Machine Tool Spindle System Research Articles

Hierarchical explicit–implicit combined sensing-based real-time monitoring method for the service performance of complex equipment

PurposeReal-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.Design/methodology/approachSensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.FindingsThe proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.Originality/valueThis research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

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.

Fast identification of machine tool spindle system temperature rise based on multi-model fusion and improved D-S evidence theory

Thermal equilibrium test is the key means to obtain the thermal characteristics of machine tools. In order to shorten the test period and reduce the research and development cost, a novel fast temperature rise identification method for machine tool spindle systems is proposed. The existing prediction identification methods ignore the limitation of the single prediction model, leading to large error fluctuations in different environments. In this study, various intelligent prediction models are combined with the improved D-S evidence theory to improve the accuracy and robustness of the prediction. Firstly, based on the virtual prediction, the evidence identification framework is established through the multiple evaluations of the data information in the evidence segment. Then, the weight allocation of each basic prediction model is carried out by the evidence combination theory. In this process, the evidence identification framework is reconstructed according to the improved strategy to avoid the high conflict problem in classical evidence theory. Finally, the fusion prediction of multiple models can be realized. The VM-850L machining center was selected as the research object for the thermal equilibrium test to evaluate the proposed method. The results show that the proposed multi-model fusion prediction method can accurately predict the temperature rise of selected points in a short time. Moreover, the prediction accuracy is significantly improved compared with the traditional single model. The proposed method has good universality and is expected to be popularized and applied more widely.

Preface

The 2021 6th International Seminar on Computer Technology, Mechanical and Electrical Engineering (ISCME 2021) was planned to be held on November 05-07, 2021 in Zhuhai, China. Taking into account the COVID-19 pandemic, the ISCME 2021 conference was held virtually as agencies around the world are now issuing restrictions on travel, gatherings, and meetings in an effort to limit and slow the spread of this pandemic. The health and safety of our participants and members of our research community is of top priority to the Organizing Committee. Therefore, the ISCME 2021 conference was held online through Zoom software.ISCME 2021 is to bring together innovative academics and industrial experts in the field of Computer Technology, Mechanical and Electrical Engineering to a common forum. The primary goal of the conference is to promote research and developmental activities in Computer Technology, Mechanical and Electrical Engineering and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Technology, Mechanical and Electrical Engineering and related areas.More than 100 participants attended the online conference. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, we invited three professors as our keynote speakers. Prof. Xupeng Wang, Northwestern Polytechnical University, China. His Research interests: Mechanical system dynamics, Human sports biomechanics, Innovative design and simulation, Computer aided industrial design. Prof. Guo Li, from Hunan University, China. He devoted in Intelligent and efficient precision grinding technology and CNC machine tool equipment, Dynamic and static pressure spindle system of intelligent machine tool, Advanced automobile design and manufacturing and body welding stamping technology, Intelligent optical-mechanical-electrical integration design and manufacture research area. Assoc. Prof. Xiaogang Liu, our finale keynote speakers, from School of Mechanical and Electronic Engineering, Wuhan University of Technology, China. His research interests: Electromechanical control, Intelligent Manufacturing, Contact Mechanics, Mechanical Vibration. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Intelligent mechatronics, robotics, biomimetics, automation and control systems, Sensor design, multi-sensor data fusion algorithms and wireless sensor networks, AI, intelligent control, neuro-control, fuzzy control and their applications and Other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.We would like to acknowledge all of those who supported ISCME 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers.The Committee of ISCME 2021List of Committee member is available in this pdf.

Dynamics analysis and energy consumption modelling based on bond graph: Taking the spindle system as an example

The spindle system of a Computer Numerical Control (CNC) machine tool is essential for improving the machining efficiency and accuracy. Although there are many studies on the energy consumption of machine tool spindle, few comprehensively consider the energy consumption and dynamics characteristics of the mechanical transmission of the spindle system. Therefore, a new bond graph method for modelling the dynamics characteristics and energy consumption of the spindle system of a CNC machine tool is proposed to solve this problem. The bond graph model of the spindle system is established based on the structure and energy flow characteristics of the spindle system. The bond graph model is converted into a dynamic simulation model that can be run in the MATLAB-Simulink environment. The dynamics and energy consumption characteristics of the spindle system are analyzed by observing the response of different input torques in the dynamic simulation model. Finally, the feasibility and practicability of the model are verified by experiments. The predictive accuracy of the proposed model is above 95%, which offers a viable approach for optimizing the design of the spindle system.

