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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]

Modern life is marked with the extensive use of machine tools. The latter stand for high consumers of electrical energy which increase carbon emissions. For this reason, various solutions aiming to reduce energy consumption in industrial sector are elaborated. One solution lies in the use of economical materials for the spindle rotation system supported by active magnetic bearings (AMBs), which corresponds to a problem that has never been undertaken so far. A new conception for machine tool spindle rotation system is elaborated for sustainable design. The spindle material is composed of a carbon epoxy hollow shaft wrapped with a steel layer and supported by AMBs. A Finite Element Method (FEM) modeling of the spindle rotation system was carried out to assess its dynamic consumed energy and power during the acceleration and cutting phases. For an accurate estimation, the power consumed by the spindle system was calculated taking into account the dynamic behavior of the cutting force and losses generated by the bearings. A comparison between energies consumed by the proposed spindle system and the steel spindle system was enacted. [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]

Modal parameters (natural frequencies, mode shapes and modal damping) help to understand the dynamic behaviour of complex systems like machine tools. There are several approaches for finding the modal parameters. The Experimental Modal Analysis (EMA) has proven to be effective at standstill of a machine tool. The excitation, realized with impulse hammer or shaker, and excited responses at several locations are measured. Alternatively, the Operational Modal Analysis (OMA) can be deployed for finding the modal parameters during operation. Here, responses to excitation resulting from operation are only measured. The modal parameters are mathematically identified from the measured signals in both cases but with different methods. This paper discusses, to what extent both approaches (EMA and OMA) can lead to plausible identification of natural frequencies of a machine tool during milling. Concerning the EMA, attention is paid to capturing the excitation. Process forces can be assumed to be the most significant excitation. However, there are other excitation sources beside the process forces (e.g. drives, hydraulic and pneumatic aggregates), which are considered by this assumption to be a part of disturbances with consequence for the identification of the modal parameters. Regarding the OMA, attention is paid to the fact that the excitation is assumed to be broadband like the white noise. Unfortunately, this assumption does not match the characteristics of a real excitation. This paper contains the identification of natural frequencies of a machine tool during milling within both approaches. The achieved results are compared and discussed. [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]

This article deals with the issue of the automatic setting of machines on their base. The focus is on machines with a selfsupporting frame, freestanding, on more than three supports. The alignment uses the previously presented method of precise and repeatable machine placement on the foundation. As a result, the machine has the necessary parameters in terms of its geometry, and at the same time a defined weight distribution on the individual building elements is guaranteed. The paper presents a new method for automatic machine alignment using so-called "smart feet". Due to this, the process of machine alignment is clearly defined, repeatable and does not depend on the experience of the personnel performing the alignment. The presented algorithm makes it possible to automate the entire machine-setting process. The article includes the presentation of a machine placement on its base, which is used for testing and verification of the functionality of the alignment algorithm. [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.

The complexity of composite grinding movement, the variety of machining features and available grinding wheels, and the changing working conditions pose a challenge to the rapid programming of safe and efficient composite grinding procedure. The procedure optimisation plays an important role in solving this difficult problem. Therefore, a procedure optimisation method is proposed for composite grinding based on a Digital Twin (DT) system, which takes procedure time as optimisation objective to achieve high efficiency and ensures process rationality and safety by constructing corresponding constraint conditions. Moreover, the actual working conditions mapped into the DT system, such as workpiece parameters, machining requirements, grinding wheel parameters and status, machine tool motion position, and so on, are obtained to update the parameters involved in the optimisation model. And thus, the proposed method has the ability to timely find the optimal procedure under changing working conditions. In addition, a combination algorithm based on genetic algorithm (GA) and dynamic programming is proposed, which greatly reduces the search space of GA and realises the two-class co-optimisation of grinding wheel selection and procedure. Finally, the case study verifies the effectiveness of the proposed method to reduce procedure time and the dynamic response ability to changing working conditions. [ABSTRACT FROM AUTHOR]

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]

