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The versatility and large work envelope have made robots a fixture in the field of assembly for years. However, their lower stiffness and pose dependency require robust models to find optimal trajectories even for high accuracy applications. A significant obstacle in this domain is parametrizing such models of compliant robots during operation. Addressing this gap, and considering the trend of robots performing manufacturing tasks in parallel with assembly, we present an automated identification process to estimate the stiffness and damping parameters of robot joints within a milling process. This method relies solely on universally accessible kinematic chain data and force and acceleration measurements at the tool center point, eliminating the need for specialized equipment. The approach is based on a multi-body simulation, which includes flexible 6-DOF bushing joints. Key to our approach is using Jacobian-based sensitivities inside a Random Search (RS) algorithm to navigate the complexities of a sparse multi-dimensional parameter space. Our approach is versatile enough to accommodate various parameter types. We test our approach on a simulated 3-joint robot with 6 DOF per joint. By pairing the Jacobian-based sensitivities with adaptions made to the RS algorithm, we obtain accurate predictions for unknown input data with a mean relative displacement error of 2%. [ABSTRACT FROM AUTHOR]

Machine tool accuracy is greatly influenced by geometric and thermal errors that cause positioning deviations within its working volume. Conventionally, these two error sources are treated separately, with distinct procedures employed for their characterization and correction. This research proposes a unified volumetric error compensation approach in terms of a calibration procedure and error compensation model, which considers geometric and thermal errors as a single error source that exhibits temporal variation primarily due to changes in the machine's thermal state. Building upon previous works that introduced a fully automated volumetric calibration procedure capable of characterizing the variation in volumetric error over time, this study extends this methodology, incorporating multiple temperature sensors distributed throughout the machine and generating a digital twin based on a volumetric error compensation model capable of predicting and compensating for the volumetric error over time at any point in the workspace, using temperature measurements and axis positions as inputs. This methodology is applied to a medium-sized milling machine tool. The digital twin is trained and validated on volumetric calibration tests, wherein various controlled heat sources are employed to induce thermal variations while measuring the temperatures in the machine. [ABSTRACT FROM AUTHOR]

Currently, there are demands for machine tools with higher dynamic performance as a result of their high machining accuracy and efficiency in designing complex parts in the manufacturing industry. The motion delays in each of the motion axes cause dynamic tracking errors or tool tracking errors which greatly affect the surface quality of machined parts; hence, tuning the dynamic parameters like the position gains of the motion axis causing the defect is as good as eliminating the error source rather than tuning the parameters of all the motion axes. This work proposes a decoupling method to identify the particular motion axis that greatly affects or causes the tool tracking error by using the ISO BK3 kinematic test, and this involves orientation contour error estimation and motion axis error computation based on inverse kinematics of the machine tool transformation. An experiment was carried out on an industrial machine tool with a tilting rotary table to verify the simulation results; the feed servo system model of the motion axes was constructed using MATLAB Simulink tools. The results obtained show that the tool tracking errors are greatly influenced by some dynamic parameters like the feedrate and the position gains emanating from a particular motion axis with dynamic deficiency, and the proposed decoupling method robustly identifies the individual motion axes greatly affecting the dynamic tracking error which makes it very relevant to tune only the dynamic parameters of those particular motion axes instead tuning all. Moreover, this method is simple and robust, and its implementation can help improve the dynamic performance of machine tools in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Abstract 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.

As the "mother machine" of manufacturing, machine tools have been widely employed, but with high energy consumption, low energy efficiency, and serious carbon emissions. How to reveal the dynamic characteristics of carbon emissions is of great significance for machine tools to achieve green and sustainable development. Existing research neglected the dynamic characteristics of carbon emissions and focused on a single phase for machine tool. To this end, this study carried out the digital twin-driven modeling and application of carbon emission for machine tool considering both the design and usage stages. Utilizing real-time data acquisition and model refinement of digital twin system, this research proposed an adaptive algorithm for the dynamic prediction and optimization of processing parameters. Related management services in usage stage based on digital twin was developed and a digital twin-driven carbon emission prototype system was established. And a milling experiment was performed to validate the practicality and feasibility of the proposed algorithm. Besides, combining with operation data recorded by digital twin model, working conditions of machine tool were simulated in the digital space. Optimization of tool part selection under simulated conditions can be realized during design stage, which makes the closed-loop design for machine tool. Considering both design and usage stages, this research offers a reference solution for the multidimensional, intelligent, and collaborative management of carbon emissions for machine tool. [ABSTRACT FROM AUTHOR]

