Spindle System Research Articles

Nonlinear dynamic analysis of spindle system considering thermal-solid coupling

Nonlinear dynamic analysis of spindle system considering thermal-solid coupling

Analysis of vibration characteristics of ship propeller spindle

In order to obtain the vibration response characteristics of the propeller spindle system effectively and accurately, and provide the basis for the subsequent fault diagnosis, the modal simulation and test of the spindle model were carried out. With the propeller set as eccentric mass, the amplitude and torsion angle of the spindle model were simulated under the condition of excited vibration and unexcited vibration respectively, and the frequency response of bending and torsion under the coupled condition was obtained. The newmark-β method was used to solve the transient response of the bent-torsional coupling model. The results shows that when the propeller spindle system undergoes rotation at a specific frequency and experiences bending vibration excitation force, the latter will induce torsional vibration response. Moreover, the amplitude of the torsional vibration response varies with changes in the frequency of the bending vibration excitation force.

High-speed grinding: from mechanism to machine tool

AbstractHigh-speed grinding (HSG) is an advanced technology for precision machining of difficult-to-cut materials in aerospace and other fields, which could solve surface burns, defects and improve surface integrity by increasing the linear speed of the grinding wheel. The advantages of HSG have been preliminarily confirmed and the equipment has been built for experimental research, which can achieve a high grinding speed of more than 300 m/s. However, it is not yet widely used in manufacturing due to the insufficient understanding on material removal mechanism and characteristics of HSG machine tool. To fill this gap, this paper provides a comprehensive overview of HSG technologies. A new direction for adding auxiliary process in HSG is proposed. Firstly, the combined influence law of strain hardening, strain rate intensification, and thermal softening effects on material removal mechanism was revealed, and models of material removal strain rate, grinding force and grinding temperature were summarized. Secondly, the constitutive models under high strain rate boundaries were summarized by considering various properties of material and grinding parameters. Thirdly, the change law of material removal mechanism of HSG was revealed when the thermodynamic boundary conditions changed, by introducing lubrication conditions such as minimum quantity lubrication (MQL), nano-lubricant minimum quantity lubrication (NMQL) and cryogenic air (CA). Finally, the mechanical and dynamic characteristics of the key components of HSG machine tool were summarized, including main body, grinding wheel, spindle and dynamic balance system. Based on the content summarized in this paper, the prospect of HSG is put forward. This study establishes a solid foundation for future developments in the field and points to promising directions for further exploration.

Study on the 2D equivalent nonlinear dynamics simulation model related to high speed precision bearing and spindle

The stiffness and contact stress of the bearing spindle system are the key factors affecting its machining accuracy and service life under the condition of high-speed service, which will be affected by different structural parameters and service conditions. It is essential to establish an efficient and accurate computational simulation model to understand the influence of complex factors on the nonlinear dynamic characteristics of the bearing spindle system. Firstly, in the paper, a 2D axisymmetric finite element model of the bearing, aims at the relationship between stiffness and contact stress of the bearing under high-speed service, has been built based on a classical dynamic analysis model and the reversed method of material parameters of equivalent rolling balls. Additionally, for the BT30 spindle, the 2D axisymmetric finite element model of the bearing spindle system also has been built and applied in mechanical analysis of spindle under different conditions of assembly and service, based on the bearing model. The results show that the axial force of bearings decreases as the rotational speed increases, and an augmentation in speed will result in a reduction in the axial stiffness of the BT30 spindle. In addition, the maximum contact stress exhibits a slight decline as the rotational speed increases. Furthermore, with an escalating preload, the stiffness and contact stress of the spindle undergo substantial increments, however, these parameters will cease to alter once a certain threshold is reached.

Dynamic modeling and vibration analysis for motorized spindle with uncertainties

Abstract Uncertainties inevitably exist in motorized spindle and it is difficult to accurately describe its dynamics based on a deterministic model. In this paper, a dynamic model of motorized spindle with uncertainty is proposed. The deterministic dynamic model of the motorized spindle rotor system is built by the finite element method, and the uncertainties are constructed by the polynomial chaotic expansion (PCE) method. The impact of each uncertainty parameter on the critical speed of the system is investigated by adopting the Sobol global sensitivity analysis method. The effects of uncertainties including the bearing stiffness, the material Young’s modulus, the disc mass and the unbalanced excitation on the vibration response of motorized spindle are analyzed. The modal experiments are carried out to verify the effectiveness of the model. The results show that the uncertainties have a great influence on the vibration characteristics of the motorized spindle system, in which the bearing stiffness mainly affects the high-order frequency response of the system, while the Young’s modulus affects all the order frequency response of the system. The influence of hybrid uncertainties on the frequency response of the system is more complex. This study provides a guidance to the design of motorized spindle.