The performance of a machine tool is directly influenced by the characteristics of the floor, subsoil, and their interaction with the installed machine. Installing a machine tool in its operational environment poses a distinct challenge that bridges mechanical and civil engineering disciplines. This interdisciplinary issue is often overlooked within the individual separate disciplines. However, effectively addressing this challenge requires a comprehensive understanding of mechanical and civil engineering principles. To address this problem, the present study proposes a method for improved modeling of the dynamic properties of the machine tool by considering the foundation and the subsoil on which it is installed. The method is based on finite element modeling. Linear models of the system components and the connections between them were used. These, supplemented with damping expressed by complex stiffness, made it possible to determine the natural frequencies, mode shapes, and frequency response functions (based on which the transmissibilities were obtained). Based on the experimentally verified models of vertical and horizontal lathes, the sensitivity analysis aimed at estimating the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation was conducted. Thus, it was possible to identify those machine tool-support-foundation-subsoil system parameters that had the most significant impacts on the vibration's transmissibility. After analyzing the cases discussed, it became evident that the transmissibility of vibrations is primarily influenced by two key factors. First and foremost, the properties of the structural loop of the machine tool played a significant role. Additionally, the characteristics of the subsoil on which the foundation was situated emerged as a crucial determinant in the observed vibration transmissibility. [Display omitted] • A method of modeling the dynamic properties of machine tool-support-foundation-subsoil system was presented. • The method was presented for two types of lathes: vertical and horizontal, and two types of foundation floor and block. • The modeling method was experimentally verified. • The sensitivity analysis was conducted to estimate the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation for vertical and horizontal lathes. [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]

One of the most crucial parameters in the field of machine tool engineering is the stiffness of the machine tool spindle, as it has a direct impact on the precision and accuracy of machining. This paper presents a research on the effects of geometric parameters of capillary tubes, including diameter (dc) and length (lc), on the stiffness of hydrostatic bearings in machine tools. The oil pump pressure in this study was set at a range from 3 MPa to 5 MPa. The simulation results demonstrate the relationship between the capillary geometric parameters, pressure, and the stiffness of the hydrostatic spindle unit. These findings indicate a direct relationship between stiffness and pump pressure, with increases in pressure also raising lubricant temperature, which may impair cooling. The shape of the capillary directly affects the ratio of oil chamber pressure to pump pressure, which in turn affects hardness. The results of the study indicate that for spindles in machine tools, a dc ranging from 0.3 mm to 0.6 mm and a (lc /dc) ratio of between 20 and 100 are feasible based on the given stiffness requirement. It is possible to select an appropriate set of capillary parameters to achieve the best stiffness with specific requirements under the working conditions of hydrostatic bearings based on the results of the simulation analysis. [ABSTRACT FROM AUTHOR]

Large-size parts such as aircraft wing structures require a lot of hole machining for assembly between parts. As demand for hole positional accuracy, such as in determinant assembly (DA) concept, securing the positional accuracy of hole machining is becoming more important. Specifically, during the hole machining of large-size parts, significant errors occur due to thermal expansion according to temperature change of a machine tool and a workpiece. In this study, a technique for efficiently reducing the positional error of hole machining in large-size parts is proposed by compensating geometric errors and thermal errors of a machine tool and a workpiece. The thermal displacement error, representing changes in volume error due to temperature variations in the machine tool, is estimated by measuring the distance between the centers of two spheres on the master with low thermal expansion coefficient measured at the reference temperature (20 °C). The thermal expansion error, caused by variations in workpiece temperature, is estimated by assuming the temperature of the workpiece to be that of the cutting fluid just before machining. Hole machining errors are integrally compensated by considering the geometric error measured at the reference temperature and the thermal errors of the machine tool and the workpiece. In the verification experiment, the maximum error was improved by 60.2%, and it was confirmed that the tendency of the error was significantly reduced. [ABSTRACT FROM AUTHOR]

Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. [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]

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. [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]

Five-axis contouring errors left on the parts contributed by feed drive servos, and intra- and inter-axis geometric errors are predicted along the five-axis tool paths. Quasi-static geometric errors are measured and modeled in the machine's workspace. Servo tracking errors of the three translational and two rotary drives are measured during machining or predicted from the CNC model. All errors are projected to the tooltip using a generalized screw theory-based forward kinematic model. The proposed model is validated experimentally on a CNC machining center for two test parts. • Developed a predictive model combining volumetric and servo-induced errors in 5-axis machining. • Utilized screw theory for adaptable modeling across different five-axis machine configurations. • Measured 41 geometric error parameters with laser interferometer and ballbar. • Included error parameter decoupling and decontamination procedures. • Achieved an 87 % accuracy in predicting dimensional errors on machined parts. [ABSTRACT FROM AUTHOR]

