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This study proposes a framework to analyze the interaction between geopolitics and the Middle-Technology Trap, drawing on evidence from China's CNC machine-tool industry. Qualitative methods such as interviews have been utilized to identify geopolitical variables that have driven the development of the Chinese CNC machine tools industry. To explore the combined effects of the Middle-Technology Trap and the intense competition between China and the West, this article focuses on an industry that has been significantly influenced by the economic decoupling between China and the US, a phenomenon deeply rooted in recent great power competition. While several industries may illustrate the new status quo of the Middle-Technology Trap, the CNC machine-tool industry is particularly exemplary due to its close interrelation with geopolitics. Accordingly, this study presents a catch-up case that incorporates technology-oriented aspects and factors from strategic competition, underlining three scenarios that geopolitics could influence the result of addresing the Middle-Technology Trap, respectively technological decoupling, the relocation of supply chain, and industrial policy. [ABSTRACT FROM AUTHOR]

During the thermal design of CNC machine tool feed systems, it is essential to obtain the thermal characteristics of components to improve the machining precision and reduce research and development costs. However, the traditional mechanism and the data-driven models with single structural information input generally cannot meet the actual prediction requirements. In this paper, a method based on heterogeneous correlation information fusion was proposed, aiming to predict the temperature increase characteristics of Multi-Component Feed System (MCFS). Firstly, the heterogeneous correlation temperature information of components for feed system was collected and calibrated via the temperature sensor (contact type) and thermal imager (non-contact type), respectively. Then, the heterogeneous information features are extracted from various aspects, and the fusion information feature matrix is constructed based on correlation screening and principal component analysis. Finally, the ensemble learning method based on Extreme Gradient Boosting was used to establish the temperature rise prediction model, and the Aquila Optimizer (AO) and three-fold cross-test were used to improve the accuracy and stability of the obtained prediction model. To verify the effectiveness of this method, a testing platform for MCFS heterogeneous information collection and testing was built. Experiments and data collection were carried out on a specialized machine tool under different working conditions. The experimental results have shown that the proposed temperature rise model can achieve accurate prediction of different components of feed system under various working conditions. Compared to the traditional prediction model with a single data structure information input, the information acquisition is more comprehensive, and the error is significantly reduced. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of traditional spindle assembly methods, which rely on trial-and-error approaches, hindering new product development evaluation. A stacking ensemble model is proposed to predict the assembly quality variation of machine tool spindles. The model uses data from 925 single-spindle inspections and extracts evaluation metrics from multiple domains to extract valuable information. Feature selection is performed using a correlation model to identify important features, and various lightweight supervised learning algorithms are applied to analyze the data. To further enhance the model's performance, a stacking ensemble approach is proposed, which combines algorithms. The results demonstrate that the proposed stacking ensemble model is an effective approach for predicting the assembly quality variation of machine tool spindles, using the data available. The proposed ensemble model enhances quality control processes in spindle assembly, enabling practitioners to identify key features and predict machine tool spindle assembly quality variations more accurately. [ABSTRACT FROM AUTHOR]

The digital twin is driving the machine manufacturing and processing workshop to change in the direction of digital intelligence and service. Aiming at the application requirements of virtual simulation monitoring of typical CNC machine tools for the unified interaction and integration of processing and production process data, this paper proposes a development architecture of virtual simulation monitoring and processing process optimization system for CNC machine tools that integrate data, model, communication, and optimization. The data semantic format and data communication are normalized by designing the OPC UA information model of CNC machine tools, modular construction of a three-dimensional digital model, and interactive mapping technology of OPC UA server address space. Virtual simulation visualization and monitoring of CNC machine tools are realized by integrating synchronous simulation modeling, collision detection, and viewpoint control technologies. Building upon this foundation, the control process of the CNC machine tool machining cell is optimized using ECRS and lean production methods. The application focuses on a typical flexible manufacturing cell (FMC) in a machine tool processing and manufacturing workshop. The development of the virtual simulation visualization monitoring system for FMC addresses challenges such as heterogeneous data interaction, sharing, and integration difficulties across multiple heterogeneous equipment. The system successfully fulfills all required functions, and the optimization of the CNC machine tool machining unit's control process has enhanced equipment utilization and productivity. This solution effectively supports the realization of intelligent manufacturing services, including standardized data-driven digital twins. [ABSTRACT FROM AUTHOR]

