The influence of concentration of the sodium chloride electrolyte, voltage, and frequency on output characteristics, such as (i) material removal rate (MRR), (ii) surface roughness (SR), and (iii) over-cut (OC), was analyzed. Experimental outputs revealed the voltage (45.29%) to be the most influential among the three controlling parameter. Grey relational analysis (GRA) was utilized to discover the ideal parametric relation and optimize machining characteristics. [ABSTRACT FROM AUTHOR]

In the realm of electric machines, there has been an increasing interest in multiphase (greater than three-phase) and hybrid excited machines. The benefits of multiphase machines include improved power density, efficiency, reliability, and fault tolerance, while for hybrid electric machines, the literature offers a variety of topologies, each with its own advantages and disadvantages. In essence, the term hybrid for electric machines is used when there is more than one source of excitation, e.g., permanent magnet (PM) excitation combined with or assisted by wound field (WF) excitation. This paper presents an extensive review of the latest topologies in hybrid machines. It explores fundamental principles, multiphase winding, and the advantage of multiphase over three-phase, as well as a comparison of ripple in the DC link for different numbers of phase winding. Additionally, this review discusses applications across industries, including automotive, aerospace, marine, and renewable energy systems. This paper later studies the motoric and generator modes of hybrid machines while considering the machine characteristics in both of these modes. [ABSTRACT FROM AUTHOR]

One of the most common faults in electric machines is a turn‐to‐turn short circuit (TTSC), which may destroy coil insulation and demagnetise the magnet. In addition, the phase‐to‐phase short circuit (PPSC) fault, which can have even more destructive effects than the TTSC fault, is introduced and analysed. The equivalent magnetic network (EMN) method, with high modelling accuracy and a short computation time, is employed for healthy and faulty machines. The current signal under fault conditions is analysed in the dqo frame, showing the presence of the second harmonic component in its waveform. This fault detection index is processed using the signal processing technique of discrete(wavelet transform (DWT). Besides, energy analysis is used to distinguish TTSC and PPSC faults. Finally, finite element and EMN modelling results are compared with the experimental data of the prototyped permanent magnet generator. The results show that the combination of the proposed EMN method and DWT has very good accuracy and speed. Furthermore, the proposed fault detection method remains unaffected by various linear loads with different power factors. The cover image is based on the article Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach by Mehrage Ghods et al., https://doi.org/10.1049/elp2.12452 [ABSTRACT FROM AUTHOR]

The authors present two fast and accurate methodologies for the computation of eddy current losses in the axially laminated rotor of a synchronous reluctance machine. The methodologies are based on different combinations of the finite element method in time and frequency domains with 2D and 3D formulations. First, a comparative study of the 2D and 3D formulations for loss calculation is presented, considering various load angles of the machine to illustrate the problem of eddy‐current losses in this type of machines and its dependence on the load angle. The influence of the iron saturation on the loss calculation is also evaluated in these computations. A novel correction factor based on the computations at two load angles is proposed to convert the losses computed from a 2D model to match those computed from a 3D model. For the sake of generality, investigations are also conducted for various thicknesses of the lamination layers and different machine lengths, and an analytical method to describe the dependency of eddy‐current losses on the load angle of the machine is introduced. Moreover, a simplified method is proposed for modelling eddy currents in the frequency domain and calculating losses in an axially laminated structure based solely on the results of a magnetostatic solution. The results obtained by the simplified model demonstrate excellent agreement with the full 3D magneto‐dynamic simulation. Overall, the findings contribute to understanding and accurately characterising the eddy current losses in axially laminated rotors, offering potential insights for designing and optimising axially laminated synchronous reluctance electric machines. [ABSTRACT FROM AUTHOR]

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of the time to event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time, possibly induced by measured covariates. Inverse probability of truncation weighting can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply semiparametric theory to find the efficient influence curve of the expectation of an arbitrarily transformed survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches were developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two datasets from practice, with different right-censoring patterns. [ABSTRACT FROM AUTHOR]

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively. [ABSTRACT FROM AUTHOR]

The rapid growth in the use of AI techniques, mainly machine learning (ML), is revolutionizing different industries by significantly enhancing decision-making processes through data-driven insights. This study investigates the influence of using ML, particularly supervised and unsupervised learning, on rational decision-making (RDM) within Jordanian e-government, focusing on the mediating role of trust. By analyzing the experiences of middle-level management within e-government in Jordan, the findings underscore that ML positively impacts the rational decision-making process in e-government. It enables more efficient and effective data gathering, improves the accuracy of data analysis, enhances the speed and accuracy of evaluating decision alternatives, and improves the assessment of potential risks. Additionally, this study reveals that trust plays a critical role in determining the effectiveness of ML adoption for decision-making, acting as a pivotal mediator that can either facilitate or impede the integration of these technologies. This study provides empirical evidence of how trust not only enhances the utilization of ML but also amplifies its positive impact on governance. The findings highlight the necessity of cultivating trust to ensure the successful deployment of ML in public administration, thereby enabling a more effective and sustainable digital transformation. Despite certain limitations, the outcomes of this study offer substantial insights for researchers and government policymakers alike, contributing to the advancement of sustainable practices in the e-government domain. [ABSTRACT FROM AUTHOR]

