Machine Tool Components Research Articles

An interval prediction approach for quantifying the thermal error uncertainty of ball screw system

The ball screw system is one of the most core components of CNC machine tool, and its thermal positioning error directly affects the machining accuracy of parts. Establishing a data-based thermal error model is currently a popular method to predict this thermal error. Uncertain disturbances are unavoidable in reality. However, most of the existing thermal error models are deterministic, which cannot consider the uncertainty and generally can only provide the predictive value. To quantify the uncertainty of thermal error from the ball screw system, this paper proposes an interval prediction method, in which gated recurrent unit (GRU) neural network is embedded into the model and prediction interval (PI) evaluation indexes are used to determine the model parameters. Both the temporal and spatial characteristics of thermal error in the ball screw system are taken into account by the proposed method. The experimental data has verified that the proposed method can effectively generate PIs for the thermal error of ball screw system.

Design and damping characterization of sandwich composite made of particle-filled hollow spheres and steel sheets

Sandwich composites made of particle-filled hollow sphere structures (PHSS) and steel sheets offer excellent lightweight and damping properties, ideal for high-speed machine tool components under dynamic loads. Previous research has focused on single particle-filled hollow sphere (PHS) and small PHSS, leaving a gap understanding of PHSS/steel sandwich composites. In this study a test rig was developed, and Design of Experiments and Response Surface Analysis were used to investigate the effects of sheet and core thickness (design parameters), filling ratio, and particle size (particle parameters) on the damping performance of PHSS/steel sandwiches. The results indicate that the design parameters have a significant influence on damping performance. The interaction between design and particle parameters also substantially affects damping. Minimizing particle size, increasing filling ratio, thinning the face layer, and thickening the core layer significantly improve structural damping. To address manufacturing tolerances, a finite element (FE) model-based optimization was developed to accurately determine PHSS material parameters. These parameters were used in an FE model of the PHSS-steel composite, with identified contact parameters minimizing measurement and simulation differences. The homogenized material model and the linear model using global damping parameters accurately reproduce the dynamic properties of the PHSS-steel sandwich composite in low vibration modes.

The intelligent operation and maintenance method and experimental research of CNC machine tool bearings

As the core components of high-precision rotary machine tools, machine tool bearings are prone to failure or damage, and their running state directly affects the safety and reliability of the entire equipment. Therefore, the paper proposes an intelligent operation and health management method for machine tool bearings based on Bayes-VMD-KPCA, Bayes-CNN-SVM, and health status evaluation index. Aiming at the complex working environment and high nonlinear degree of fault features of machine tool bearings, the Bayes-VMD-KPCA algorithm is used to perform variational mode decomposition of the original signal of the bearing fault, which avoids the influence of background noise and solves the problem of high dimension of multi-domain fault feature set. Second, the CNN-SVM fault diagnosis model uses a convolutional neural network (CNN) to deeply mine the extracted multi-domain fault features, and a support vector machine (SVM) can be used as a fault classifier to complete the diagnosis of various fault types of machine tool bearings. At the same time, the Bayes algorithm is introduced to tune the hyperparameters in the CNN-SVM model to further improve the prediction performance of the CNC machine tool-bearing fault diagnosis model. The results show that the intelligent operation and maintenance and health management method of CNC machine tool bearings proposed in this paper can effectively complete the fault type diagnosis and health status assessment of bearings, and its accuracy index reaches 99.33%. Compared with the single SVM model, Bayes-CNN model, Bayes-SVM model, and CNN-SVM model, the health effect is evaluated. The accuracy is increased by 6.33%, 4.33%, 2.66%, and 1%, respectively. It can be seen that the proposed method can more effectively realize the monitoring and health management of bearing fault.

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.

Research on the influence of applying counterweight on the deflection and stability of cantilever beam of machine tool

Abstract The deflection and stability of the cantilever beam of the machine tool are important factors that affect the machining accuracy of the machine tool. Because of the space size, it needs to be weighted within a limited size. By designing different weighting schemes, the static analysis and modal analysis are carried out by finite element analysis software, and the influence law of the deflection and stress of the beam is obtained, so the appropriate weighting scheme is selected. Through the analysis of different schemes, it is known that the deflection of the beam is the smallest under the action of the arm of 900mm and the force of 1000N, and the machine tool structure is more compact, which is the key component of the subsequent machine tool.

Fuzzy Evaluation Model for Critical Components of Machine Tools

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools.