Traditional data-driven fault diagnosis methods require a massive amount of data to train diagnosis models. However, the complex and coupled structure of CNC machine tools makes it difficult to obtain enough usable data. Current data generation methods ignore actual operating conditions and have imbalance, which reduces the accuracy of fault diagnosis. To tackle these problems, this paper presents a digital twin-driven fault diagnosis method for CNC machine tools. Firstly, a digital twin model of a CNC machine tool is established and validated. Then, a twin model library is constructed to include multiple twin models under different fault status. A model data fusion method is presented, using the decision tree algorithm Classification and Regression Tree (CART) to train a model selector and actual sensor data as input to select the optimal model from the library and realize fault diagnosis with the model. Finally, taking the CNC machine tool spindle as an example, the stiffness deterioration of the spindle during operation is effectively diagnosed, which verifies the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the intricate mechanism of Abbe error's impact on spatial accuracy, the accumulated Abbe error in the traditional spatial accuracy model is difficult to identify and eliminate, resulting in reduced modeling accuracy and limiting accuracy improvement. This paper proposes a data-driven spatial accuracy modeling approach for machine tools under the influence of Abbe error, using a three-axis coupling measurement optical path to directly measure the comprehensive spatial accuracy data of the machine tool containing Abbe error. Firstly, to effectively identify Abbe error in the comprehensive spatial accuracy, an Abbe error quantization function is established, which analyzes its formation mechanism in the measurement process to eliminate Abbe error in the machine tool's spatial accuracy data. Furthermore, to address the issue of limited data samples after eliminating Abbe error, data samples are extended based on the degradation mechanism of machine tool spatial accuracy at different coordinate positions, resulting in a high-precision spatial error model for machine tools. Finally, the experiment is conducted on a three-axis CNC machine tool, with a model accuracy of over 95%. The example application verification demonstrates that the developed model scheme is feasible and effective. [ABSTRACT FROM AUTHOR]

The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical measurement system to measure the positioning offset error of a rotating shaft. This study set α value to be the maximum allowed ridge parameter and named the method Critical–α, allowing an appropriate ridge parameter for use with a ridge regression model to be quickly selected, and integrated into a backward elimination procedure to achieve ridge regression thermal error compensation modeling. The study considered three methods for selecting temperature variable combinations. The first method requires the use of all sensors, the second method selects the combination with the minimum mean-square error, and the third method considers the effect of diminishing returns. The ridge regression method, which considers the diminishing returns effect, is known as the "R–DR model." The R–DR model is applied to the CNC machine used in this study to reduce the maximum peak-to-peak error on the Y-axis from 54.41 to 13.94 µm using only three temperature sensors, and on the Z-axis from 73.59 to 10.12 µm using four temperature sensors. Therefore, the R–DR model has two advantages: high precision (post-compensation peak-to-peak thermal error of less than 14 µm) and fewer temperature sensors, thereby allowing the thermal error compensation modeling method to demonstrate high engineering applicability and accuracy. [ABSTRACT FROM AUTHOR]

Spindle thermal error is a major factor affecting the machining accuracy of machine tools. The time-consuming experiments required to model the thermal error of each machine tool spindle makes exhaustive studies difficult. Therefore, it is critically important to develop a transferable spindle thermal error prediction model that ensures robustness and accuracy, which provides theoretical guidance for the thermal error compensation and accuracy improvement of different machine tools. To achieve this, this paper proposes a migratory prediction method based on spatial–temporal axial attention bidirectional long short-term memory (axial attention-BiLSTM) network to predict the thermal error of computer numerical control (CNC) machine tool spindles under varying working conditions. By analyzing the mechanism of spindle thermal error generation, the spindle system is divided into multiple temperature regions, and the importance of each region is automatically determined by using the spatial attention mechanism. At the same time, considering the historical dependence of thermal error, BiLSTM is used to fuse the previous and following time series information and determine the weights of different time steps by temporal attention mechanism to strengthen the times series memory of thermal error prediction. An axial attention-BiLSTM model for thermal error prediction is built based on the spindle test bench. Compared with the BiLSTM and long short-term memory (LSTM) models, this model shows better and more stable prediction performance when migrated to various working conditions of horizontal CNC grinding machines. [ABSTRACT FROM AUTHOR]