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.

An integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools

Thermal error significantly influences the accuracy of precision computer numerical control (CNC) machine tools. The key to compensating thermal error lies in selecting appropriate temperature measurement points and establishing an accurate error prediction model. Traditional methods separate measurement points selection and prediction modeling, that is, selecting temperature measurement points first and then establishing the prediction model using these points. These methods are difficult to achieve optimal matching between measurement points and the prediction model, resulting in shortcomings in modeling accuracy. To address these challenges, an integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools is proposed. A dual-stage attention-based long short-term memory combined with convolutional neural network (DA-CLSTM) error modeling method is proposed to accurately predict the thermal error for different numbers of temperature measurement points. Then, an integrated method of virtual-real temperature measurement points layout and error modeling is proposed, which ensures the temperature measurement points and the error prediction model are closely matched, offering rational sensors layout and high-accuracy prediction. Finally, experiments are conducted on the spindle system test bench to validate the method proposed in this research. Through integrated optimization of measurement points layout and the prediction model, the proposed method achieves high-accuracy thermal error prediction across various sensor counts and maintains reliable prediction ability when the number of sensors is reduced. This method is applied to a digital twin system of MKL7150 grinding machine, and effectively improves the accuracy of actual machining.

Digital twin–driven causal diagnosis mechanism for life health of high-speed spindle system

Digital twin–driven causal diagnosis mechanism for life health of high-speed spindle system

Nonlinear dynamic analysis of high-speed precision grinding considering multi-effect coupling

The aerostatic spindle is widely used in the field of precision grinding because of its excellent machining stability and machining accuracy. The spindle is jointly affected by multiple excitation effects in the high-speed grinding process. In order to study the dynamic characteristics of the aerostatic spindle under high-speed precision machining, it is necessary to analyse the nonlinear response of the aerostatic spindle with multi-physical field coupling. In this paper, the nonlinear dynamic model of the aerostatic spindle system under multi-effect coupling is established by considering three aspects: fluid-structure coupling, mass eccentricity excitation coupling, and vibration-grinding force coupling. Firstly, the instantaneous nonlinear air film force excitation is calculated by the derivation of the dynamic air film thickness model with multi-degree-of-freedom vibration responses coupling. The mass eccentricity excitation considering dynamic deflection angle is derived through the eccentric-load relationship. Afterwards, the dynamic precision grinding force model under the full surface of the tool is established based on judging the material removal mode, which considers the grain-workpiece interaction relationship under the influence of the nonlinear vibration. Finally, the accuracy and effectiveness of the coupled model is verified by high-speed precision grinding experiments, and the nonlinear dynamic behaviour under different operating parameters is analysed.

Estimated economic impact of fire blight on long-term orchard economic performance with susceptible and resistant rootstocks

We estimated the economic impact of theoretical fire blight induced tree loss using data from two long-term field trials in NY State. An economic analysis of profitability using Net Present Value (NPV) was conducted with three cultivars, eleven rootstocks and four planting systems. The impact of fire blight was modeled in terms of both the severity of the disease and the year of infection. We considered a range of scenarios with different infection rates (10 %, 50 %, 80 % and 100 %) of all the trees planted and different timings when the infection took place including the 1st year (prior to production), 5th year, 10th year and the 15th year during the life cycle of an orchard. The analysis showed that the smallest impact of fire blight induced losses on lifetime NPV occurred when the fire blight infection and tree death occurred in year 1 and was greatest when infection and tree death occurred in year 10. If the infection occurred in year 15 then the losses in NPV were less. As expected, the analysis showed that a low percentage of tree loss due to fire blight at any given year of the orchard life resulted in a low impact on lifetime NPV while greater levels of infection and tree loss were associated with higher losses in NPV. The use of fire blight resistant rootstocks dramatically reduced the negative impact of fire blight induced tree losses on NPV. With susceptible rootstocks (M.9 and M.26), the reduction in NPV with high levels of tree infection was as high as 70 % which would render the planting unprofitable, while with resistant rootstocks the losses in NPV were lower (<30 %) and the orchard would still be profitable. High-density orchard systems like the Tall Spindle system had less sensitivity to fire blight induced losses than lower density systems. Cultivar also had an important effect on the level of fire blight induced losses of NPV. With a high priced cultivar like ‘Honeycrisp’ the percentage loss in NPV was less than with the lower priced cultivars like ‘Fuji’ and ‘Gala’.

