Spindle Motor Research Articles

MSRCN: A cross-machine diagnosis method for the CNC spindle motors with compound faults

The cross-machine diagnosis of CNC spindle motors with compound faults is essential and challenging because of the subsystem coupling and individual difference. This paper proposed an in-situ fault diagnosis method for cross machine-level individual diagnosis. Plug-and-play modules are specifically designed inspired by signal processing theory, and are embedded into mainstream CNN-based models as an effective industrial diagnostic model, the multiscale spatial–temporal residual capsule neural networks (MSRCN). The internal mechanism of these new modules is explored through ablation experiments and visualization on real industrial motor signals, which shows MSRCN-based models can enrich the multi-scale feature extraction capabilities and benefits the interference resistance of individual related features. In addition, new evaluation operators for degree of confidence are proposed to comprehensively evaluate the performance of deep learning in classification tasks and the reliability of the decision-making.

Adaptive backstepping dynamic sliding mode control for maglev turning motor spindle based on limited time preset performance

In the process of turning a maglev motorized spindle, there are problems such as system model time-varying, cross-coupling of control parameters, difficult measurement of system state variables, and nonlinear characteristics of active magnetic bearings, which lead to the inevitable cutting chatter phenomenon, difficult control algorithm design, and then the reduction of workpiece surface quality and accuracy, affecting machining efficiency and tool life. In this paper, the “multimodal-distributed parameter” model is extended to the “magnetic bearing-rotor-workpiece” variable mass turning process. An adaptive backstepping fast dynamic terminal sliding mode control is designed to address the model’s time-varying parameters and cross-coupling issues. In view of the difficulty in measuring the vibration displacement at the cutting point, the displacement field reconstruction method was introduced to reconstruct the vibration displacement field online and provide effective feedback for the previously designed control strategy. Finally, the proposed controller is applied to adjust and control the turning process of a maglev motorized spindle and compared with other advanced controllers. The simulation results show that the proposed control method has a better control effect than other control methods in the presence of unmodeled dynamics, uncertainties, and external disturbances.

THE CONCEPT OF THE STEP-BY-STUDY EVOLUTION OF SPINDLE MOTORS USING VENN-EULER DIAGRAMS

The evolution of creation and improvement of rotary drives of the main movement with their translational movement, beginning with aggregate machines and finishing with modern intelligent motor spindles for CNC machines is traced. Symbols for the circuit image of motor spindles are offered. Graphical representations of motor-spindle hybrids of different level of crossing using VennEuler diagrams are presented.

Case study of vibration in a mobile milling machine

Many industries rely on heavy-duty machinery such as cranes, pipelines, motors, which are subject to wear and tear. Milling of such parts using conventional machine tools presents many drawbacks and is impossible in some cases. In such situations, mobile machine tools can be used to machine directly on site. However, mobile machines are flexible, and are prone to detrimental vibrations during cutting operations. Vibrational stability in the surfacing process has been extensively studied in conventional milling. However, the focus of this article is an original study on a mobile milling machine used for on-site surfacing operations. An experimental investigation into the vibrational behavior of the machine is carried out and a sensorless method is used to detect the onset of chatter. Machine flexibility is discussed using a tool-point frequency response function and references from the literature. Milling operations are carried out over a range machining parameter to determine an experimental stability limit. Internal sensors for spindle motor consumption and spindle motor encoder are monitored during the machining process. A threshold is established for determining the appearance of chatter using only the machine’s internal sensors.

An adaptive morphological filtering and feature enhancement method for spindle motor bearing fault diagnosis

The unbalanced electromagnetic force and unqualified assembly of the spindle motor lead to weak vibration energy and the difference in fault features in different life cycles and different bearing individuals. Compared with single fault diagnosis and the datasets that come from the test bench, compound faults diagnosis of the spindle motor is a challenging task. To solve it, an improved filtering and feature enhancement method combined with the merits of AMF and TEO is proposed. Firstly, the adaptive morphological filtering (AMF) method is developed by adaptively constructing the size of the structural element (SE) for remaining in corresponding SE with the component of interest, which can reduce interference from background noise. Still, the periodic fault impulse, especially the incipient fault signals, is easily modulated by other fault-unrelated harmonic components. Thus, the filtered signals are processed by the Teager energy operator (TEO) for enhancing the faint transient impact compositions. Finally, the effectiveness of the method is verified by simulation and fault motor matched with NTN ceramic bearing and FAG metal bearing respectively, where the motor matched with NTN ceramic bearing is an early failure case and the other is a late failure case. Besides, compared with some traditional methods, the result proves that the proposed method has better performance under the actual engineering scenarios for different degrees of fault feature identification.