Surface grinding is critical for producing precise workpiece dimensions and surface finishes, especially for materials like cast iron and steel. Optimal grinding materials must reliably cling to magnetic chucks and resist melting or fouling the cutting wheel. Our investigation seeks to optimize the static and dynamic performance of surface grinder machines by evaluating their structural integrity and dynamics using finite element analysis (FEA). Modal and static studies are done under various tool post and column configurations to simulate real-world machining circumstances. Modal analysis reveals key resonant modes, with lowest frequencies 55, 61, and 85 Hz, respectively, seen when the column and tool post are centrally positioned. Modal analysis elucidates the natural frequencies affecting the machine's structure, aiding in resonance abatement during machining, and increasing machining precision. Static obtains a −500 N force along the Z-axis direction at the tooltip, alongside a gravity force of 9.8 m/s affecting all components. Investigation indicated substantial distortion at the junction of the grinding stone and primary tool, with a maximum deformation of 18.5 μm. Harmonic analysis, done within a frequency range of 55–235 Hz, corroborated the mode shape analysis results by exerting a −500 N force along the Z-axis. Spatial analysis was done to investigate the movement of the machine column and tool post during milling. Position C displayed the highest modal frequency 61, 67, and 95, respectively, due to the tool post's proximity to the working surface. This emphasizes the necessity of spatial optimization in machining. Experimental validation utilizing the predictive diagnostic performance system (PDPS) validates the differentiation between healthy and unhealthy machine signals during grinding, facilitating real-time issue identification. Predictive maintenance predicts machine issues, cuts downtime, boosts quality, and saves costs. This study attempts to find maximum deformation in machine tool structures under different configurations, ultimately increasing the structural properties and stability of surface grinding machines. [ABSTRACT FROM AUTHOR]

This paper introduces a complete real-time algorithm, where the chatter is detected and eliminated by spindle speed manipulation via the chatter energy feedback calculated from the axis encoder measurement. The proposed method does not require profound knowledge of the machining dynamics; instead, the entire algorithm exploits the fact that milling vibrations consist of forced vibrations at spindle speed harmonics and chatter vibrations that are close to one of the natural modes, with sidebands which are spread at the multiples of spindle speed frequency above and below the chatter frequency. The developed algorithm is able to identify the amplitude, phase and frequency of all the harmonics constituting the periodic forced and chatter vibrations. The key challenge is to select dominant chatter frequencies for the calculation of a robust and accurate chatter energy ratio feedback; this is achieved by utilizing the frequency estimation variance of EKF as a novel chatter indicator. Based on the chatter energy ratio feedback, the controller overrides the spindle speed in order to suppress the chatter energy below a particular threshold value. The varying spindle speed challenge is handled by updating the state transition matrices of the Kalman filters and real-time calculation of the band-pass filter coefficients, based on the derived discrete time transfer functions. The developed algorithm is tested on a Deckel FP5cc CNC which is in-house retrofitted and has a PC-based controller for the real-time application of the proposed algorithm. It is shown that the real-time chatter frequency and amplitude estimates are compatible with off-line FFT analysis, and chatter can be successfully eliminated by energy feedback. [ABSTRACT FROM AUTHOR]

Computer numerical control (CNC) machine tools mainly use transmission elements such as screws and gears as drive systems. To obtain better accuracy, equipment manufacturers apply pre-pressure to the screw to reduce the occurrence of backlash errors. Backlash errors are one of the important factors affecting positioning accuracy. When the pre-pressure fails, a backlash error occurs. Backlash error can cause abnormal vibration or drive delays in CNC drive systems, leading to reduced transmission efficiency and poor positioning accuracy. Therefore, in most research, vibration sensors are installed on the fixed end of the screw or the nut to detect abnormal phenomena and to estimate the backlash error using the algorithmic model. This traditional method has been established on the basis of the relationship between the amount of vibration and backlash error using an algorithmic model. The backlash can be estimated using this traditional method. However, this traditional method cannot directly sense backlash errors, and the accuracy of the measurement is limited by the algorithm model. Therefore, in this study, we attempted to use a photoelectric switch sensor combined with a microcontroller unit to detect the backlash errors of the CNC drive system. This system can detect a time change of 10 µs. After error corrections, the gain values of each backlash amount and delay time can be used to predict the backlash amount. Through the final verification with commercially available instruments, the system accuracy was found to reach 0.001 mm on the linear axis and 0.001° on the rotating axis. Owing to the streamlined sensing principle of the proposed system, in the future, the system can be integrated into the relevant rotary table components or within the CNC drive system. [ABSTRACT FROM AUTHOR]