Reliability assignment of a heavy-duty CNC machine tool spindle system based on fault tree analysis

Reliability assignment of a heavy-duty CNC machine tool spindle system based on fault tree analysis

Reliability assignment of a heavy-duty CNC machine tool spindle system based on fault tree analysis

Research on the distribution of machine tool reliability indexes is of great significance to improve the reliability of machine tools in the design and manufacture stages. Owing to the complex structure of heavy-duty CNC machine tools, multi-source and heterogeneous failure causes, etc., the traditional single method cannot get accurate results. In this paper, based on the FTA method, a three-level machine tool fault tree model is established to carry out research on reliability index assignment. Then, as the levels progress, the distribution of reliability indicators is based on probability importance analysis, reliability redistribution method, and AHP respectively. Finally, the top-down reliability assignment results are obtained. After analysis, the structure obtained by combining multiple reliability allocation methods is more in line with the actual situation. According to the results, the weak parts of the machine tool are designed and improved, thereby improving the reliability of the machine tool.

A method of thermal error prediction modeling for CNC machine tool spindle system based on linear correlation

A method of thermal error prediction modeling for CNC machine tool spindle system based on linear correlation

Fuzzy FMECA for CNC machine tool spindle system

The spindle systems of the state-of-the-art computer numeric controlled (CNC) machine tools are increasingly important than ever, for that the even distinguished demand of higher efficiency, reliability, and productivity of today’s manufacturing industry lead to the aforementioned system works under higher rotational speed and higher cutting performance. This paper extended the conventional failure modes, effect and criticality analysis by integrating which with fuzzy theory to finitizing a failure analysis of an electric spindle system of a CNC machine tool aims to avoid unwanted failures of the system and achieve safe and reliable operation of the device. Critical components, critical failure modes of the spindle system that call for special attention are ascertained. Root causes of the critical failure modes as well as recommendations e.g. periodical inspection of the bearings as well as robust design for electrical and electronical complements for avoiding their occurrence are obtained.

Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints

Motorized spindle system is one of the crucial components affecting the machine tools energy performance. Many previous studies have examined its energy optimization problems, however, most such studies focused mainly on parameters optimization to improve material removal energy efficiency or reduce total energy consumption. A missing research area is energy optimization problem considering thermal stability and productivity constraints simultaneously. Against this background, an energy optimization approach of motorized spindle system is presented with consideration of thermal stability and productivity adequately, with the goal of maximization of energy efficiency and material removal rate, and minimization of spindle average temperature which is closely associated with thermal stability. Firstly, the energy characteristics of motorized spindle and its cooling system are mathematically modelled. Then, a multi-objective optimization model is established to take the maximum energy efficiency, minimum spindle average temperature, and maximum material removal rate as objectives. The optimal solution is obtained by solving the proposed optimization model with the Non-dominated Sorted Genetic Algorithm-II (NSGA-II). Finally, a case study is introduced to validate the proposed method and the results indicate that the proposed method is more effective to find optimal decision variables for balancing the considered objectives compared with the existing optimization method.

Performance Research on Bi-directional Tilting Pad Thrust Bearing Considering the Verticality Error of Thrust Plate

In order to improve the axial stiffness and its controllability of the CNC machine tool spindle system, a bi-directional tilting pad thrust bearing is used instead of the hydrostatic thrust bearing to bear the axial load. Due to the processing and installing, there must be a certain verticality error between the thrust plate and rotor axis. The lubrication model of the thrust bearing considering the verticality error is established in this paper. The Reynolds equation is solved by FDM (finite difference method), and the key performance data of the bidirectional tilting pad thrust bearing under rated conditions are obtained, including eccentricity, minimum film thickness, maximum temperature rise and axial stiffness, etc. The variation of performance parameters such as film thickness, temperature rise and stiffness of thrust bearing with different working conditions are mainly studied. The maximum allowable verticality errors in the whole working condition range of load and speed are given. According to the requirements of film thickness and temperature rise, the thrust bearing clearance is optimized based on the comprehensive consideration of lubrication performance and dynamic performance, and the allowable ranges of clearance under different verticality errors is analyzed. This research provides theoretical guidance for the design and manufacture of tilting pad thrust bearings used for the CNC machine tool spindle system.