Transforming acidic coiled-coil containing protein 3 suppresses influenza A virus replication by impeding viral endosomal trafficking and nuclear import

Transforming acidic coiled-coil containing protein 3 (TACC3) is a motor spindle protein that plays an essential role in stabilization of the mitotic spindle. In this study, we show that the overexpression of TACC3 reduces the viral titers of multiple influenza A viruses (IAVs). In contrast, the downregulation of TACC3 increases IAVs propagation. Next, we map the target steps of TACC3 requirement to the early stages of viral replication. By confocal microscopy and nuclear plasma separation experiment, we reveal that overexpression of TACC3 results in a substantial decrease of IAV NP accumulation in the nuclei of infected cells. We further show that viral attachment and internalization are not affected by TACC3 overexpression and detect that the early and late endosomal trafficking of IAV in TACC3 overexpression cells is slower than negative control cells. These results suggest that TACC3 exerts an impaired effect on the endosomal trafficking and nuclear import of vRNP, thereby negatively regulating IAV replication. Moreover, the infection of different IAV subtypes decreases the expression level of TACC3 in turn. Consequently, we speculate that IAV ensures the generation of offspring virions by antagonizing the expression of inhibitory factor TACC3. Collectively, our results establish TACC3 as an important inhibitory factor for replication of the IAV, suggesting that TACC3 could be a potential target for the development of future antiviral compounds.

МОДЕЛИРОВАНИЕ СОБСТВЕННЫХ ЧАСТОТ И АМПЛИТУДЫ ВЫНУЖДЕННЫХ КОЛЕБАНИЙ ВЕРТИКАЛЬНО-ФРЕЗЕРНОГО СТАНКА

The article demonstrate the technique for modeling the form of natural oscillations, as well as the amplitude and mode of forced oscillations of the HAAS VF-1 vertical milling machine. As initial data, a publicly available 3D model of load-bearing elements, information on the mass and dimensions of the spindle motor are used. In preparation for the simulation, the original solid model is idealized by removing small chamfers, fillets, and small diameter holes. Movable joints with linear rolling guides and ball bearings are replaced by rigid joints, since vibration damping in these units is not considered. During the first simulation, ten natural frequencies with the largest values of the oscillation amplitude are obtained. The second simulation shows, the influence of the spindle drive layout (coaxial installation of the electric motor with the spindle through a cam clutch or connection using a toothed belt transmission) and additional devices for dissipating vibration energy (mass and structural dampers in the form of structural elements articulated through plastic bushings) is revealed by the magnitude of forced steady-state oscillations that may occur during the processing of the part.

A signal-filtering and feature-enhancement method based on ensemble local mean decomposition and adaptive morphological filtering

The bearing fault signals from the spindle motors of computer numerical control machines are complex and non-linear due to being coupled to multiple subsystems. The complexity of industrial signals, with increased industrial noise, and the difference in fault features in different life cycles and different individual signals bring great challenges for fault feature extraction. In this paper, a signal-filtering and feature-enhancement method based on an ensemble local mean decomposition and adaptive morphological filtering (ELMD-AMF) method is proposed. First, the original vibration signal of the bearing is reconstructed by ELMD to reducing interference from background noise. Next, an improved feature-enhancement process based on AMF is constructed, a particle swarm optimization with maximum-weighted spectral kurtosis as an optimization objective is used to adaptively construct the size of the structural element, and a morphology hat product operator one is adapted to extract the periodic impulse features. Finally, the effectiveness of the method is proved by using an actual three-phase induction motor matched with an NTN ceramic bearing and a FAG metal bearing, respectively. Further, compared with minimum entropy deconvolution and fast kurtogram methods, the result proves that the proposed method has better performance for both early-failure and late-failure scenarios under real-world engineering conditions.

Speed estimation of the spindle motor of the grinding head for a high-speed precision roll grinder based on the extended Kalman filter

ABSTRACT Since installing speed sensors on the grinding head for high-speed precision roll grinders is not feasible, it is necessary to use direct torque control (DTC) without speed sensors, and its control system requires high estimation accuracy. In this paper, speed estimation methods using extended Kalman filter (EKF) and model reference adaptive system (MRAS) have been used to realise speed estimation in the DTC system of the spindle motor of the grinding head for a high-speed precision roll grinder. The simulation results showed that the EKF method has relatively high accuracy and a fast convergence speed for the estimation speed of the spindle motor of the grinding head for a high-speed precision roll grinder by comparison with the MRAS method. Moreover, the EKF method was also able to overcome the nonlinear and parameter uncertainty of the operating system of the motor and strongly immune to the noise interference of the system. Finally, the speed estimation accuracy of the motor using the EKF method had better than the MRAS method. Therefore, this work provides a general approach to applying the DTC without speed sensors for the motor and further lays a solid foundation for developing high-speed precision roll grinders.