Machining configurations, as well as machine tools and coolant conditions, have a significant impact on the selection of cutting parameters. Cutting parameter selection varies with the dynamics of the machining configuration. However, most of the existing approaches carry out cutting parameter optimization under the fixed machining configuration and cannot have the capability to dynamically optimize cutting parameters according to the machining configuration. To this end, a data and mechanism hybrid-driven cutting parameter optimization method is proposed considering machine tool and coolant condition flexibility. Specifically, the effect of various machining configurations and cutting parameters on energy consumption is analyzed, and the cutting parameter optimization model is proposed considering the machine tool and coolant condition flexibility. Secondly, the proximal policy optimization-based cutting parameter energy-efficiency optimization method is developed to select cutting parameters based on the dynamic change of machining configurations. Finally, a case study is conducted to validate the proposed cutting parameter optimization method, and the results show that (1) the proposed data-mechanism hybrid-driven modeling approach can integrate the advantage of data-driven and mechanism-driven models, improving its prediction accuracy compared with other methods; (2) the energy consumption characteristics varies with the dynamic change of machining configurations, and the proposed cutting parameter optimization method can efficiently select cutting parameters adapted to the dynamic change of machining configurations. [ABSTRACT FROM AUTHOR]

As the world is more focused on developing products and services, which are sustainable, special attention is needed in the design and manufacture of machine tools. There is a multitude of elements that contribute to the sustainability of machine tools. Moreover, the elements are interdependent with each other. The presence and absence of each element can be measured by different indicators. This paper analyzes the influence of elements and their indicators on the sustainability of machine tools. Sustainability elements and their indicators have been identified first. Further, a new hybrid model was developed to find the interdependencies between the sustainability elements for machine tools and determine the priorities of the indicators. Two structural models, i.e., Decision-Making Trial and Evaluation Laboratory and Analytic Network Process, have been combined to establish levels and intensities of interdependence as well as the relative weight of selected indicators. The results classify the indicators into the receiver, dispatcher, and neutral groups and also point out the indicators with feedback dependencies, i.e., indicators that depend on themselves. The methodology can be applied for evaluating as well as comparing the sustainability of different machines. The observations would be helpful to the designers of machine tools to make strategies for improving the sustainability of their machines. [ABSTRACT FROM AUTHOR]

The assessment of production plant efficiency is crucial for optimizing the operational performance of manufacturing systems. In traditional facilities, automated data collection is limited and information primarily relies on operators declarations, which are prone to inaccuracy. There is therefore a need for readily accessible digital alternatives. This paper introduces a cost-effective method for classifying the status of machine tools using smart sensors to monitor their primary doors with minimal integration, and a streamlined algorithm for efficient data processing. The innovative algorithm was conceived using data collected in over 3 months in a manufacturing plant comprising 50 diverse machine tools engaged in batch production for the automotive industry, and is based on non-dimensional thresholds, making it suitable for generic applications requiring classification of repetitive patterns. Also, a realistic simulator was developed to provide reliable data for algorithm accuracy evaluation. The classification performance was fully tested using synthetic data, showing very good accuracy. In addition, the performance of the algorithm was compared to basic machine learning approaches further proving the validity of the proposed method. Ultimately, the classification algorithm was employed to assess the Overall Equipment Effectiveness (OEE) of the real plant machines, which were closely aligned with the estimates provided by the enterprise management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The thermal error of machine tool has always been an important reason affecting the accuracy of machine tool. In modern methods to reduce thermal error, thermal error compensation method and thermal error isolation method are two methods with high operability. Thermal error compensation method emphasizes predicting deformation in advance and then compensating in advance. Thermal error isolation method emphasizes the reduction of numerical value in thermal error propagation path. In this paper, the machine tool swing head is taken as the research object, and the above two methods are innovatively combined; Build an experimental platform and a simulation model to ensure the accuracy of the experiment through mutual verification; The POA-LSTM prediction model is established to predict the expansion of the swing head shell structure caused by thermal load accumulation, and compared with the LSTM prediction model. The results show that the prediction accuracy of the POA-LSTM prediction model in Z direction and X direction is 98.89 % and 98.38 %, respectively, which is higher than that of LSTM's 89.33 % and 89.47 %. The experiment for shell structure optimization is designed to reduce the thermal expansion of the pendulum and improve the machining accuracy. As a result, the thermal expansion inhibition effect on the whole pendulum is 26.14 %, and the thermal expansion inhibition effects on the X-direction, Y-direction and Z-direction are 34.93 %, 33.35 % and 30.24 % respectively.

