Spindle Motor Research Articles

Overview of Electric Spindle Cooling Patents

Background: The heating caused by the high-speed rotation of the motorized spindle motor and the friction-induced heating of the bearings are inevitable. After the temperature rise, the relative position and size of the spindle and other parts or components of the machine tool will differ from that before the temperature rose, forming different temperature fields and then producing different degrees of thermal expansion, leading to processing errors. This paper analyzes the characteristics and application scenarios of various cooling mechanisms for motorized spindles. Objective: By analyzing and discussing the patents related to the cooling methods of motorized spindles, this study aims to explore the factors of temperature variation during the working process of the motorized spindle and seeks to predict future research and development in motorized spindle cooling equipment. Methods: The present status of various representative patents related to the cooling device of the motorized spindle was analyzed, and the influence of the cooling device of the motorized spindle on the working effect of the spindle was obtained. According to the classification of the characteristics of the cooling device, the degree of development of the existing cooling device in the cooling effect, temperature control, and other aspects is analyzed, and the latest patent technology is expounded and compared. Results: This paper analyzed the characteristics of cooling devices with different structures, analyzed the main problems existing in their development, and discussed solutions to the problems existing in motorized spindle cooling mechanisms and the current and future research directions. Conclusion: It is found that the circulating passage cooling device has the broadest application range and cools the motorized spindle effectively. The cooling structure has little influence on the motorized spindle and prolongs its service life. The cooling effect of adaptive and systemic cooling devices is better. However, there are some defects in both of them and so there is room for further development in both the cooling efficiency and machining accuracy.

Experimental research on iron loss of high-speed spindle motor under variable frequency drive power supply

Experimental research on iron loss of high-speed spindle motor under variable frequency drive power supply

Optimising Sensor Placement for Tool Condition Monitoring: A Comparative Analysis of Acoustic Emission Data

In the monitoring of machining processes and in the context of tool condition monitoring, the position of acoustic emission sensors affects the quality of the resulting information. This drives this comparative study which presents a comparison of acoustic emission data recorded simultaneously by a sensor attached to the side of the milling table and another sensor near the spindle during a tool wear test on a CNC milling machine. The signal information is clearly influenced by the different positions. Whereas the signals near the spindle are dominated by vibrations of the spindle motor during machining, these strong contributions do not occur in the recorded data of the machining process detected by the sensor mounted to the table. However, the position of the sensor near the spindle has the advantage of maintaining a constant distance to the source of the acoustic emissions of the machining process, as the sensor moves with the tool. In contrast, the sensor attached to the milling table is affected from the fact that the distance to the milling tool is constantly changing. This results in different dominant frequencies, that are analysed in this study. To analyse both the entire frequency range and the higher frequencies, two distinct approaches are employed, one without and one with the use of a high-pass filter. Furthermore, the recorded signals were used to calculate the characteristic features, namely the Partial Powers, thereby enabling assessment of the condition of the tool. The results demonstrate that this single feature type is sufficient for training a machine learning model to predict the tool wear, focusing on the flank wear width. This study analyses the efficiency of wear prediction with respect to both sensors and frequency spectra, and compares the results. In this context, the most accurate predictions were achieved with Gaussian process regression models.

Advancements and Best Practices in Hard Disk Drives (HDDs) Repair: A Comprehensive Review of Diagnostic Techniques, Repair Methods, and Preventive Strategies

Hard Disk Drives (HDDs) remain a crucial component of data storage systems despite the advent of Solid State Drives (SSDs). However, the mechanical and electronic nature of HDDs makes them susceptible to various failures over time. This article provides a comprehensive examination of HDD repair, focusing on the anatomy and function of these devices, common causes of failure, diagnostic techniques, and effective repair methods. Mechanical failures, such as head crashes and spindle motor issues, electronic failures like PCB damage, and logical failures including file system corruption and bad sectors are explored in detail. Diagnostic approaches utilizing visual inspection, diagnostic software tools, hardware-based diagnostics, and SMART monitoring are discussed to aid in identifying issues accurately. Repair methods covered include data recovery techniques, mechanical repairs, electronic component replacement, and firmware fixes. Real-world case studies illustrate practical challenges and solutions in HDD repair, highlighting the complexities involved. Additionally, best practices and safety measures for handling and maintaining HDDs are provided to prevent future failures and ensure data integrity. This article aims to equip data recovery professionals, IT technicians, and tech enthusiasts with the knowledge and skills necessary to diagnose and repair HDD issues effectively. By integrating technical insights and practical approaches, it serves as a valuable resource for enhancing the reliability and longevity of HDDs in various applications.