Interference-enhanced micro-vision-based single-shot imaging of five degrees-of-freedom error motions for ultra-precision rotary axes

The measurement of five degrees-of-freedom (5-DOF) error motions, including radial, axial, and tilt motions, is crucial for ultra-precision rotary axes, which are key components of ultra-precision machine tools and instrumentation. In this study, we propose an interference-enhanced micro-vision technique to concurrently derive the 5-DOF error motions from a single-shot two-dimensional image, which was captured by a standard industrial camera equipped with an interference objective lens. By consolidating the essential features into a single optical path, the interference-enhanced micro-vision technique ingeniously merges machine micro-vision and modified white-light interference to detect in-plane and out-of-plane motions. Numerical simulations demonstrated, the basic principle for deriving the 5-DOF error motions, and the magnification of objective lens had inconsistent effects on the measurement accuracy for the radial and tilt motions, i.e. higher magnification led to higher radial accuracy but lower tilt accuracy. As practical application, the error motion detection capability was demonstrated by simultaneously measuring the 5-DOF synchronous and asynchronous error motions for a typical air bearing spindle at rotation speeds of 8.33, 108.33, and 308.33 rpm. The synchronous errors were nearly identical at various spindle speeds. However, because of system dynamics, increased vibrations were observed to be superimposed on the basic tilt error motions as the spindle speeds increased, which were verified by the vibration marks imprinted on the turned surfaces. For the 5-DOF motion measurements, the least-square fitting using large-volume edge and greyscale data of the captured image enabled super-high resolutions, despite using a camera with a relatively large pixel size and low bit depth. These results demonstrate that the proposed interference-enhanced micro-vision technique is a simple and effective tool for measuring spatial error motions in ultra-precision rotary axes.

Fatigue life prediction and structural optimisation of linear precision sliding tables under variable loads

ABSTRACT A crucial component of CNC machine tools is the linear precision sliding table, but how to improve the support and fatigue resistance performance of the slide table base has been a problem to be solved. In this research, a variable density method based on interpolating functions is used to topologically optimise the slide table base for better load-carrying capacity. The minimal compliance subject to a specified mass percentage constraint is formulated, and a quadratic topology optimisation by expanding the solution domain yields an innovative base structure. A comprehensive assessment of the structural performance of the new base was performed, including maximum von Mises stresses and cumulative fatigue damage for the reference load case. The fatigue resistance of the optimised slide table base in the reference condition proved to be much better than that of the original structure, while the new design also reduced the mass of the base. These findings conclusively show that the proposed topology optimisation methodology can propose novel base designs that can reduce the experimental cycle time in the design of the base.

FCE-based health status evaluation and IGM-based failure prediction of tool magazine manipulator for CNC machine tools

The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator’s health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator’s health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model’s prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability.

A methodology for laser tool setters calibration and its precise mathematical model

On-machine tool setting is a pivotal approach in achieving intelligent manufacturing, and laser tool setters have become a crucial component of smart machine tools. Laser tool setters play a crucial role in precisely measuring the dimensions of cutting tools during the part machining process, focusing on tool length and diameter. As a measuring instrument, the positions of the laser axis of the laser tool setter need to be accurately calibrated before use. However, in actual calibration scenarios, traditional calibration methods face challenges due to installation errors in the tool setter and geometric errors in the measuring rod. To address this issue, this study proposes a novel calibration method. Initially, the calibration mechanism of the laser beam axis is established. Based on the accurate mathematical model of the laser beam and the measuring rod, and using the polygon clipping algorithm, the mathematical mechanism of the laser tool setter's work is established. Then, a novel method is introduced to calculate the compensation distance between the laser beam reference point and the rod bottom center point at each moment during calibration. Furthermore, by utilizing the kinematic chain of the tool setter calibration system, a new calibration method is developed to accurately calibrate the position of the laser beam axis in the machine tool coordinate system. Finally, the accuracy of the calibration method is verified through simulation experiments and calibration tests. This method improves the calibration accuracy of the tool setter, and the mathematical model of the laser tool setter can be extended to the measurement of tools, thereby improving the precision of tool measurements. This research significantly improves the efficient production performance of smart machine tools.

Post-process updating of model parameters to approximate thermal errors of machine tools operating in different configurations

Machine tool (MT) thermal errors induced by external and internal heat sources (e.g. changing environment, friction and power losses in MT components) are an important element in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors on account of their economic and ecological aspects. Many slightly different approaches are described in the literature. If thermal error models are properly identified based on experiments with sufficiently varying input parameters, they usually work within similar calibration and verification conditions. As the sensory equipment of machines increases, models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into model structures. However, adaptive functionalities require discrete interruptions of the MT's work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. In this research, a novel approach of automatic post-process adaptation suitable for repetitive manufacturing, which takes advantage of transfer functions (TF), is proposed for thermal error reduction. The method respects basic heat transfer mechanisms in the MT structure by combining linear parametric models, requires a minimum of additional gauges, and feedback from direct measurements is obtained only during technological pauses in the production process. Post-process adaptation is solved retrospectively by updating model parameters using the heuristic method of genetic algorithms (GA). First, the approach was tuned on a case study of a heavy-duty milling machine with a horizontal headstock for a configuration with an exchangeable spindle head and a configuration with a change in the position of the headstock in the workspace, both of which were different from the initial model's calibration range. Subsequently, the developed approach was applied to repeated production on a medium-sized milling centre. Another goal of the paper is to emphasize the need for quality input information for modelling efforts and the industrial applicability of scientific results.