Abstract Machine tools, often referred to as the “mother machines” of the manufacturing industry, are crucial in developing smart manufacturing and are increasingly becoming more intelligent. Digital twin technology can promote machine tool intelligence and has attracted considerable research interest. However, there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence. Herein, digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin. The review then delves into state-of-the-art digital twin modeling-enabled machine tool intelligence, examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling. Additionally, it highlights three bottleneck issues facing the field. Considering these problems, the architecture of a digital twin machine tool (DTMT) is proposed, and three key technologies are expounded in detail: Data perception and fusion technology, mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology, and dynamic optimization and collaborative control technology for multilevel parameters. Finally, future research directions for the DTMT are discussed. This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence, making it significant for developing intelligent machine tools.

Machine tools, often referred to as the "mother machines" of the manufacturing industry, are crucial in developing smart manufacturing and are increasingly becoming more intelligent. Digital twin technology can promote machine tool intelligence and has attracted considerable research interest. However, there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence. Herein, digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin. The review then delves into state-of-the-art digital twin modeling-enabled machine tool intelligence, examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling. Additionally, it highlights three bottleneck issues facing the field. Considering these problems, the architecture of a digital twin machine tool (DTMT) is proposed, and three key technologies are expounded in detail: Data perception and fusion technology, mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology, and dynamic optimization and collaborative control technology for multilevel parameters. Finally, future research directions for the DTMT are discussed. This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence, making it significant for developing intelligent machine tools. [ABSTRACT FROM AUTHOR]

During the polishing process of complex curved PMMA parts, the polishing force is an important factor affecting the surface quality and optical performance. In this paper, a force-controlled polishing device integrated into a machining center to maintain the polishing force is investigated. In order to achieve the real-time active control of the polishing force, the linear voice coil motor and force sensors are used for motion and measurement. A compact structure was designed to couple the linear motion of the voice coil motor with the rotation for polishing. The force-controlled polishing system with a high real-time hardware architecture was developed to perform complex curved polishing path movement with precise force control. Next, the polishing force between the device and the workpiece was analyzed to obtain the mathematical model of the device. Considering the impact during the approaching phase of polishing, a fuzzy PI controller was proposed to reduce the overshoot and response time. To implement the control method, the controller model was established on Simulink and the control system was developed based on TwinCAT 3 software with real-time computing capability. Finally, a polishing experiment involving a complex curved PMMA part was conducted by a force-controlled polishing device integrated into a five-axis machining center. The results show that the device can effectively maintain the polishing force to improve surface quality and optical performance. [ABSTRACT FROM AUTHOR]

Thermal error is an essential factor affecting the processing accuracy of machine tools. Achieving accurate modeling of thermal errors is an essential means to control accuracy. However, the current method for selecting temperature measuring points does not consider the formation mechanism of thermal error, and the employed thermal error models suffer from poor accuracy or low efficiency. To this end, a novel method for selecting key temperature measuring points (KTMPs) is proposed in this paper, and a Transformer-based thermal error model is also established. Firstly, the impacts of thermal offset of various sub-assemblies on thermal errors are analyzed by developing a high-precision thermal behavior model of the machine tool. The selection of KTMPs on the machine tool is guided by the generation of thermal error. Then, the multi-head attention mechanism of the Transformer is employed to acquire global features of the temperature sequence while ensuring parallel computation, which improves operational efficiency. To enhance the performance of Transformer in thermal error modeling, adaptive modifications are made to aspects such as input encoding, activation functions, and regressor. Finally, the Transformer-based model is compared with the widely used long short-term memory (LSTM) and recurrent neural network (RNN) models. The results show that the Transformer-based model achieved lower root mean square error (RMSE) values of 0.458 µm, 0.507 µm, and 1.985 µm for the X-, Y-, and Z-axis predictions, respectively, and the Transformer-based model consumes less time. The results also indirectly validate the correctness of the proposed method for selecting KTMPs. [ABSTRACT FROM AUTHOR]

Accurate power consumption models are the basis for improving energy efficiency of machine tools. The acquisition of energy consumption characteristics of different machine tools requires a large number of calibration experiments, which leads to low modelling efficiency. This paper proposes a rapid modelling method using transfer-learning to obtain the power consumption model of the target machine tool. After obtaining the power consumption model of the source machine tool through detailed experiments, this method only needs a few experiments to obtain the power consumption model of the target machine tool, which greatly improves the modelling efficiency, and the method is experimentally verified on different machine tools. [ABSTRACT FROM AUTHOR]

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 %.