Tool wear feature extraction in BTA deep hole drilling process based on maximum probability multi-synchrosqueezing transform of spindle current signal

Multi-synchrosqueezing transform (MSST) enhances the concentration of the time–frequency representation (TFR) of time-varying signals by introducing multi-instantaneous frequency (IF) estimation operator. However, the existence of estimation bias may lead to uncertainty in the discrete values of IF during computer computations, causing issues with non-reassigned points. In this paper, a maximum probability multi-synchrosqueezing transform (MPMSST) is proposed, which incorporates distribution probability during the discretization process of the IF, taking the frequency corresponding to the maximum distribution probability as the determined value of IF. Through verification with multi-component time-varying simulation signals, MPMSST not only effectively eliminates non-reassigned points but also accurately estimates the theoretical time–frequency curve. By applying MPMSST to analyze spindle motor current signals during deep hole machining, TFRs under different tool wear states are obtained. The singular value decomposition (SVD) method is utilized to extract the principal component of the TFR, and a tool wear state feature index is constructed based on the sum of squares of the first 10 singular values. The correlation coefficient and covariance between the proposed feature index and the tool wear curve are 0.835 and 5.704 respectively, indicating a good consistency of the index with tool wear changes, and a high sensitivity to variations in tool wear state.

A Review of Machine Tool Motorized Spindle Variable Pressure Preload Technology Research

High-precision spindles are a vital study topic because of the growing demand for highspeed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent.

Intelligent Sensing of Thermal Error of CNC Machine Tool Spindle Based on Multi-Source Information Fusion.

Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment's results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems.

Design of machine tool cooling system and optimization of cooling parameters based on differential cooling control strategy

Thermal errors of machine tools account for 40–70% of machining errors, with motorized spindles and linear motors being the primary heat sources adversely impacting machine tools’ precision. This article aims to solve the issue of significant thermal errors brought about by numerous internal heat sources, complex heat transfer processes, and uneven temperature field distributions. This article proposes a cooling control strategy that employs multiple channels with varying water temperature and velocity. The article also presents the design of the cooling system for a machine tool. The established multi-channel cooling control equation is then used to optimize the cooling water parameters of different channels. Ultimately, finite element simulations and cooling experiments verify the system’s efficacy. Based on the data collected from simulations and cooling experiments, it has been demonstrated that the motorized spindle motor’s thermal balance temperature decreased by 58.5% after cooling, and the shaft deformation was reduced by 70.2%. Similarly, the linear motor’s thermal balance temperature decreased by 37.13%, and the parallelism error of the machine tool guide rail was reduced by 72.3%. These results demonstrate convincingly that the machine tool cooling system can achieve more accurate and efficient cooling, which can provide a reference for machine tool thermal error control technology.

Research on the thermal characteristics of motorized spindle under magnetic–thermal coupling based on digital twin

In operation, traditional mechanistic models fail to iteratively update with the motorized spindle, leading to inaccuracies in dynamic data like the motorized spindle's internal magnetic and thermal fields. To address the issue, we propose a data-driven, magnetic–thermal bidirectional coupling twin model based on digital twin technology. This model, considering temperature effects on the motorized spindle motor's materials, enables dynamic updates through a twin database and service platform. Comparative analysis shows minor temperature calculation differences between this twin model and traditional mechanistic models at speeds below 9000 r/min. As the speed reaches 9000 r/min and the motorized spindle's efficiency peaks, the disparity between the two models grows. Experimental data show that the twin model outperforms the traditional model in temperature accuracy at the motorized spindle's test points, achieving up to a 4.57% improvement in front bearing temperature calculations. The proposed motorized spindle twin model demonstrates higher accuracy, providing significant assistance in the dynamic process simulation and operational monitoring of the motorized spindle.