SELF-HEALING MECHANISM BASED ON FAULT MANAGEMENT FOR SMART-GRID DEVELOPMENT ON 20kV SPINDLE NETWORKS TO IMPROVE DISTRIBUTION SYSTEM RELIABILITY AT PLN UID JAKARTA-INDONESIA

This study aim is to study for the development of smart grid with the implementation of network automation in the spindle system to improve reliability, revenue and customer experience in the priority areas of PLN UID Jakarta. The addition of new substations has an impact on the reconfiguration changes and the addition of new networks in the context of loading feeders and causing the topology of the distribution network to be increasingly complex and impure spindle systems, in addition to the jammed condition of Jakarta also has an impact on the longer time in investigating disturbances, as well as the limited investment budget of PLN has not supported the installation of new keypoints in all distribution substations. This research was carried out by system modeling in the SCADA Distribution Management System for N-1 contingency and a simulation of the Self-Healing Mechanism test based on static and dynamic data parameters in real-time according to the Load Flow and Load Forecast in the SCADA systems. Evaluation of technical and financial feasibility as a smart feeder design that will be implemented in the spindle system, on the other hand, it is expected to be a Distribution Grid Management design that is feasible to be implemented in the 20kV Spindle system as an alternative to Zero Down Time to accelerate the recovery time of distribution disruptions and improve reliability performance in controlling the operation of the distribution system at PLN UID Jakarta.

Chatter and Surface Waviness Analysis in Oerlikon Face Hobbing of Spiral Bevel Gears

A vectorized analytical model for the cutting dynamics in the spiral bevel gear face hobbing process has been developed, which is based on machine tool kinematics and vibration vectorization. The structural modal parameters of the cutter head spindle system are obtained through experimental modal analysis with hammer impact testing. The analytical model is utilized to simulate the generation of simulated vibration acceleration signals during spiral bevel gear hobbing. A wavelet threshold denoising method is applied to process the simulated vibration signals of the spiral bevel gear face hobbing with added white noise. Signal processing methods, including short-time Fourier transform are employed for time-domain analysis, frequency-domain analysis, and time–frequency-domain analysis of measured signals and simulated signals, thereby extracting the corresponding statistical features. In addition to the results of the experimental modal analysis, the causes of chatter in spiral bevel gear hobbing are discussed in detail, revealing that the main factor is cutter head vibration in the Y direction of the Hunan ZDCY CNC EQUIPMENT YKA2260 machine tool used in this research. The error in the time-domain characteristic parameters between simulated signals and measured vibration acceleration signals is within 15%, with a difference of 3.5% in spectral peak values. The predicted tooth surface morphology from simulation matches the actual morphology on the workpiece, comprehensively validating the reliability of the cutting dynamics model for the spiral bevel gear face hobbing process. Another conclusion drawn from numerical simulation experiments is that the amount of tooth surface waviness of the spiral bevel gears is the ratio of tool chatter frequency to cutting fundamental frequency.

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.

Multi-verse optimizer for thermal error modeling approach of spindle system based on thermal image

Since the spindle thermal error of CNC machine tools has a significant impact on machining precision, this paper introduces a unique approach for modeling spindle thermal error. Several key steps are involved in the proposed approach. First, the Fluke thermal imaging camera is employed for acquiring thermal image information of the spindle system. Second, the Gaussian filter is employed to denoise the thermal image sequence. Next, the temperature values at the measurement points are extracted from the thermal image sequence according to the mapping relationship between the grayscale value and the temperature value. Subsequently, critical temperature points are identified from thermal images using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the correlation coefficient method. Finally, the multi-verse optimized NARX neural network is employed to investigate the nonlinear prediction of thermal deformation. The research is conducted on the VMC-850E vertical machining center as the subject of study. The performance of the model is validated under conditions of idle spindle and 5000 r/min, comparing prediction results against traditional algorithms. The findings demonstrate that the non-contact measurement method based on thermal imaging successfully establishes the thermal error model, achieving a prediction accuracy of 0.1517 μm for the MVO-NARX model.