The feed system is an important part of CNC machine tools, and its condition monitoring is mostly based on vibration signals measured by sensors. To remove the noise mixed in the signal, a joint denoising method based on variational mode decomposition (VMD), simple correlation analysis (SCA) and translation invariant wavelet (TIW) denoising is proposed in this paper. Firstly, the VMD parameters are adjusted adaptively by the Aquila Optimizer (AO) to meet the demand of signal decomposition from different components of feed system. Then, the intrinsic mode functions (IMFs) obtained after VMD processing are reconstructed based on correlation analysis to eliminate irrelevant information. Finally, the reconstructed signal is denoised by translation invariant wavelet to obtain the denoised signal. The feasibility and universality of the joint analysis method are verified by the denoising test of simulation signals and measured signals from a certain machine feed system. The results show that the joint analysis method has better denoising effect than some general denoising method, and it can satisfy the denoising requirement when processing signals from different components. Besides, the proposed method has the ability of adaptive denoising. Compared with other optimization algorithms, AO algorithm has better optimization accuracy and efficiency, which can provide good support for the universality of the joint analysis denoising method.

Machine tool accuracy is greatly influenced by geometric and thermal errors that cause positioning deviations within its working volume. Conventionally, these two error sources are treated separately, with distinct procedures employed for their characterization and correction. This research proposes a unified volumetric error compensation approach in terms of a calibration procedure and error compensation model, which considers geometric and thermal errors as a single error source that exhibits temporal variation primarily due to changes in the machine’s thermal state. Building upon previous works that introduced a fully automated volumetric calibration procedure capable of characterizing the variation in volumetric error over time, this study extends this methodology, incorporating multiple temperature sensors distributed throughout the machine and generating a digital twin based on a volumetric error compensation model capable of predicting and compensating for the volumetric error over time at any point in the workspace, using temperature measurements and axis positions as inputs. This methodology is applied to a medium-sized milling machine tool. The digital twin is trained and validated on volumetric calibration tests, wherein various controlled heat sources are employed to induce thermal variations while measuring the temperatures in the machine.

According to ISO 230-3, the linear axis includes six thermal error motions. Measuring and modeling multi-position full-term thermal errors in linear axis are challenging and essential since they are position-dependent and time-varying. This study presents for the first time a method for modeling and predicting the full-term thermal error of linear axis with multi-positions. Firstly, the full-term thermal error of the linear axis was measured in real-time. The nonlinearity and time delay of the thermal deformation of the screw are analyzed based on the heat transfer theory. Then, a multidimensional spatio-temporal graph convolution-attention mechanism (MSTGCN-A) model is proposed to model the full-term thermal error at multi-positions on the linear axis. The model adaptively learns the adjacency relationship of nodes and fuses the spatial information of temperature points without screening temperature-sensitive points. GRU-CNN, MTCN-A, and BiLSTM-CNN are also used as comparative models to verify the accuracy of the models. Under varying operating conditions, the proposed model outperforms the comparative model that requires temperature-sensitive point selection. [ABSTRACT FROM AUTHOR]

The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine's tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine's kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached. [ABSTRACT FROM AUTHOR]

Machine tools need to possess excellent positioning accuracy to meet increasingly stringent dimensional tolerances and geometric specifications for producing parts. The accuracy is significantly affected by distortions caused by irregular thermal distribution throughout the machine's structure, with the motorized spindle being a major source of heat. In this study, through finite element analysis, the influence of the spindle's heating on other machine components is examined, revealing that the air inside the protection cover acts as one of the main mediums for heat transfer from the spindle to other components far from the spindle. A thermal error compensation model is proposed, considering the in-series heat transfer through the air inside the protection cover, leading to a higher-order thermal expansion curve. Additionally, the thermal errors caused by ball screws are addressed using a first-order exponential model. Testing the two models on a CNC turning center shows a considerable reduction in radial thermal error when combined with inputs from the CNC and temperature sensors strategically placed on the machine. [ABSTRACT FROM AUTHOR]