Research on online identification of surface burnishing tool machining conditions by spindle current signal analysis

The surface burnishing tool (SBT) is optimized for aluminum alloys' surface hardness and surface roughness. The SBT's rolling body wears during machining and reaches a certain threshold where the machining performance decreases. Sandpapers are used to abrade the rolling bodies to simulate surface damage. The preconditioned tools are utilized for machining, and the spindle motor current signals are recorded. The SBT's hardening capacity is weakened when the rolling body's Sa exceeds 0.581, and the finishing capacity is weakened when the Sa exceeds 0.684. The PSO-SVM model accurately identifies the failure point of the SBT's hardening capacity with an accuracy of 96.67%. Another PSO-SVM model accurately identified the failure point of SBT's finishing capacity with an accuracy of 85.83%.

Regression predictive modeling of high-speed motorized spindle using POA-LSTM

With the increasing importance of motorized spindles in high-end machining, the accuracy requirements for motorized spindles have been increasing. Therefore, the accuracy problem caused by thermal deformation of the motor spindle during machining has become a prominent and current topic of interest. The problem of thermal deformation of the spindle is due to the fact that a large amount of heat is accumulated inside the spindle during the high-speed rotation of the electric spindle, which has no timely temperature compensation measures and can only rely on the conditions of heat conduction, heat convection and heat radiation to transfer the heat to the outside world, which leads to the thermal expansion of the spindle. Aiming at this problem, this paper takes the A02 electric spindle as the research object, and collects and analyzes the temperature information of the current temperature rise of the key parts of the electric spindle and the thermal elongation of the spindle by constructing the experimental platform for thermal simulation and analysis; and then through the COMSOL steady-state simulation cloud diagram combined with the K-means clustering and gray correlation analysis, it filters out the four temperature points with the highest correlation degree of the temperature data, and constructs the POA-LSTM thermal error model to predict the thermal elongation of the spindle at different rotational speeds, and compared the accuracy of SSA-LSTM and LSTM thermal error models, at a high rotational speed of 10,000 r/min, the accuracies of Early LSTM, SSA-LSTM, and LSTM were 97.44 %, 90.27 %, and 86.66 %, respectively, and the model accuracy of POA-LSTM was about 98.257 %; thus the POA-LSTM thermal error model has high prediction accuracy and robustness.

Notice of Retraction: Reliability Sensitivity Analysis for Dynamic Stiffness of Motor Spindles in a Machining Center

Notice of Retraction: Reliability Sensitivity Analysis for Dynamic Stiffness of Motor Spindles in a Machining Center

Nanocrystalline alternative rare earth-iron-boron permanent magnets without Nd, Pr, Tb and Dy: A review

The continuous increase in the output of Nd-Fe-B permanent magnets leads to the increasing consumption of rare earth (RE) metals of Nd, Pr, Tb and Dy. As a result, a serious unbalanced utilization of RE resource occurs. To reduce the use of these critical REs, high abundance REs of Y, La, Ce and even Gd and Ho have been proposed to partly substitute Pr and Nd in sintered magnets and rapid-quenched nanocrystalline magnetic powders. Recently, the cost-effective RE-Fe-B magnets without Nd, Pr, Tb and Dy have also been developed. This type of magnet is preliminarily designed to bridge the magnetic properties gap between hard ferrites and bonded Nd-Fe-B magnets, which shows the potential applications in voice coil motor, spindle motors and magnetic resonance imaging systems. This review summarizes the progress of RE-Fe-B-type magnets containing no Nd, Pr, Dy and Tb based on the world-wide investigations and our recent findings. The fundamentals of intrinsic magnetic properties of Y, La, Ce based RE2Fe14B phases are introduced first. The relationship between magnetic properties and microstructure of rapidly quenched and bulk RE-Fe-B magnets with nanocrystalline structure is then emphatically discussed. Hard magnetic properties and performance/cost ratio of various RE-Fe-B alloy systems are summarized and compared. Furthermore, the current discoveries and the existing challenges are discussed. Some potential strategies for the future research in this field are finally proposed.