Fault diagnosis is a crucial task to guarantee reliability and reduce losses and production cost in smart machining in the Industry 4.0. In order to do so, it is necessary to implement a fault diagnoser that does not use a large amount of memory and which is capable of detecting the occurrence of a fault in a fast manner. In this paper, we propose a timed automaton model, called Timed Automaton with Timing Intervals and Outputs (TATIO), which is suitable for fault diagnosis. The TATIO model represents a typical drilling process on a CNC machine which uses an ISO code for programming and is obtained by identification of timing intervals associated with the time instants that the events occur in the system. The fault diagnoser uses only the spindle power and Z position read directly from the system controller and does not need any additional sensor. The proposed diagnoser is capable of detecting the use of a wrong cutting speed for a specific workpiece material, the use of a material different from the expected, and the occurrence of a wrong sequence of events executed by the system. [ABSTRACT FROM AUTHOR]

Due to the complex structure and diverse functions of the CNC machine tool, there may be structural interactions between contemporaneous meta-action (CMA), which will lead to reliability coupling of the CMA. And these coupling relationships have a great influence on the reliability comprehensive evaluation of the CNC machine tool. Therefore, in order to analyze the reliability coupling mechanism of T-rotation-type CMA, based on the meta-action theory, this paper first introduces the corresponding concepts vis-à-vis CMA and analyzes the reliability coupling reasons of T-rotation-type CMA. Then, the bearing models, stiffness models, and dynamic models of gear 1 rotation meta-action unit under frame offset and frame deflection are established. What's more, the interval theory is utilized to construct the reliability coupling model of T-rotation-type CMA. Finally, by comparison and simulation analyses, the rationality and effectiveness of the proposed stiffness model and reliability model are verified, and the reliability coupling mechanism of T-rotation-type CMA under frame deformation is analyzed. [ABSTRACT FROM AUTHOR]

The quality of CNC (computer numerical control) machine tool is the key to the survival of manufacturing enterprises. How to comprehensively and accurately evaluate its quality is very important for the timely discovery and improvement of machine tool defects. The current research either only evaluates a certain quality characteristic of CNC machine tool, or ignores the dynamic characteristics of CNC machine tool quality, which reduces the accuracy of quality analysis results. To address these problems, firstly, RMS (reliability, maintainability, supportability) was taken as the comprehensive evaluation indexes of CNC machine tool quality characteristics to improve the comprehensiveness of the analysis. Secondly, considering the dynamic characteristics of machine tool reliability, the ADC (availability, dependability, capability) model was improved to obtain a comprehensive quality evaluation index function ERMS with time-varying characteristics. Finally, the characteristics of ERMS were analyzed, and its evaluation criteria were also given. Several CNC machine tools made in China were tracked, and the collected fault data were analyzed by the proposed method. The ERMS of the machine tools was obtained, and its characteristics were analyzed. The results verify the correctness of the proposed method, which provides theoretical basis and technical support for the improvement of the quality of CNC machine tool and the market competitiveness of enterprises. [ABSTRACT FROM AUTHOR]

Computer numerical control (CNC) machine tool is the foundation of the equipment manufacturing industry, and its technical level is an important indicator to measure the development level of a country's equipment manufacturing industry. Tool wear during machining has a great impact on the important performance indicators of CNC machine tools, such as machining accuracy, machining efficiency and reliability. Tool wear monitoring is of great significance to improve the machining efficiency, machining accuracy and reliability of CNC machine tools. Multidomain features (time domain, frequency domain and time–frequency domain) can accurately characterise the degree of tool wear. However, manual feature fusion is time consuming and prevents the improvement of monitoring accuracy. A new tool wear prediction method based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network is proposed to solve these problems. In this method, multidomain features of cutting force and vibration signals are extracted and recombined into feature tensors. The proposed hypercomplex position encoding and high-dimensional self-attention mechanism are used to calculate the new representation of input feature tensor, which emphasizes the tool wear sensitive information and suppresses large area background noise. The designed depth-wise separable convolutional neural network is used to adaptively extract high-level features that can characterise tool wear from the new representation, and the tool wear is predicted automatically. The proposed method is verified on three sets of tool run-to-failure data sets of three-flute ball nose cemented carbide tool in machining centre. Experimental results show that the prediction accuracy of the proposed method is remarkably higher than other state-of-art methods. Therefore, the proposed tool wear prediction method is beneficial to improve the prediction accuracy and provide effective guidance for decision making in processing. [ABSTRACT FROM AUTHOR]