Energy consumption is an important factor in the operation of machine tools. Recent studies showed that in average, 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. [ABSTRACT FROM AUTHOR]

This article presents a spindle system with an active bearing support for which the author proposed a control system based on a PID controller. This type of solution improves the mechanical properties of this spindle by changing the stiffness, which results in a wider range of permissible rotational speeds, lower power losses and an increased resistance to vibrations of the entire system. This work describes the mechanism of changing the bearing preload. During tests on a real test stand, the correct functioning of the preload control system was checked for various dynamic conditions. In this work, the author describes the controller used to work in a feedback system, explains how the settings for this controller were selected and their optimization. The assessment of the correct operation of the controller is based on minimizing the amplitude of forced vibrations for the front spindle end by actively controlling the preload of the bearings. This publication is intended to present one of the ways to solve the important problem of limiting the maximum values of the vibration amplitude in the spindle system for its wide range of operating conditions. It also shows how important its optimal settings are in control systems. [ABSTRACT FROM AUTHOR]

Machine tool volumetric compliance quantifies the deflection of the tool tip relative to the workpiece under the effect of a mutual force. Its knowledge can help to virtually monitor tool path errors. This study, conducted under static conditions, aims to establish a model for directly predicting static translational volumetric errors caused by compliance from machine tool CNC feed motor torque outputs without the need to calculate the tool tip disturbance force. Static translational volumetric compliance was measured as proposed in ISO 230-1 in the X, Y and Z directions. The friction torques in each direction were modelled as a function of volumetric errors using Dahl's theory with some adjustments. The friction torque variations were detected. The final model links feed motor torques directly to static translational volumetric errors. With the help of an exponential term, the model proposed has a better performance on capturing the friction variations at the motion starting and movement reversal points. The static translational volumetric compliance models had R2 adj values of 0.999, 0.997, and 0.997 in the X, Y and Z directions, respectively. The RMSE with the best starting estimates on the testing dataset for predicting the static translational volumetric errors directly from the feed motor torques along the X-, Y- and Z-axis were 4.9 μm, 6.6 μm and 2.8 μm under maximum absolute volumetric errors values of 200 μm, 200 μm, and 50 μm, respectively. [ABSTRACT FROM AUTHOR]

The deformation of press bolster plates under load can significantly impact the quality of manufactured workpieces. Consequently, press manufacturers are usually obliged to provide the metric proof of the permissible deformation values during the press commissioning process. Unfortunately, the existing measurement methods for determining bolster plate deformations present flaws in the measurement accuracy, the application flexibility, the metrological effort, and other aspects. These issues have been addressed through the development of a new measurement method using multiple inclination profiles on the surface of the measured object. Based on the difference in the inclination between the unloaded and loaded states, the method approximates the inclination surface and calculates the deformation of the measured object through integration. The measurement method was first used for commissioning tests of forming presses. A comparison with the results obtained with a measurement frame equipped with high-resolution measurement probes demonstrates an accuracy of the new method of less than 20 µm. [ABSTRACT FROM AUTHOR]

These days, most machine tools are interlocked by an enclosure for safety control. At that time, internal heat generation in machine tools first causes thermal deformation of the machine structure, which reduces the machining accuracy of the workpiece. Furthermore, the internal heat generation heats the air inside the enclosure, causing a heat build-up phenomenon, and the trapped heat causes re-thermal deformation of the machine tool structure. As a result, machine tools with enclosures are subject to extremely complex thermal deformation. On the other hand, we would like to use FEM thermal simulation to study thermal deformation countermeasures for machine tools with enclosures at the design stage, but it is difficult to analyse the heat build-up phenomenon using conventional FEM thermal simulation. In this research, the new FEM thermal simulation technology for the heat build-up phenomenon was developed and heat build-up phenomenon in a CNC lathe with enclosure was calculated using the proposed FEM simulation technology. As a result, it had been concluded that the proposed FEM simulation could calculate with high accuracy for the phenomenon of heat build-up in a CNC lathe with enclosure, and the proposed technology is very effective in the design. [ABSTRACT FROM AUTHOR]

Concept of machine tool’s column in the appearance of vertical bionic console is proposed. Console was 3D-transformed from the shape of legacy oak-like structure – Eddystone lighthouse. It is proposed to create the variable-section column with “layer-by-layer” technics from the polymer concrete.FEA shows high stiffness of the column. It increases from 1500 to 11000 N/μm from top to bottom along vertical guideways. Such stiffness needs only 19.3 ton of concrete for the instance of 4.5 m height’s column. FEMmodelling pointed out rather high eigenmode’s frequencies (˃49 Hz). FRF is appropriate for pre-resonance heavy cutting as for post-resonance finishing. Proposed bionic-shape column is perspective as for stand-alone variant so for portal composition created by two opposite columns.