Research on thermal characteristics modeling of CNC machine tools based on submodel method

To improve the accuracy of thermal characteristics analysis of CNC machine tools, a modeling method for the thermal characteristics of CNC machine tools based on submodel method is proposed in this paper. In this method, the thermal-structural coupling calculation of the feed system and spindle system is initially completed in ABAQUS to obtain temperature and deformation results of the submodels. Subsequently, User Define Function (UDF) in Fluent is used to extract the temperature information from the surface of the submodels. The obtained surface temperature data is then imported into the overall machine model for fluid-structure coupling heat transfer calculation. The model developed in this study takes into account factors such as the dynamic heat generation of the heat source, the cooling fan system, and the secondary heat source effect of the cutting fluid. To validate the approach, a thermal characteristics experiment was conducted, and the research results demonstrate that the thermal analysis model established in this paper exhibits both high accuracy and efficiency. During the 180 min of machine operation, the thermal displacement between the tool tip and the workpiece increased. The maximum thermal deformation of the machine tool tip was 40.83 [Formula: see text], primarily observed in the X and Y directions.

Efficient boundary conditions identification in thermal simulation of the spindle system with reduced order model and differential evolution algorithm

The boundary conditions in thermal simulation of the spindle system are very complicated and the empirically calculated ones will usually result in a significant discrepancy between experiment and simulation. Thus, the optimal boundary conditions were identified by treating it as an inverse optimization problem in this paper. Moreover, to advance the optimization efficiency, a reduced order model is established to replace the time-consuming thermal simulation of the spindle system. By superposition of truncated eigenmodes and accurately predicted modal coefficients, the reduced order model can reconstruct the field very similar to thermal simulation. Experimental reconstruction of both the temperature field and thermal deformation field by the reduced order model demonstrated its at least 360 times speedup with 99.8 % accuracy compared to the actual thermal simulation. With the accurate reduced order model as a lightweight digital twin and using the differential evolution algorithm, three types of boundary conditions, i.e., the heat generation rates, the convective heat transfer coefficients and the thermal contact resistances, under the shaft rotation speed of 4,000r/min were identified within 11s. The maximum temperature simulation error was reduced from 85.6 % to 6.6 % and the thermal deformation simulation error was reduced from 60.9 % to 10.8 %.

Stability prediction for robotic milling based on tool tip frequency response prediction by considering the interface stiffness of spindle-tool system

The change of tool tip frequency response caused by the posture dependence of robot dynamic is one of the key problems that make it difficult to accurately predict the milling stability of robot. In this paper, a tool tip frequency response prediction method considering the interface stiffness characteristics of spindle-tool system is proposed for stability prediction of robotic milling under the condition of posture variation. Firstly, the interface stiffness models of spindle-toolholder, toolholder-spring clip and spring clip-tool are established based on Yoshimura's unit area method. Then, The dynamics model for the robot body and the interface stiffness models for spindle-tool system are imported into the finite element analysis model of the spindle system, so that the prediction of the tool tip frequency response is realized by harmonic response analysis. Compared with the experimental results, the maximum error of the natural frequency was not more than 2 %, and the maximum error of the amplitude was not more than 12 %. Finally, the 2 DOF robot milling stability prediction model is established. Then the robot milling chatter is predicted considering redundant degrees of freedom from the perspective of regenerative chatter prediction theory, and the accuracy of prediction results is verified by milling experiment.

Effects of cultivar, planting density and rootstock on long-term economic performance of apple orchards in the Northeastern U.S.