Error compensation is an effective method to guarantee the machining accuracy of machine tools. This paper proposes a method for geometric error identification and compensation of CNC machine tools based on volumetric diagonal error measurements. A geometric error model for CNC machine tools was established. Thereafter, the error measurement principle of the laser tracker was established. Based on this characteristic, error identification equations used to describe the position-dependent geometric errors were derived based on polynomials. A regularisation method was then selected to identify geometric error parameters. The identified geometric errors could be substituted in the space compensation module for error compensation. This paper takes a horizontal three-axis machining centre as an example to illustrate the proposed method. Results of the experiment reveal that the error identification and compensation method is convenient and effective in reducing volumetric diagonal errors. Moreover, the final volumetric diagonal positioning error with compensation was controlled at approximately 20 μm. [ABSTRACT FROM AUTHOR]

China has the world's largest machine tool consumer market, and its proportion of annual output of machine tools is still gradually increasing. As basic equipment of the manufacturing industry, the energy consumption of machine tools is significant. A lot of research has been done to model the cutting power, spindle power, and feed power of machine tools. However, many studies have shown that the fixed energy consumption of machine tools (generated by standby and auxiliary devices) accounts for a high proportion. This paper solves the problem of standby and auxiliary power modeling. The following work is done: (1) a new standby power empirical model is proposed, which distinguishes the standby power of CNC machine tool in weak and strong current states. Meanwhile, the new model reveals the influence of the intermittent working characteristics of the lubrication cooling device on the standby power, through deriving the power equations of the lubrication and cooling system (three stages are considered namely preheating, minimum theoretical oil supply period, and non-oil interval). (2) The new standby model is verified by experiments performed on a lathe, milling machine, and machining centers. Comparing with the existing standby power model, the new model is confirmed to be closer to the actual measurement curve of standby power, and avoids the standby energy consumption prediction error caused by using a single constant power value. (3) Through the analysis of the working principle and energy consumption of auxiliary functional components, the theory and empirical power equations of lighting, cutting fluid device, tool changer device, chip removal device power, and air blowing device are deduced, and also have been verified by experiments on three machine tools. [ABSTRACT FROM AUTHOR]

The moon-shaped trajectory is designed as to study the influence of dynamic error outside servo loop (DEOS) on the trajectory error of CNC machine tool. Firstly, the system identification method is used to build up the transfer function from the setpoint position (SP) to the linear encoder feedback position (LEFP), as well as the transfer function from the SP to the effector end position (EEP). Inputting the setpoints of the moon-shaped trajectory into these transfer functions, the LEFP and EEP can be predicted. Secondly, the setpoint trajectory, trajectory at LEFP, and trajectory at EEP are generated by D-H method. In addition, the trajectory errors are solved. Finally, the influence of DEOS on the trajectory error at EEP is investigated. The results show that there is a clear difference between the trajectory error at LEFP and the trajectory error at EEP, which causes the moon-shaped trajectory to be overcut and undercut. At a feed rate of 10000 mm/min, the difference between the two errors can reach a maximum of 0.03 mm. The main reason for the inconsistency is that the DEOS causes the trajectory at LEFP and the trajectory at EEP to not coincide. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic linearization modeling method for machine tool thermal errors based on data-driven control theory, with improved accuracy and robustness under various practical working conditions of machine tool. The nonlinear, quasi-static and pseudo-hysteric characteristics of the machine tool temperature field are identified as the main causes for poor robustness in conventional thermal error mathematical models. The theoretical and practical difficulties in applying conventional modeling approaches based on the model-based control theory are demonstrated using two types of CNC machine tools as examples. The data-driven control theory is applied to dynamic linearization modeling and the developed data model has shown significant improvement over the general dynamic model in terms of model accuracy and robustness. The feasibility and effectiveness of the proposed dynamic linearization modeling method has been verified using two experiments, demonstrating excellent robustness and ability to adapt to various machining conditions and to improve machining accuracy. [ABSTRACT FROM AUTHOR]