This paper introduces a complete real-time algorithm, where the chatter is detected and eliminated by spindle speed manipulation via the chatter energy feedback calculated from the axis encoder measurement. The proposed method does not require profound knowledge of the machining dynamics; instead, the entire algorithm exploits the fact that milling vibrations consist of forced vibrations at spindle speed harmonics and chatter vibrations that are close to one of the natural modes, with sidebands which are spread at the multiples of spindle speed frequency above and below the chatter frequency. The developed algorithm is able to identify the amplitude, phase and frequency of all the harmonics constituting the periodic forced and chatter vibrations. The key challenge is to select dominant chatter frequencies for the calculation of a robust and accurate chatter energy ratio feedback; this is achieved by utilizing the frequency estimation variance of EKF as a novel chatter indicator. Based on the chatter energy ratio feedback, the controller overrides the spindle speed in order to suppress the chatter energy below a particular threshold value. The varying spindle speed challenge is handled by updating the state transition matrices of the Kalman filters and real-time calculation of the band-pass filter coefficients, based on the derived discrete time transfer functions. The developed algorithm is tested on a Deckel FP5cc CNC which is in-house retrofitted and has a PC-based controller for the real-time application of the proposed algorithm. It is shown that the real-time chatter frequency and amplitude estimates are compatible with off-line FFT analysis, and chatter can be successfully eliminated by energy feedback.

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.

Machine tools with high dynamic performance are in high demand currently in the manufacturing industry due to their high machining accuracy and efficiency in manufacturing complex parts. Though the S-shaped test piece shows much improvement over the standard cone-frustum test piece even under the non-machining dynamic performance test, however, the S-shaped test piece is still under development and there has been a call for a new test piece better than it. Therefore, the Scone test piece, which combines the geometric and the kinematic features of the S-shaped and the cone-frustum test pieces, has been proposed; this work objectively validates the dynamic performance of the five-axis machine tool based on the trajectory of the Scone test piece. The RTCP trajectory test is used to build the R-test process which involves the simultaneous movement of all five axes, and the results of the S-shaped and the cone-frustum part trajectory test are compared to that of the Scone part; an experiment was conducted on an industrial five-axis machine tool, with a tilting rotary table, to verify the simulation results. According to the results obtained, the Scone test is more sensitive to the dynamic tracking errors of the machine tool than the cone-frustum and the S-shaped tests under the same conditions; hence, the Scone trajectory based on the kinematic test can better reflect the dynamic performance of the five-axis machine tool. [ABSTRACT FROM AUTHOR]

The thermal error of a machine tool can be regarded as an important index of machining accuracy. Such an error is reproducible and stable, meaning that machine tools can be easily maintained by thermal compensation, ensuring a long-term good machining quality. In this study, reliability engineering was applied to increase the mean time between failures (MTBF) of a cooling system to maintain the structure temperature and accuracy, and the MTBF was increased from 3787 h to more than 23041 h. In addition, the Taguchi method was used with the finite element method and multivariate regression analysis to obtain the optimal cooling conditions to reduce thermal deformation and stabilize the structure temperature. The variation of the temperature of the machine tool structure was improved from ±0.517 to ±0.367 ℃, a reduction of 29%. [ABSTRACT FROM AUTHOR]

After the machine tool works continuously, the temperature of the hydraulic system continues to rise, which affects the work efficiency of the machine tool. Therefore, it is very important to control the temperature within a reasonable range. This paper proposes an improved scheme to replace a single fan with dual fans to improve the heat dissipation capacity of the radiator. Starting from the principle of heat exchange between oil and air, the relationship between the oil temperature and the wind speed on the face of the heat exchanger is derived, and the theoretical basis of the cooling system is given. Combined with logic control, the fan has the advantages of fast action, high efficiency and low energy consumption, which ensures the efficient and reliable operation of the machine tool. A one-dimensional simulation model of the thermal hydraulic system is established, and the heat generation and heat dissipation power of each element are calculated. Among them, the heat dissipation of the radiator is the largest, accounting for about 55% of the total heat dissipation. The experimental results show that the optimal fan speed is 3200 r/min and the flow rate is 0.2 m3/s at 26 °C. The thermal balance temperature of the hydraulic system is reduced from the original 65 °C to 58 °C, and its cooling capacity meets the requirements of a high-altitude and high-temperature environment. [ABSTRACT FROM AUTHOR]