Multi-objective Optimization of Machine Tool Spindle-Bearing System

In this study, a multi-objective optimization is performed for the design of a spindle-bearing system based on particle swarm optimization (PSO). Multiple objectives, such as natural frequencies, static stiffness, and total friction torque are considered in this design optimization. Bearing preload and bearing locations are selected as the design variables. Pareto-optimal solutions are used to support the selection of optimal values of the design parameters. A finite element model is established for the analysis and design of the spindle system with four angular contact ball bearings. Two optimization processes are performed with the PSO technique. The first process involves the first two natural frequencies and friction torque of the spindle, whereas the second process focuses on the spindle’s static stiffness and friction torque. The simulation results show noticeable improvement in the objectives compared with those of the primitive spindle. The experiments conducted on an actual spindle system fabricated with the optimal design demonstrate the benefits of the optimal design. The proposed design method is expected to be very useful in the design optimization of machine tool spindle systems subjected to various customer-oriented objectives.

Thermal error modeling of CNC milling machine tool spindle system in load machining: based on optimal specific cutting energy

The aim of this study is to reduce the influence of machine tools spindle thermal error on machining accuracy in load machining state. The temperature and thermal error measuring system and experiments of the 3-axis vertical milling machine tool spindle in idling and load machining state were established and carried out. The differences in temperature and thermal error between the idling and load machining states were analyzed. Upon combining the fuzzy clustering and gray correlation algorithm, the temperature-sensitive points were optimized. The thermal error prediction models of machine tool spindle system in load machining state with the optimal specific cutting energy were established based on the adaptive chaotic particle swarm optimization algorithm, and the model prediction effects were evaluated. The results showed that the spindle system temperature and thermal error in load machining were higher than the idle state. Two temperature-sensitive points were selected that not only reduced the redundancy of temperature measuring points but also ensured the model prediction accuracy. The thermal error models prediction accuracy was above 90%, and the root mean square error and residual error were better than PSO and regression. The experimental results showed that the thermal error prediction models have a high prediction accuracy and engineering application value.

Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm

The thermal error of machine tool spindle system has become an important factor affecting machining accuracy. Measurement experiments of a 3-axis vertical milling machine spindle system were conducted to assess temperature fields and thermal errors. The fuzzy clustering and gray correlation algorithms were adopted to cluster the temperature measuring points and identify the temperature-sensitive points. Based on the adaptive chaos particle swarm optimization algorithm, thermal error models were established in the axial and radial directions for the spindle system, and the compensation effects were evaluated by the workpiece machining accuracy. The results showed that the number of temperature measuring points was reduced from 12 to 6. The residual range of measured and predicted thermal error values in the axial direction was 6.17–4.19 μm, and the modeling accuracy was 95.53%. The radial residual ranges were − 2.75–3.05 μm and − 2.10–2.15 μm, and the modeling accuracies were 90.74% and 91.10%, respectively. The model compensation effect was demonstrated remarkably in the verification experiments. The thermal error models showed high prediction precision, could improve machining accuracy and had strong engineering application value.

Fast heat conduction-based thermal error control technique for spindle system of machine tools

In view of the thermal declination of spindle system caused by the uneven heating, this paper proposed a thermal error reduction strategy by optimizing the thermal conductivity of the material. Firstly, according to the structural size and material characteristics of the spindle system, the thermal resistance and thermal contact resistance of each component are calculated, thereby establishing a spindle-housing thermal resistance network model and calculating temperature values of the key points. Then, heat conduction and insulation device are properly installed in the headstock, so that the heat in the high temperature zones can be quickly conducted to the low temperature zones, and the temperature field of the headstock is balanced. Finally, the multi-objective optimization method is used to optimize the size, material properties, and surface parameters of the device to achieve optimal heat balance. Verification was performed by numerical simulation and experiments. The results demonstrate that the thermal conductivity optimization strategy can effectively improve the temperature nonuniformity of the headstock and reduce the thermal tilt of the spindle.