OPTIMIZATION EXPERIMENTAL STUDY OF MACHINING ENERGY CONSUMPTION OF ZIG-ZAG MILLING CUTTING PATH USING RSM.

Machining operations in CNC milling which remove the work material require power and energy to activate the machine components such as spindle motor, table and tool movement in order to withstand the high friction and load between tool and work material. Energy consumption during cutting operation is greatly influenced by the machining condition and parameters. This experimental research aims to investigate how energy responds to changes in the machining parameters such as depth of cut, spindle speed, and feed rate during face milling operation of CNC machine. The high-speed steel (HSS) tool with a 10mm diameter was used to face mill the 40mm x 40mm of Aluminum 6061. The design of experiment technique using Response Surface Methodology (RSM) is utilized to optimize the experimental work. Power usage and machining time were recorded for each machining process, which is then used to determine the machining energy consumption. The interaction between machining parameter and energy is comprehensively visualized using surface and contour plot. Additionally, the ANOVA analysis investigates the feed rate as the most influential parameter to the machining energy. Finally, the regression equation of machining energy is generated with reliability (R) value of 0.88 which can be used as an energy prediction model.

Identifikasi Aliran Energi Listrik pada Mesin CNC Machining Center

<p><em>CNC machine tools have been widely used in the process and manufacturing industries, which have the potential to increase the intensity of electrical energy consumption so that energy efficiency is needed. Energy efficiency is a necessity for the sustainability of the process and manufacturing industrial sector which will be related to energy costs and environmental factors. In order to carry out energy efficiency, it is necessary to identify the consumption of electrical energy by tracing and mapping the electrical energy flow to the components in a CNC machining center machine, so opportunities for energy savings can be known. This research was conducted by collecting data by monitoring energy consumption in stand-by, setting and cutting machine conditions. Cutting tests are carried out to determine the energy consumption of each machine component that affects energy use. After that it can be seen the energy flow pattern by classifying the energy user components into primary and secondary components. Based on the analysis of electrical energy consumption data during the cutting process, it is known that the significat energy users are the spindle motor, the axis feed motor (X, Y, Z) and the coolant motor.</em></p>

Digital process control of multi-step assays on centrifugal platforms using high-low-high rotational-pulse triggered valving.

Due to their capability for comprehensive sample-to-answer automation, the interest in centrifugal microfluidic systems has greatly increased in industry and academia over the last quarter century. The main applications of these "Lab-on-a-Disc" (LoaD) platforms are in decentralised bioanalytical point-of-use / point-of-care testing. Due to the unidirectional and omnipresent nature of the centrifugal force, advanced flow control is key to coordinate multi-step / multi-reagent assay formats on the LoaD. Formerly, flow control was often achieved by capillary burst valves which require gradual increments of the spin speed of the system-innate spindle motor. Recent advanced introduced a flow control scheme called 'rotational pulse actuated valves'. In these valves the sequence of valve actuation is determined by the architecture of the disc while actuation is triggered by freely programmable upward spike (i.e. Low-High-Low (LHL)) in the rotational frequency. This paradigm shift from conventional 'analogue' burst valves to 'digital' pulsing significantly increases the number of sequential while also improving the overall robustness of flow control. In this work, we expand on these LHL valves by introducing High-Low-High (HLH) pulse-actuated (PA) valving which are actuated by 'downward' spike in the disc spin-rate. These HLH valves are particularly useful for high spin-rate operations such as centrifugation of blood. We introduce two different HLH architectures and then combine the most promising with LHL valves to implement the time-dependent liquid handling protocol underlying a common liver function test panel.