Recent Patents on Motorized Spindle Cooling Strategies

Abstract: High-speed motorized spindle is the core component of high-speed machining machine tools, and thermal error is the main factor affecting the machining accuracy of the motorized spindle. Reducing thermal error has become the most important research topic in the case of motorized spindles. The development status of the cooling strategy of the motorized spindle is explored, and the methods to improve the cooling efficiency of the motorized spindle are discussed. Some representative research results of the cooling strategy of motorized spindles at home and abroad are reviewed. Based on summarizing the related research results on cooling strategies for the core components, such as the stator, rotor and bearing of the motorized spindle, this paper analyzes the advantages and disadvantages of various cooling strategies and their applicable working conditions, and the future development direction of motorized spindle cooling strategies is discussed. The combination of various channel structures and the cooling strategy of the stator outer ring of the motorized spindle will improve the cooling efficiency of the motorized spindle, and research on the motorized spindle core cooling will become more and more important. In addition, the cooling system of the motorized spindle bearing, stator, and rotor can be improved at the same time, and the heating problem of the motorized spindle can be solved.

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Abstract The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

Application and evaluation of classic and demand oriented cooling strategies in context of machine tools

AbstractEnergy consumption is an important factor in the operation of machine tools. Recent studies showed that in average, $${1}/{5}$$ 1 / 5 of the machine tools total energy demand is caused by internal cooling systems. The continually rising performance of machine tools, together with the importance of thermal stability, reasons the requirement for demand oriented cooling approaches, which could significantly reduce the energy consumption for internal machine cooling. This study applied classic and demand oriented cooling strategies on a machine tool frame, which is one of the most complex machine tool components from a thermal point of view. The demand oriented cooling is based on volume flow control and can either be configured close to the environmental temperature or close to the thermal steady state, which can be obtained by loss forecast in combination with an FE model or measurement. The impact of the strategies on the thermal reaction as well as the energy consumption is compared, showing a significant reduction of energy demand without significant impairment of the thermal stability.

A fuzzy ANP-based criticality analyses approach of reliability-centered maintenance for CNC lathe machine components

Calculation of the criticality score and its analysis are important for the reliability-centered maintenance (RCM) of CNC lathe machine components. The tools of a CNC lathe machine include complex critical part and sub-systems. The reliability of parts decline due to the independent system fault. To identify the essential components of CNC lathe machine tools, a system level component criticality analysis method is proposed. In this study, a comprehensive framework of criticality-based RCM implementation through experimentation of a CNC-lathe machine is proposed. The criticality of machine components were identified by investigating their dependency on the criteria and sub-criteria. Five main criteria were found effective for the criticality of the components which are the cost, complexity, sustainability, functional dependency, and safety impacts. Fuzzy analytical network process (FANP) approach was employed to evaluate criticality scores for eleven-component of a CNC lathe machine. This is a preliminary study and aims to contribute to the literature and practitioners in many ways. The primary focus of our method lies in its capability to predict criticality values for a manufacturing system in absence of historical maintenance data relying on the expertise/intuition of the operator(s) for decision making for the predictive maintenance of the system with the help of fuzzy ANP approach. It was found that the turret was the most critical component with a metric of 0.0641 which was also found to be over 3 times more critical than the least critical hydraulic system, which had a criticality metric of 0.0187. This novel FANP framework provides a credible solution to real-world RCM scheduling and can be applied to any manufacturing system-related application.

Modeling and compensation of the axial thermal error of electric spindles based on HHO-GRU method

As the core component of precision CNC machine tools, a lot of heat is generated from the internal heat source of electric spindles during operation, resulting in thermal deformation and thermal errors that affect machining accuracy. Thermal error compensation is an economical method for reducing thermal errors, through which the impact of thermal errors on machining accuracy can effectively decrease. Taking a high-speed electric spindle as the research object, the temperature measurement points are selected as its front and rear bearing seat, as well as some positions far from the heat source. The temperature changes at the front and rear bearing as well as in the environment are monitored, then the thermal errors are measured using a Lion spindle rotation accuracy instrument. The optimal training parameters of the gated recurrent unit (GRU) network are optimized utilizing the global optimization ability of a Harris Hawks optimizer (HHO). Finally, the thermal error prediction model of the GRU electric spindle optimized using the Harris Hawks optimizer (HHO-GRU) is established, based on which axial thermal error compensation experiments are conducted. The results show that using the HHO-GRU prediction model for compensation, the axial thermal errors of the electric spindle can be reduced by more than 80%, which can be controlled within 5 μm.