The feed system is a typical mechatronics system, as well as motion control equipment, that serves as a fundamental component of CNC machine tools. Developing a precise mechatronic integrated model of the motion control of the feed system is a crucial step in achieving the digital twin and virtual machining. The extant literature extensively covers the dynamic modeling and identification of feed systems, the characterization and traceability of position deviations, and the study and optimization of motion control algorithms. Nevertheless, there is a deficiency in the analysis and optimization of the uncertainty pertaining to the motion control position of the feed system. This paper focuses on the positioning repeatability and considers the random position deviation that occurs during the movement of the feed system. The current research on positioning repeatability primarily concentrates on static factors such as geometric error and assembly error. Three primary causes of uncertainty in the motion control position of the feed system are identified: disturbance uncertainty, parameter uncertainty, and model uncertainty. Each component of uncertainty is thoroughly classified and studied based on the specific characteristics of the feed system. This study provides a comprehensive overview of the current data-driven control approaches used to address uncertainty in the feed system. It specifically focuses on the data-driven controller turning method, feedforward compensation method, and controller design method. The review concludes by discussing the latest research progress and limitations and suggests hopeful trends. Specifically, when conducting integrated modeling of the feed system, it is crucial to thoroughly account for the impact of stochastic processes. Additionally, the issue of switching between dynamic and static states in the dynamic model should be taken into consideration. Simultaneously, it is necessary to examine a more suitable approach that integrates the system model with the uncertainty characteristics to develop a more robust optimization strategy for controlling and efficiently managing the spread of position deviation in the feed system. • A comprehensive review of uncertainty for machine tool feed systems. • This work presents a new viewpoint on the uncertainty in the position control of feed system's motion. • The full description encompasses the multi-source uncertainty in feed system. • This study reviews data-driven control algorithms for feed systems uncertainty. [ABSTRACT FROM AUTHOR]

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. • An indirect compensation model of machine tool (MT) thermal errors distinguishing between the heating and cooling phases is developed. The model includes the influence of ambient temperature change. • Granular structure of transfer-function-based model enables possibility of modifying the magnitude of the approximation value and of adapting the time constant of the simulation to the approximated phenomenon. • The thermal error compensation strategy based on post-process updating of model parameters is demonstrated on heavy-duty milling machine and applied on medium-size milling centre. • Spatial thermal errors, MT equipment modularity and process liquid presence are considered in post-process modelling effort. • The necessity of quality and correct input signals to the compensation model is emphasized. • Only the measured residual deformations in technological pauses are used to update the parameters of the model for repeated production. [ABSTRACT FROM AUTHOR]

This paper outlines the evaluation methodology for a Telescopic Simultaneous Ballbar (TSB) developed for distance measurement using an interferometer as a reference equipment. The procedure is executed in a 3-axis machine tool, one of the final applications of the TSB that is machine tool volumetric verification. Two distinct evaluation techniques are devised, with the new virtual line approach enabling an assessment of the TSB's performance within an extended verification volume and requiring only a single alignment position for the interferometer. The measurement repeatability of the equipment is assessed and its measurement uncertainty is estimated in workshop conditions. The results reveal the flexibility of the TSB measurement process similar to that of a laser tracker and the degree of measurement accuracy, which shows values comparable to the interferometer. Consequently, the TSB could facilitate faster and more accurate machine tool verification and calibration compared to existing systems. • Method for evaluating a Telescopic Simultaneous Ballbar using an interferometer is proposed. • Simultaneous operation of three interferometric lines for machine tool volumetric verification. • The virtual lines enable TSB verification in a wider MT volume using single alignment positions. • Standard deviation of the TSB distance measurement error is below 0.6 μm. • TSB measurement uncertainty in workshop conditions shows values below 1.3 μm. [ABSTRACT FROM AUTHOR]

The manufacturing industry solves mainly a double problem of high productivity rate and quality of product. This dual task depends on the machining regimes which changes have limitations conditioned by the quality of products machined and the reliability of the machine tool units. Studies of machining processes show the productivity rate of machine tools depends on the cutting speed and reliability of the cutting tools. An increase in the cutting speed decreases the tool life and machining time. The optimal balance of the cutting speed and tool life yields the maximal productivity rate of the machine tool. Recommended machining regimens for cutting tools' obtained at laboratory conditions do not consider the specificity of work of machine tool units in the manufacturing environment. The publications related to the optimization of machining processes do not consider the specificity of muti-blade cutting tools' work and the reliability of machine tool units. The milling machining processes implement the multi-cutter tool, in which cutters are involved serially. The specificity of the milling tool operation is not fully described in publications. The presented research paper considers a mathematical model for optimal cutting speed by the criterion of the maximal productivity rate for the face milling process. [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]

Additive manufacturing creates parts by depositing a preform, typically layer by layer. Subtractive manufacturing involves removing material from a preform to create parts. Hybrid machine tools combine both additive and subtractive processes in the same workspace. They can be used to create parts that meet functional tolerance and surface finish requirements, or to create features that are difficult to produce using additive or subtractive processes alone. This paper describes hybrid metal additive/subtractive machine tools. It covers design considerations, sensors and controls, process management, programming and software, and the impact on the design space. It also identifies future research challenges. [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]

With the help of plane geometric algebra, we see volumetric errors as pure geometric objects. We use this identification to expand errors with respect to Abbe's principle into the whole working space with respect to some additional conditions. We show that geometric algebra helps us to understand errors in kinematics chains. We demonstrate our approach on a model example. [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]