Temperature Control Performance of a Built-In Motor Spindle by Developed Temperature Feedback Control System

The temperature control performance of a developed temperature feedback control system was experimentally investigated. The control system was based on a real-time temperature control of a cooling fluid. In particular, this study focused on the temperature control performance of a built-in motor spindle that used the developed temperature fedback control system. The built-in motor used in the study had water cooling jackets. The temperature of the built-in motor spindle was measured and feedback into the developed temperature feedback control system. Temperature control accuracy of the built-in motor spindle under steady state was then assessed. Furthermore, the effects of the time-variant changes in spindle rotation and ambient temperature on the performance of the temperature control system was investigated. The results of the experiments show that the temperature control accuracy of the built-in motor spindle under steady state condition was ±0.03°C. The temperature control performance of the built-in motor spindle under changes in the rotational speed of the spindle was examined. The experimental results show that the temperature change of the spindle could be suppressed to a maximum of approximately 0.3°C under transient state during sudden change in spindle speed. In addition, the effects of the changes in ambient and cooling water temperatures, which simulated actual environmental operating conditions, on the spindle temperature were investigated. The results show that the change in the spindle temperature could be suppressed by approximately less than ±0.1°C. These experimental results indicate that the developed temperature feedback control system achieved high temperature control accuracy and high response for the built-in motor spindle. In particular, the developed control system successfully controlled the time-variant change in the generated heat, thereby improving the thermal stability of the machine tool spindle.

Enhancements to the Work Ability of a High-Speed Motor Used in Machine Tools

This paper presents a strategy for maximizing the effectiveness of high-speed spindle motors in manufacturing equipment. Using the ideal design of the stator winding geometry, as calculated by the Taguchi method, we were able to increase the maximum torque and efficiency. The rotor slot geometry was optimized using a response surface to raise the starting torque and reduce the starting current. The objectives of this investigation were to come up with a strategy for designing high-speed spindle motors so that they have better performance in terms of starting torque, maximum torque, efficiency, and starting current. Spindle motor temperature rise was the focus of the thermal measurements. Finally, a working spindle motor prototype was built and tested to verify the results of the Finite element analysis (FEA). The validity of the suggested model is demonstrated by a comparison between experimental measurements and finite element findings. The total percentage of mistakes was close to 2%.

Analysis and Elimination of Speed Measurement Ripple in Sinusoidal Coded Gearwheel Encoder for Spindle Motor Drive

The gearwheel encoder with a magnetoresistive (MR) sensor is robust, simple, and widely used in high-speed industrial motor drives. Moreover, a high-precision rotor position detection is possible with the gearwheel encoder due to its large number of output pulses per revolution. The MR sensor outputs two sinusoidal signals that are 90 degrees apart, the period of which matches with one tooth of the gearwheel. However, these output signals are not perfectly sinusoidal because the MR sensor is manually mounted, and the magnitudes of these signals depend on the distance between the MR material and gearwheel. This slight imperfection in the output signals provokes large speed ripples in the speed measurement. This study analyzed the speed measurement ripples, and an effective ripple-elimination method was devised based on the results of the analysis. Ripple components caused by each signal error were calculated from the error signal models, and exact ripple frequencies were extracted. From the calculation results, a frequency adaptive series notch filter algorithm was designed. The simulation and experimental results demonstrated the effectiveness of the analysis and proposed approach. It is obviously shown through the experimental results that the speed measurement ripple is almost eliminated with the proposed method, and, thus, the analysis is valid.

Motor Current-Based Degradation Modeling for Tool Wear Hybrid Prognostics in Turning Process

For many machines with turning process systems, the application of economical indirect Tool Condition Monitoring (TCM) is enhanced by utilizing internal encoder spindle motor current signals. In this study, we proposed a novel approach to extract the total harmonic distortion (THD) feature associated with the metal cutting frequency of a specific working tool in the time domain. Our method entailed the application of filtered variational mode decomposition (VMD) combined with envelope analysis to demodulate the motor current signal and define TCM features based on the THD of odd harmonics, which are more related to the motor structure. These features serve as inputs for a hybrid prognostics technique, employing the Geometric Brownian Motion (GBM) to stochastically model the degradation process along with a deep learning transformer-based framework called the time series Transformer (TST) to improve the life prediction. Finally, to validate our approach, we conducted experiments based on 36 sets of tool run-to-wear data extracted from a CNC machine operating under turning process conditions using two different tools. Finally, we compared the degradation models based on the extracted odd-THD and even-THD features.