Plant pathogen detection on a lab-on-a-disc using solid-phase extraction and isothermal nucleic acid amplification enabled by digital pulse-actuated dissolvable film valves

By virtue of its ruggedness, portability, rapid processing times, and ease-of-use, academic and commercial interest in centrifugal microfluidic systems has soared over the last decade. A key advantage of the LoaD platform is the ability to automate laboratory unit operations (LUOs) (mixing, metering, washing etc.) to support direct translation of ‘on-bench’ assays to ‘on-chip’. Additionally, the LoaD requires just a low-cost spindle motor rather than specialized and expensive microfluidic pumps. Furthermore, when flow control (valves) is implemented through purely rotational changes in this same spindle motor (rather than using additional support instrumentation), the LoaD offers the potential to be a truly portable, low-cost and accessible platform. Current rotationally controlled valves are typically opened by sequentially increasing the disc spin-rate to a specific opening frequency. However, due lack of manufacturing fidelity these specific opening frequencies are better described as spin frequency ‘bands’. With low-cost motors typically having a maximum spin-rate of 6000 rpm (100 Hz), using this ‘analogue’ approach places a limitation on the number of valves, which can be serially actuated thus limiting the number of LUOs that can be automated. In this work, a novel flow control scheme is presented where the sequence of valve actuation is determined by architecture of the disc while its timing is governed by freely programmable ‘digital’ pulses in its spin profile. This paradigm shift to ‘digital’ flow control enables automation of multi-step assays with high reliability, with full temporal control, and with the number of LUOs theoretically only limited by available space on the disc. We first describe the operational principle of these valves followed by a demonstration of the capability of these valves to automate complex assays by screening tomato leaf samples against plant pathogens. Reagents and lysed sample are loaded on-disc and then, in a fully autonomous fashion using only spindle-motor control, the complete assay is automated. Amplification and fluorescent acquisition take place on a custom spin-stand enabling the generation of real-time LAMP amplification curves using custom software. To prevent environmental contamination, the entire discs are sealed from atmosphere following loading with internal venting channels permitting easy movement of liquids about the disc. The disc was successfully used to detect the presence of thermally inactivated Clavibacter michiganensis. Michiganensis (CMM) bacterial pathogen on tomato leaf samples.

Study on milling tool wear morphology and mechanism during machining superalloy GH4169 with PVD-TiAlN coated carbide tool

Tool life, dimension accuracy and surface quality of workpiece, intelligent operation and maintenance of automated production are closely related to tool wear. However, for many years, relevant scholars have mainly focused on the research of the tool wear mechanism in the sharp wear stage, mainly involving various machining methods, various superalloy materials, various tool materials and geometric parameters, and different coolant methods et, al. The morphology and mechanism of tool wear need to be further studied in the initial and steady wear stage, which is helpful to ensure the dimensional accuracy and surface quality of workpiece, especially in the finishing stage. 45 groups of tool wear experiments are carried out under dry condition during milling of GH4169 with PVD-TiAlN coated carbide tools. The tool wear micro-topography, tool wear curve, tool cutting force, spindle motor current, tool wear mechanism, chip topography and machined surface topography were investigated during the milling tool wear. The tool wear mechanism in the sharp wear stage was further explored by SEM, EDS and XRD, which will help to deeply gain the milling characteristics of GH4169 and help to test the cutting performance of the tool. The results show that during the tool wear process of milling superalloy GH4169, severe abrasive wear and adhesive wear occur in large numbers, the rate of tool wear is fast, the cutting force increases, the current of the spindle motor increases, and the tool coating and substrate material were worn seriously, the main tool wear mechanisms are abrasive wear, adhesive wear, oxidative wear and breakage. Severe abrasive wear occurs between lots of hard abrasive carbides in the workpiece material and the rake and flank faces of the milling tool. Fe, Cr, Al, etc. were found on the rake and flank faces, and the adhesive wear was very serious. Many oxides (Al2O3, Fe2O3, etc.) appear on the rake and flank surfaces, which indicate that oxidative wear occurs. Due to the periodic intermittent cutting during the milling process, the breakage of milling tool edge is very serious. Chips or adherents with different microscopic topographies were adhered to the tool wear area due to extrusion and stretching in the sharp wear stage. The machined surface micro-topography of the workpiece, which affected by tool wear, was greatly of difference during the entire wear stage.

Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing

Abstract The predictive methods of tool wear are usually based on different algorithm predictors, different source data, and different sensing devices for remaining useful life (RUL). In general, it has challenges to model and ensure all of the cutting conditions that are suitable in the actual cutting process for sustainable manufacturing. In order to overcome the doing large amount of experimental data and predict different tool RULs, this study combines the analytical force modeling, the back-propagation neural network (BPNN) machine learning, and the current sensor which all are integrated in smart machine box (SMB) to realize the practical RUL prediction for on-line and real-time applications. The analytical model of the cutting force coefficients of shear and ploughing was established from average cutting forces, it could reduce the experimental number and predict the different cutting conditions. In general, the loading current of the cutting tool from a spindle motor is relatively easier acquired than the resultant forces. Thus, the loading currents of the spindle are used to train and predict the cutting forces using the BPNN model during intelligent manufacturing. The SMB architecture mainly performed the autonomous actions based on the edge layer, the fog layer, and the cloud layer via the TCP/IP, the MQTT protocol, and the unified communication library. Results showed that a predictive error for the ends of the tool life is about 3–10% that are based on the calculating of the cumulative current ratio.

In-situ fault detection for the spindle motor of CNC machines via multi-stage residual fusion convolution neural networks

The faults from the spindle motor of CNC machines result in excessive vibration affecting the manufacturing quality. In-situ signals of intact motors are complex and nonlinear due to coupling of multiple subsystems. The manufacturing and assembly errors lead to individual differences resulting in more challenging fault detection of motor systems compared with detection of disassembly parts in an experimental environment. In this paper, an in-situ fault diagnosis method via the multi-stage residual fusion convolution neural networks (MSRFCNN) model is proposed. The MSRFCNN with excellent generalization is specially designed for the cross-individual detection of the motor systems, which can extract comprehensive individual-irrelevant features from in-situ multi-channel signals with industrial noise. Integrated experiments are performed on real industrial motor datasets for assessing and analyzing the effectiveness of the proposed method. The experiment results show that the MSRFCNN is superior to classical and some state-of-the-art DL methods.

Thermal-mechanical dynamic interaction in high-speed motorized spindle considering nonlinear vibration

The neglection of thermal-mechanical interaction of the high-speed motorized spindle system may lead to modeling errors of thermal and dynamic characteristics. In this work, the thermal-mechanical interaction mechanism is analyzed, and a closed-loop iterative modeling method for thermal and dynamical characteristics is proposed to improve the modeling accuracy. The nonlinear dynamic model with thermal effects of the spindle system under unbalanced magnetic pull and unbalanced mass excitation is established to evaluate its transient dynamic behavior. A multi-node thermal resistance network model of the motorized spindle system considering the nonlinear vibration of the spindle system is established to evaluate transient thermal behavior. Based on the load-displacement relationship, the nonlinear stiffness and restoring force model of bearing considering thermal effects and nonlinear vibration is established. The heat generation of the bearing and the built-in motor are calculated by Palmgren's formula and the equivalent circuit model of the induction motor, respectively. The bearing heating is improved considering the comprehensive effects of lubricant viscosity change, bearing thermal expansion, and nonlinear dynamic load. The mechanical power loss of the motorized spindle is analyzed to calculate the electrical parameters required to study the spindle dynamics and motor heat generation. The accuracy and effectiveness of the proposed theoretical method are experimentally verified. Numerical simulations show that the thermal and mechanical effects in the motorized spindle system exhibit a non-negligible dynamic interaction.

Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning

Towards industry 4.0, monitoring the degradation of machine tools’ components becomes a key feature so that smooth productivity is achieved. To preserve the functionality and performance of the machine tools, proper maintenance activities must be planned and carried out. V-belt is important component in machine tools that transmits power from the electric motor spindle in order to machine to work and cut desired material properly. The purpose of this research is to develop a predictive maintenance system for v-belt milling machine Krisbow 31N2F using machine learning. The machine learning algorithm models using multiple and simple linear regression algorithm was developed in an open-source program. The test results show that the machine learning model has a high accuracy value in both the training data and the testing data. The multiple linear regression model has MSE value of 5.8830x10-6 and MAE value of 0.002. The Simple linear regression model has an MSE value of 0.0004x10-6 and MAE value of 0.162. The results shows that the use of the linear regression algorithm as a support for determining the prediction of RUL v-belt milling machine model 31N2F (BS) is successfully carried out.