An Algorithm for Optimizing the Process Parameters of the Spindle Process of Universal CNC Machine Tools Based on the Most Probable Explanation of Bayesian Networks

As an essential component of a universal CNC machine tool, the spindle plays a critical role in determining the accuracy of machining parts. The three cutting process parameters (cutting speed, feed speed, and cutting depth) are the most important optimization input parameters for studying process optimization. Better processing quality is often achieved through their optimization. Therefore, it is necessary to study the three cutting process parameters of the CNC machine tool spindle. In this paper, we proposed an improved algorithm incorporated with the beetle antennae search algorithm for the most probable explanation in Bayesian networks to achieve optimization calculation of process parameters. This work focuses on building adaptive dynamic step parameters to improve detection behavior. The chaotic strategy is discretized and used to establish the dominant initial population during the population initialization. This article uses four standard network data sets to compare the time and fitness values based on the improved algorithm. The experimental results show that the proposed algorithm is superior in time and accuracy compared to similar algorithms. At the same time, an optimization example for the actual machining of a universal CNC machine tool spindle was provided. Through the optimization of this algorithm, the true machining quality was improved.

Remaining Useful Life Estimation of Spindle Bearing Based on Bearing Load Calculation and Off-Line Condition Monitoring

Spindles are key components of machine tools. An efficient estimation of the spindle condition and its prognosis can improve production efficiency and quality due to predictive maintenance planning. This paper proposes a method for predicting the remaining useful life (RUL) of machine tool spindle bearings using a combined calculation and experimental approach. The calculation model based on the ISO 281 standard uses monitored real loading conditions caused by the machining process and the machine tool operation. The model enables the updated calculation of the spindle lifetime L10h using real load distribution. Since the operation hours of the spindle are also monitored, the remaining useful life (RUL) of the spindle can be calculated. This RUL value is corrected using a bearing condition assessment based on the effective value of the vibration velocity RMS according to the ISO 20816 standard and measured data from the machine tool control system. The proposed method is tested on two different spindle types featuring three pieces of every type. The experimental results of six spindles are compared and validated with a concurrent blind evaluation conducted by a skilled expert. The validation shows a very good match of the proposed method and the expert opinion. The method combining a calculation of the spindle lifetime using monitored real load distribution and subsequent result correction using vibration signal enables the implementation of a full automated estimation of the spindle RUL.

Study on the 2D Equivalent Simulation Model of Bearing and Spindle for Precision Machine Tools

Precision spindle and bearings are the key components in precision machine tools. These structures greatly affect the machining accuracy and service life of the machine tools. In this paper, considering the uncertainty of rolling elements when the bearings are working at high speed, a new 2D equivalent simulation model of angular contact ball bearing was established based on the general finite element software, Abaqus. Meanwhile, the equivalent material parameters of virtual bearing ball in this 2D model were obtained via a standard bearing stiffness test and a parametric inversed method. The time to calculate of this model is reduced by 200 times compared with the 3D bearing simulation model. Then, the 2D equivalent simulation model of the spindle was established based on the 2D bearing model, which is used to calculate the axial stiffness and maximum contact stress between bearing balls and inner/outer rings in different assembly parameters. The results show that the stiffness of the spindle increases slowly at first, but then increases rapidly to a peak value after the bearing inner spacer sleeve is in contact with the bearing inner ring, and finally tends to stable, with the preload of the spindle continuing to increase.

An update method for digital twin multi-dimension models

Digital twin, as an effective means to realize the fusion between physical and virtual spaces, has attracted more and more attention in the past few years. Based on ultra-fidelity models, more accurate service, e.g. real-time monitoring and failure prediction, can be reached. Against the background, some scholars studied the related theories and methods on modeling to depict various features of physical objects. Some scholars studied how to use Internet of Things to realize the connections and interactions, thereby keeping the consistency between the virtual and physical spaces. During this process, a new question arises that how to update the models once digital twin models are inconsistent with the practical situations. To solve the problem, this paper proposed a general digital twin model update framework at first. Then, the update methods for multi-dimension models are further explored. The cutting tool is the core component of machine tools which are the key equipment in industry. The precise cutting tool models are essential for realizing the digitalization and servitization of machine tools. Therefore, this paper takes a cutting tool as the application object to discuss how to conduct physics model update based on the proposed framework and methods. Through model update, a more accurate and updated tool wear model could be obtained, which contributes to the prognostics and health management for machine tools.