An economic analysis of profitability using Net Present Value (NPV) was conducted using data from two long-term training system × rootstock field trials conducted in New York State from 2006 to 2016 (Dressel Farm in southeastern New York State and VandeWalle Farm in Western, New York State). We used trial data for the first 11 years and estimated values for years 12–20 using average data from the last 4 years of field data. The field trials compared four training systems with different planting densities (Slender Pyramid, 840 trees ha−1; Vertical Axis, 1282 trees ha−1; Slender Axis, 2244 trees ha−1; and Tall Spindle, 3262 trees ha−1) each evaluated with several rootstocks in an incomplete factorial treatment list and with two cultivars at each location. By the end of the trial (11 years) all combinations of planting density, rootstock and cultivar were profitable (NPV positive) at the VandeWalle site but at the Dressel site seven combinations of rootstock and planting density with ‘Fuji’ and two combinations with ‘Gala’ were not profitable. Projected profitability over 20 years using estimated yields and fruit quality for years 12–20 showed that all combinations would be profitable by year 20. Estimated 20-year NPV was greatest with the Tall Spindle system with the highest planting density compared to the other lower density systems. Economic performance was mostly driven by planting density, regardless of the rootstock selection. Among cultivars, ‘Honeycrisp’ had significantly higher profitability largely due to high fruit price). ‘Gala’ had intermediate profitability due to high yields and medium fruit price while ‘Fuji’ which had low fruit price had significantly lower profitability than ‘Gala’. Among rootstocks, there was a significant interaction with training system and cultivar, so the same rootstock was not the most profitable with every cultivar and system. With ‘Fuji’ the most profitable combination was on G.16 rootstock planted at the highest density, however, it was not significantly better than with G.11 or M.9. With ‘Gala’ at Dressel farm the most profitable combination was on G.11 in the Tall Spindle system (planted at the highest density) but it was not significantly better than with G.16, G.41, M.9 or B.9. With ‘Gala’ at VandeWalle farm the most profitable combination was on G.41 planted at the highest density but it was not significantly better than on G.11, G.16, M.9 or B.9. With ‘Honeycrisp’ the most profitable combination was on M.9 planted at the highest density but it was not significantly better than G.11, G.16, G.41 or B.9. A sensitivity analysis showed that among economic parameters affecting the long-term profitability of an orchard, fruit price and yield were vastly more important than other factors. Of intermediate importance were the discount rate and labor costs while of much lesser importance were tree costs and land costs.

Performance evaluation and comparison of different types of full and partial texture of hydrostatic bearings

To reveal the influence of the different types of surface microtexture on the performance of hydrostatic bearing, microtexture (square, cylindrical, and composite texture) on the inner surface of the bearing were designed. The finite element model of bearing with textures was established. Under the condition of considering the cavitation effect, the influence of the different types of microtexture on performance of the bearing is studied based on the N-S equation. The SEA9 spindle rotation error analyzer is used to determine the eccentricity of the spindle in the machining process and to verify the accuracy of the theoretical simulation. The results demonstrates that the microtexture enhances the performance of hydrostatic bearing, when the eccentricity is in the range of 0∼0.3, compared with square, cylindrica, and smooth bearing, the performance of composite texture bearing can effectively improve the load capacity and reduce the friction coefficient. When the eccentricity is larger than 0.3, the partial textured bearing is analyzed. The initial angle is 120°, the load capacity of the partial microtextured bearing are greater than full microtextured bearing and smooth bearing, which can be used to improve the performance of the bearing. The optimal combination of structural parameters for the performance of the partial composite texture is provided, which has important theoretical significance and practical value for expanding the application range of the spindle system.

Prediction of the Modal Parameters of The Spindle System in High-speed Dry Hobbing Machine with Thermal Effects

High-speed dry hobbing is an advanced green machining technology for gears. However, thermal issues in the machine tools become serious under the high cutting speed and dry cutting conditions, which result in a nonlinear evolution of the machine tool dynamics, and finally affect the hobbing stability. In view of this, introducing the hob spindle system as the object, this paper intends to investigate the influence of time-varying temperatures on the modal parameters based on the modal tests. Then, a temperature-dependent modal parameters prediction model based on the multi-output Gaussian process regression (MOGPR) is proposed. The experimental results indicated that the modal parameters of the spindle system vary obviously with changing thermal states. The natural frequencies for the first and third modes increased with the temperature rise, while the variation of the natural frequency for the second mode was insignificant. The damping ratios for the first three modes showed a decreasing trend with the temperature rise. The constructed MOGPR model has the capability to effectively predict the modal parameters with the time-varying thermal effects, which can provide a support to build the temperature-dependent hobbing stability boundaries.