Flow characteristic analysis of a herringbone groove thrust bearing with shaft misalignment

PurposeHerringbone groove thrust bearings are typically used in high-speed, light-load applications, such as spindle motors for hard disk drives. In the past researches, the effect of shaft misalignment was little considered. This study aims to reveal effects of shaft misalignment on the microscopic flow regime in the water-lubricated herringbone groove thrust bearing.Design/methodology/approachThe liquid film in a thrust herringbone groove bearing was investigated by computational fluid dynamics. The effects of micro-grooves on the flow field were carefully explored. Two-dimensional liquid films at four different sites were examined for obtaining the rich flow field properties.FindingsThe distributions of pressure, temperature and water vapor volume fraction were obtained, the micro hydrodynamic effects were formed by the herringbone grooves and the effects of the shaft misalignment on lubrication and sealing performance could be found.Originality/valueThe influence of misalignment on the herringbone groove thrust bearing performance was investigated in detail. The obtained results could give the reference guideline for the bearing design.

Deep transfer residual variational autoencoder with multi-sensors fusion for tool condition monitoring in impeller machining

The data-driven deep learning models show incomparable superiority in tool wear monitoring. However, facing the monitoring environment of real-time changes in tool model, workpiece size, and cutting parameters in the machining of centrifugal compressor impeller, the flexibility of the model between different tools is rarely considered. In this manuscript, a novel deep transfer learning model based on residual variation autoencoder (Tresvae) with multi-sensors fusion signals is proposed. The sound pressure signals, acceleration signals, and spindle motor current signals are collected and confused into images as monitoring samples. Moreover, the residual network is used to optimize the encoding part of the variational autoencoder network while the decoding part remain unchanged. Then the parameter transfer strategy is adopted to realize the model transfer diagnosis between different tools. Experiments show that the proposed model achieves superior identification results than other methods on the same types and different types tools monitoring in impeller machining. Among them, the maximum recognition accuracy of the same type of tools is 97.25%, and that of different types of tools is 92%.

Research on Adaptive Control of Grinding Force for Carbide Indexable Inserts Grinding Process Based on Spindle Motor Power

The grinding force is the most sensitive physical measure of reaction loads in the grinding process. To enhance surface quality and assure high efficiency and stability of the grinding process, it is essential to accomplish adaptive control of the grinding force. This paper suggests a grinding force adaptive control system based on spindle motor power feedback, considering the process–machine interaction. The spindle motor power is utilized as a proxy for the grinding force because of the mapping relationship between the two variables. The machine tool’s feed rate is automatically modified to achieve adaptive control of the grinding force, after assessing the discrepancy between the collected spindle motor power and the preset power. Finally, a cemented carbide tool grinding experiment was performed on a 2MZK7150 CNC tool grinder. During the experiment, the grinding force was precisely controlled between 80 and 100 N, ensuring machining quality and increasing machining efficiency. The experimental results show that the adaptive control system can meet the high-efficiency and high-quality machining requirements of cemented carbide rotary blades.

Fuzzy Based Tool Wear Monitoring of the CNC Milling Machine

In machining systems, cutting tool wear causes errors in precision manufacturing processes. It causes a waste of raw material processed in faulty production and a waste of time spent in vain. Continuous monitoring of tool wear and generating an automatic warning in case the wear value falls outside the tolerance value will resolve these issues. Vibration values and the powers drawn by the motors provide important clues in the non-contact monitoring of cutting tool wear during production. In this study, thanks to the use of low-cost sensors and the applied fuzzy decision mechanism , the cutting tool status could be detected online with an accuracy of 90.17 percent. The RMS value of the power drawn by the spindle motor, average value of fiber optic sensor output voltage, and the average values of selected fiber optic sensor output wavelet transformations are the inputs of the designed system. The output of the system is the cutting tool wear value estimated by the fuzzy decision mechanism.

Performance Analysis of Automated Air Pollution Removable System with Fire Indicator

Air pollution and aging populations are one of the most important issues facing in the development of all countries around the world. In this project work and effort has been made to develop a air pollution removable and fire indicated for fire work industry to safety purpose. The aim of this work is to develop a fire indicated and pollution removable that will use a collect fire work chemical powder around the working area. This model implies a conversion of rotary motion to the overall system. This motion is uses for chemical dust arrest by viscous liquid to all direction in the industry. In this model we can operate two pin switches for the clock wise and antilock wise direction. the gear motor is using to rotary motion. The rotary disk is mounted on the motor spindle. The tank is resting on the rotary disk. The exhaust fan is fitted in the top position of the tank. They are two way in the tank. One is inlet for suck the pollution air another one is outlet. Bottom of the tank dust despatch valve attached. The fire indicates system is mounted on the rotary disk plate vertically. After this work performance test to the working area. This research provides a new perspective and the health effect of air pollution.