Energy consumed by a bearing supported spindle in the presence of a dynamic cutting force and of defects

A machine tool spindle system model is proposed in this paper to investigate the non-linear face-milling cutting forces behavior, which are neglected in the literature, in order to predict the total mechanical power of a spindle. A simulation of the structure of the spindle based on the finite-element method is elaborated to estimate the variable cutting forces and then the variable power loss generated by bearings, considering the angular position and contact angles of the variable balls. Experiments are elaborated to compare the experimental power values with the predicted results. Particular attention is paid to different types of defects (inner ring spalling, outer ring spalling, eccentricity, and unbalance) in order to study their impact on the power consumed by the spindle during the approach and cutting phases under different rotating conditions.

Fault tree analysis method based on probabilistic model checking and discrete time Markov Chain

ABSTRACTIn order to fully consider the dynamic characteristics of event failure in fault tree analysis, the probabilistic model checking and discrete-time Markov chain are applied to the fault tree analysis. The formal probability of gate events in a fault tree is studied and the fault tree is established based on the probabilistic model checking technique. The logic gates in a fault tree are converted into discrete-time Markov chain that can be encoded into the probabilistic model checker. The discrete-time Markov chain is used to simulate the known behavior probability distribution of the system in the known situational environment, and the Markov decision process is used to simulate the non-deterministic behavior of the system under the unknown environment, and to carry out the quantitative analysis of the system. The fault tree analysis of the spindle system of a certain CNC machine tool is taken as an example to illustrate the proposed method.

Detection of spindle thermal state accuracy of vertical CNC machine tool

In order to test the thermal accuracy of spindle system of mining vertical CNC machine tools, to enhance the error compensation ability of the system accuracy detection model and improve the machine tool processing accuracy, a test platform for thermal accuracy of spindle system was constructed. Five-point method was used to test the thermal accuracy of spindle system, and thermocouple and infrared thermal imager were used to measure the spindle system. Temperature change data at key points of temperature rise are used to identify temperature sensitive measurement points by grey comprehensive correlation analysis method. A thermal accuracy prediction model based on cooperative quantum particle algorithm is constructed. The prediction effect of the model is evaluated. The experimental results show that the thermal accuracy detection model has high detection accuracy.

Research on the axial stiffness softening and hardening characteristics of machine tool spindle system

Bearing stiffness exhibits non-linear characteristic when the bearing is subjected to a certain external load, which will inevitably cause changes in the spindle stiffness, since bearing stiffness has a decisive impact on the spindle stiffness. In this paper, the axial stiffness softening and hardening characteristics of machine tool spindle were studied. A novel spindle axial load automatic applying device and the spindle stiffness test bench were proposed. Meantime, an axial stiffness model for the experimental spindle was established based on the bearing load–displacement model and mechanical analysis of spindle. Then, the axial stiffness of fixed position preload spindle under different preload was measured experimentally and the evolution mechanism was analyzed. The results show that when bearing preload reaches a certain relatively large threshold, a “sag” shape occurs in the axial stiffness curve, indicating the “stiffness hardening” characteristic of the spindle. On the other hand, for a small preload, no “sag” shape occurs in spindle stiffness curve, indicating the “stiffness softening” characteristic of the spindle. This phenomenon is of great significance for the acquisition of the excellent spindle stiffness properties.

Study of the temperature distribution of a machine tool spindle bearing based on FBG quasi-distributed sensing

Temperature field distribution of a machine tool spindle system, especially the spindle bearing, directly determines the size and direction of the thermal errors, which will result in poor processing precision and degradation of mechanical property of the machine tool. Measurement and monitoring of spindle bearing temperature field are of great significance to enhance the manufacture precision of NC machine tool effectively. Traditional measurement methods of spindle bearing temperature field are single-point or two-point measurement and cannot be stuck to the bearing outer ring directly, thus the data being inaccurate. In this paper, a multipoint quasi-distributed sensing method based on embedded fiber Bragg grating (FBG) sensors was proposed to measure the temperature field of the outer ring of the bearing under three different working conditions: idling, uploading axial, and radial cutting force respectively. A traditional thermocouple had been used for measurement comparison and a finite element method (FEM) prediction method utilizing ANSYS Workbench software package had been presented for temperature distribution prediction. Experiment results showed that the measurement error between FBG and thermocouple was within 10%. The temperature distribution achieved by FBG sensors had great consistence with the simulation prediction model. It was demonstrated that this quasi-distributed sensing method can be applied to the measurement of spindle bearing temperature field distribution very well. Moreover, it can be seen as a significant means to effectively verify the thermal model of the spindle bearing integrated system.