The results of numerical investigation of electric motors with permanent magnets of cylindrical type are presented. It is shown that replacement of a squirrel-cage rotor in a standard induction motor by a rotor with permanent magnets allows to obtain a significant increase in the specific power of the electric machine. Numerical investigations and analysis of characteristics of the electric motor with tangentially magnetized neodymium magnets for five configurations of the rotor magnetic system are carried out. It is shown that to ensure maximum specific characteristics in electric motors for electric vehicles it is necessary to take into account the driving cycle and to carry out liquid cooling of the electric motor. Thermal calculations taking into account the driving cycle of NEDC are also performed and it is established that the use of liquid cooling allows to provide cooling of neodymium magnets and windings to a temperature lower than critical at increased current in the windings. Characteristics of the investigated electric motors are calculated in Simcenter MagNet and Simcenter MotorSolve software packages. [ABSTRACT FROM AUTHOR]

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications. [ABSTRACT FROM AUTHOR]

This paper presents a thermal analysis of a switched reluctance machine (SRM) with a 6/4 pole topology. Employing a two-dimensional thermal finite element model in Maxwell 2D integrated into a lumped parameter thermal network in MotorCAD, the research aims to realize critical temperature points within the SRM, focusing on the windings and laminations to identify potential hotspots. To ensure continuous operation without compromising structural integrity, temperature constraints of 150 °C for the windings and 100 °C for the laminations were enforced. The investigation extends to the cooling domain by utilizing a housing cooling jacket and ethylene glycol and water ratio (EGW) of 60/40 as the cooling fluid. An exploration of various volume flow rates revealed that 15 l/min was the optimal choice, resulting in the lowest temperatures observed in both the winding and lamination regions. This study emphasizes the shift of hotspot temperatures from the stator winding in the low-speed region to the rotor in the high-speed range, impacting the overall continuous operating time. This holistic examination significantly contributes to a broader understanding of electric machine thermal management and offers invaluable insights for practical applications in electric vehicles, manufacturing processes, and various industrial settings. [ABSTRACT FROM AUTHOR]

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. This study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations. [ABSTRACT FROM AUTHOR]

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding. [ABSTRACT FROM AUTHOR]

To accurately, efficiently and stably predict the thermal displacement of the spindle, a prediction model based on the beluga whale algorithm (BWO) optimized bi-directional long and short-term memory neural network (BiLSTM) is introduced in this paper. Firstly, the thermal characterization and simulation analysis of the spindle are carried out, and the temperature and thermal displacement change characteristics of the spindle are obtained. Then the thermal deformation experiment of the spindle is carried out, and the temperature and displacement sensors are set up reasonably according to the temperature and thermal displacement change characteristics of the spindle, and the experimental data are collected and analyzed. The adaptive and globally convergent BWO is selected to optimize network parameters of BiLSTM, and the BWO-BiLSTM prediction model is constructed by learning the nonlinear correlation characteristics between spindle temperature and axial thermal displacement. The constructed BWO-BiLSTM prediction model is compared with other prediction models, and it is found through analysis that the prediction results output from the BWO-BiLSTM model have better accuracy and stability. The results of the study can provide a certain theoretical basis and technical support in predicting the spindle thermal displacement, which can help to promote the precision machining production of electric spindles. • Spindle thermal characterization and simulation studies are carried out. • Significant changes in axial temperature and thermal displacement are found. • Sensors are set up according to the results of studies. • A BWO-BiLSTM model is built to predict the spindle thermal displacement. [ABSTRACT FROM AUTHOR]

This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. The primary objective is to discern the nuanced influences of various algorithms on predictive performance, with a specific focus on the F1 score metric as evaluation criterion. Four ML algorithms have been carefully assessed in this study: random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression (LR). The choice of these algorithms reflects their prevalence in software defect prediction literature and their diversity. Through rigorous experimentation and analysis, the investigation unveils compelling evidence affirming the superiority of RF over its counterparts. The F1 score utilized as evaluation metric, capturing the delicate balance between precision and recall, essential in defect prediction scenarios. The nuanced examination of algorithmic efficacy provides practical insights for developers and practitioners navigating the challenges of cross-project defect prediction. By leveraging the rich and diverse AEEEEM dataset, this study ensures a comprehensive exploration of algorithmic influences across varied software projects. The findings not only contribute to the academic discourse on defect prediction but also offer practical guidance for real-world application, emphasizing the pivotal role of RF as a tool in enhancing predictive accuracy and reliability. [ABSTRACT FROM AUTHOR]

When a long cable is included in power conversion systems, it causes adverse effects, such as voltage spikes and ringing at load terminals. These nonideal voltages can break the insulation of electric machines, such as transformers and motors, and reduce their lifespan. To estimate such voltage characteristics, cable impedance should be modeled on the basis of the cable length. In this paper, two cable impedance models, a multiphysics model and an equivalent circuit model, are introduced. The multiphysics model using Ansys Q3D Extractor is suggested in consideration of the structure, material, and length of a practical cable. Meanwhile, the equivalent circuit model can be quickly utilized to examine voltage spikes and frequency. The accuracy of the proposed models is verified through simulation and the experimental results based on a motor drive system equipped with 30 and 100-m cables. [ABSTRACT FROM AUTHOR]