Radical novelty is one of the key characteristics of emerging technologies. This characteristics makes emerging technologies as a quite different from established technologies. From the perspective of radical novelty, some studies consider patents with little similarity in terms of key concepts and contents to existing patents as candidate emerging technologies. However, existing research remains in examining small-scale patents for evaluating candidate emerging technologies due to the lack of data-processing capacity—the recent rising of deep learning methods may help in this. This study, therefore, develops a novel deep learning based framework for identifying emerging technologies by combining a technological impact evaluation using patents and a social impact evaluation using website articles. Using a large scale multi-source dataset including 129,694 patents and 35,940 website articles, this paper applies the framework to investigate the case of computerized numerical control machine tool technology, through which the framework is validated. The results show that 16,131 patents out of 129,694 patents are considered as candidate emerging technologies, and 192 patents out of 16,131 patents are identified as emerging technologies through the evaluation of technology impact and social impact. This implies that these candidate emerging technologies can evolve to emerging technologies, though not all of them—we need deep learning method to scrutinize a larger scale multi-source data to identify rather a small number of potential emerging technologies. The proposed framework can also be extended to explore other disciplinary multi-source data for strategic decision support in identifying emerging technologies. [ABSTRACT FROM AUTHOR]

To improve the machining accuracy and stability of CNC machine tools, the lead screw is divided into several segments, and the action law of single section thermal deformation is analyzed, then the thermal deformation model of lead screw base on subsection processing is established. Then the relative relation of thermal deformation of each segment, the cumulative thermal drift of thermal deformation of each segment itself, the total thermal deformation of the free end of lead screw, and the relationship between them are obtained. Then, a segmented thermal compensator based on PLC for CNC system is developed. The thermal error of each segment of the lead screw is detected and compensated separately, and then the thermal error of the segment where the machine tool worktable moves to the different segment of the lead screw is compensated. Tests show that the segmented thermal compensator has achieved good actual compensation effect, so that the positioning error caused by thermal deformation of each shaft can be reduced by more than 50%. [ABSTRACT FROM AUTHOR]

The optical path calibration process using a laser interferometer is time-consuming and tedious, requiring repeated adjustment and experienced operators, leading to a significant reduction in measuring efficiency. A novel optical path calibration process of the linear axis of a three-axis machine tool using a Renishaw XL-80 laser interferometer is proposed in this paper. The homogeneous coordinate transformation error model of the measured axis is established. Using the established error model, only the initial distance from the laser transmitter to the beam splitter and the initial distance from the beam splitter to the linear mirror are required to accurately calibrate the laser beam. The relationship between the fixed distance from the center of the light target to the quarter point on the edge of the light target and the movement distance of the measured axis are obtained. Eight deviations of the optical path affected by the laser transmitter, the beam splitter and the linear mirror are solved. Calibrations based on the deviations can be achieved by adjusting the positions and orientations of the laser interferometer components, leading to a rapid and accurate calibration method for measuring the optical paths. [ABSTRACT FROM AUTHOR]

Incremental forming is a flexible and adaptable process with a high scope in future for prototyping sector and batch shop production. It finds application in almost every engineering field. One such prominent field is the research and development. The research and development are often associated with prototyping of a variety of products for evaluating and testing the design and clarifying production costs and issues. In this paper, experimentation of Ti-6Al-4V sheets using SPIF was studied, and the influence of tool feed (f), incremental step depth (d) and spindle speed (s) to the surface roughness (Ra), wall angle (θ), and average thickness (t) were evaluated. The method was carried out using CNC Milling Machine with the help of a fixture and hemispherical end tool. Response surface methodology was used to design the experiments, and ANOVA was performed to find the factor which affected the selected method significantly. Finally, the input parameters were optimized to achieve maximum wall thickness, minimum surface roughness, and maximum wall angle. [ABSTRACT FROM AUTHOR]