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis's acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically. [ABSTRACT FROM AUTHOR]

Digitisation offers manufacturing companies new opportunities to improve their operations and competitiveness in the market by unleashing potentialities related to real-time monitoring and control of operating machines. Through condition-based and predictive maintenance, the knowledge about the health state and probability of failure of the machines is improved for better decision-making. Amongst them, CNC machine tools do represent a complex case from a maintenance viewpoint as their operations are ever-changing and their high reliability brings to a lack, or limited set, of run-to-failure data. To address the problem, the research work proposes an operations-aware novelty detection framework for CNC machine tools based on already-in-place controllers. The framework is based on statistical modelling of the behaviour of the machine tools, namely through gradient boosting regression and Gaussian mixture models, to identify the health state considering varying operations through time. The proposed solution is verified on sixteen multi-axis CNC machine tools in a large manufacturing company. The results show that the proposed solution can effectively support maintenance decisions by informing on the health states while discerning between varying operations and abnormal/faulty states of interest. This solution represents a brick in a cloud-edge-based industrial information system stack that can be further developed for shop floor-integrated decision-making. [ABSTRACT FROM AUTHOR]

The time-varying thermal error of the horizontal boring and milling machine tool (HBMMT) directly affects the performance of high-end parts. The time-varying thermal error is in the axial direction due to the radial symmetry of the spindle box being good. The spindle axial time-varying thermal error compensation method based on edge computing was studied for reducing the effect of the spindle axial time-varying thermal error of HBMMT on the accuracy consistency of parts. The spindle axial time-varying thermal error model was established based on the speed and acceleration of spindle temperature change. The error compensation value was acquired by edge computing, and the error was compensated based on the principle of coordinate origin offset. The error fluctuation range with compensation was reduced, and the accuracy stability of HBMMT was greatly improved. [ABSTRACT FROM AUTHOR]

The sequential multilateration principle is often adopted in geometric error measurement of CNC machine tools. To identify the geometric errors, a single laser tracker is placed at different positions to measure the length between the target point and the laser tracker. However, the measurement of each laser tracker position is not simultaneous and measurement accuracy is mainly subject to positioning repeatability of the machine tool. This paper attempts to evaluate the measurement uncertainty of geometric errors caused by the positioning repeatability of the machine tool and the laser tracker spatial length measurement error based on the Monte Carlo method. Firstly, a direct identification method for geometric errors of CNC machine tools based on geometric error evaluation constraints is introduced, combined with the geometric error model of a three-axis machine tool. Moreover, uncertainty contributors caused by the repeatability of positioning of numerically controlled axes of the machine tool and the laser length measurement error are analyzed. The measurement uncertainty of the geometric error and the volumetric positioning error is evaluated with the Monte Carlo method. Finally, geometric error measurement and verification experiments are conducted. The results show that the maximum volumetric positioning error of the machine tool is 84.1 μm and the expanded uncertainty is 5.8 μm (�� = 2). The correctness of the geometric error measurement and uncertainty evaluation method proposed in this paper is verified compared with the direct geometric error measurement methods.

Abstract Assembly geometric error as a part of the machine tool system errors has a significant influence on the machining accuracy of the multi-axis machine tool. And it cannot be eliminated due to the error propagation of components in the assembly process, which is generally non-uniformly distributed in the whole working space. A comprehensive expression model for assembly geometric error is greatly helpful for machining quality control of machine tools to meet the demand for machining accuracy in practice. However, the expression ranges based on the standard quasi-static expression model for assembly geometric errors are far less than those needed in the whole working space of the multi-axis machine tool. To address this issue, a modeling methodology based on the Jacobian-Torsor model is proposed to describe the spatially distributed geometric errors. Firstly, an improved kinematic Jacobian-Torsor model is developed to describe the relative movements such as translation and rotation motion between assembly bodies, respectively. Furthermore, based on the proposed kinematic Jacobian-Torsor model, a spatial expression of geometric errors for the multi-axis machine tool is given. And simulation and experimental verification are taken with the investigation of the spatial distribution of geometric errors on five four-axis machine tools. The results validate the effectiveness of the proposed kinematic Jacobian-Torsor model in dealing with the spatial expression of assembly geometric errors.