Titanium alloys and other difficult-to-machine materials are machined using large-torque CNC machine tools, and asymmetric mechanical spindle is employed in its spindle system. The mechanical spindle demonstrates high power and large torque. The asymmetric design makes the spindle structure more compact while reducing assembly requirements. Nevertheless, when processed, the asymmetric spindle shows complex thermal characteristics and makes it difficult to ensure real-time control, which easily results in significant errors for parts. To eliminate the uncontrollable thermal error, the efforts are made as follows. Firstly, the cold state and hot state attributes in thermal error analysis of mechanical spindle are defined, and the method to distribute the measuring position of asymmetric mechanical spindle temperature is proposed. Secondly, a three-dimensional space detection and analysis method for thermal error of six-vector eight-test based on rotation error vector and deviation error vector is suggested. Thirdly, according to the experimental results, the six-vector thermal error of the three-dimensional space is determined, and then a thermal error compensation technology based on Siemens space coordinate transformation parameters is proposed. Finally, the S specimen recommended by ISO 10791-1-annex A was used for machining verification before and after thermal error compensation. By comparing the 0.129 mm deviation before compensation and the 0.085 mm deviation after compensation, the machining accuracy of parts is discovered to improve by 34.1%. Therefore, the above research lays a theoretical foundation for the follow-up thermal error detection and compensation for the asymmetric spindle of large torque CNC machine tools of the same type. [ABSTRACT FROM AUTHOR]

The feed system of CNC machine tool has the characteristics of multiple heat sources and strong time-varying heat generation rate of each heat source. However, previous studies have paid little attention to the time-varying heat generation rate of each heat source, so it is difficult to accurately predict the temperature rise and thermal error of the lead screw. In this paper, Monte Carlo (MC) simulation-integrated FEM method was used to capture the heat generation rate of each heat source in the feed system. Consequently, relationship of the ratio of each heat source to the total heat generation rate of the system with the running time was obtained. Then, based on heat ratios of the heat sources, the torque current of the servo motor in the feed system was proposed to calculate the heat generation rate of each heat source. Finally, based on the finite difference method of one-dimensional heat conduction equation, numerical prediction model for thermal error of the lead screw was put forward. Finally, the accuracy and validity of the calculation model were verified by comparing with the experimental results. [ABSTRACT FROM AUTHOR]

Tool path deviation reduces machined parts quality. To enhance machine tool accuracy, compensation tables are provided in most controllers to automatically apply small corrections to axis commands. A model-based approach, considering the ISO 230-1 machine geometric error parameters, is proposed to generate the table entries. The error parameters are estimated using model-based indirect calibration results from a scale and master balls artifact probing (SAMBA) test. Two models are used, one with primarily the axis alignment errors and scale errors and the other including many error motions. The 3D grid error compensation is generated with a minimal optimum mesh grid dimension to achieve a preset precision considering the estimated model error parameters. The efficiency of the table is evaluated using a 3D ball-bar test consisting of various circular trajectories along several meridians and the equator before and after applying the table-based error compensation. It is shown that the volumetric errors due to out-of-squareness errors and linear axis linear positioning errors can be compensated using a 2 × 2 × 2, 8 nodes, grid. However, when including error motions, the optimum grid dimension depends on the specific error values of the machine. For the tested machine, a 19 × 19 × 19 for 6859 nodes grid was required, with which the out-of-sphericity of the tool trajectory relative to the workpiece frame is improved by over 82%. [ABSTRACT FROM AUTHOR]

Establishing a mathematical model that can reflect the relationship between temperature increase and thermal error during machining is the core of thermal error compensation technology for CNC machine tools. The collinearity between temperature-sensitive points and the correlation between temperature-sensitive points and thermal errors are important factors affecting the prediction accuracy and robustness of the thermal error compensation model. Based on the thermal error measurement experiments of the Leaderway-V450 CNC machine tool in different periods of the year, the principal component regression (PCR) modelling algorithm, which can eliminate the collinearity effect, is proposed to establish the thermal error compensation model of the machine tool on the basis of selecting the temperature-sensitive points by using the correlation coefficient. It is compared with the newly proposed ridge regression thermal error compensation modelling algorithm. The results show that the thermal error compensation modelling method proposed in this paper can basically control the Z-direction thermal error of the CNC machine tool spindle within 10 μm with only two temperature sensors and has higher engineering practicability. It is found that the thermal error compensation model of machine tools has a jump interval affected by the ambient temperature. This interval is called the temperature-sensitive interval, and a temperature-sensitive interval subsection point selection algorithm is proposed to build a subsection model on both sides of the segment point. The results show that the Z-direction thermal error of the spindle of CNC machine tools can be basically controlled within 5 μm with only two temperature sensors and that the model is highly robust and has great engineering application value. [ABSTRACT FROM AUTHOR]

With the rapid development of modern industry and industrial upgrading, machine tool performances such as high machining speed, high machining accuracy, and high reliability have gradually become the development trend of high-end CNC machine tools. The vibration characteristics of the machine structure become an important factor affecting the machining quality and processing efficiency of the machine tool. Since the movable parts of the CNC machine tool will impact the machine structure itself during the acceleration and deceleration process, by identifying the structural vibration response signal, the modal parameters of the machine tool in the running state can be obtained, called active excitation mode analysis. However, this method still has certain defects in theory and experiment. For example, the excitation principle is not theoretically explained, and the excitation energy and the frequency band of the method are limited. To solve these problems, this paper conducts more comprehensive research on the modal analysis method of CNC machine tools based on operational impact excitation. The main research contents are as follows: considering the rigid body motion of the table and the elastic collision of the screw nut pair, the impact excitation is modeled and analyzed. We have introduced a response-based modal identification algorithm. The influence factors of impact excitation energy and frequency band were studied from both theoretical and experimental aspects. Design three excitation sequences for workbench excitation and spindle excitation, respectively, analyze the vibration response and modal identification of the machine tool structure under different excitation sequences. It is verified that the method does not depend on the specific excitation sequence, as long as it satisfies the random characteristics. Aiming at the problem of insufficient excitation energy of the machine tool structure for single-component impact excitation, a multi-component joint impact excitation method based on the principle of multi-point excitation technology is proposed. The design experiment is carried out to verify the impact excitation method, which shows that the joint excitation has better excitation effect than single-axis excitation. [ABSTRACT FROM AUTHOR]

As the CNC machine tool feed mechanism is affected by joint action of heat and load during the working process, thermal–mechanical coupling analysis and experimental research on CNC machine tool feed mechanism are conducted. On the basis of exploring the thermal–mechanical coupling effect of CNC machine tool feed mechanism, a thermal–mechanical coupling dynamic analysis method is proposed through combining multi-step finite element simulation with temperature field experiment. According to the proposed method, the transient and steady-state simulation analysis on CNC machine tool feed mechanism are conducted and conclusions obtained are as follows, before the whole feed mechanism reaches thermal balance state, the thermal–mechanical coupling effect makes the deformation of the feed mechanism increase with the temperature rising. The correctness of the proposed thermal–mechanical coupling dynamic analysis method was verified by the contrastive analysis of the experiments. Therefore, in the process of CNC machine tool feed mechanism's analysis and design, the influence of thermal–mechanical coupling effect on dynamic performance should be considered. [ABSTRACT FROM AUTHOR]

Reducing machine tool energy consumption is critical for the manufacturing industry to achieve sustainable development. An improved energy consumption model is proposed based on the classification of computer numerical control (CNC) machine tool components. The model can be applied in various machine tools and express the machine tool operations flexibly and accurately. To facilitate the application of the model in the workshop, a corresponding procedure is proposed including data acquisition experiment, coefficient estimation, and numerical control (NC) program interpretation. The implementation of the method (i.e., the model and the procedure together) is illustrated in a grinder and a vertical machining center. The results show that the method has a good performance in predicting the power trend of machine tools during their working period. The energy consumption predictive accuracy is more than 96% in the two cases, which demonstrates the high robustness of the method. [ABSTRACT FROM AUTHOR]

The characteristic responses of a mini three-axis computer numerical control (CNC) machine tool based on the controller tuning operation were investigated for big data estimation. The major tuning parameters included the position control gains Kp, the position feed-forward control gains Kf, the speed control gains Kv, and the gain ratios Kg of the position and speed control values in manufacturing industries. Kp gains of 10, 30, 50, 80, 100, 200, 300, and 400 rad/s, Kf gains of 0, 30, 50, 60, 80, and 100 %, Kv gains of 30, 50, 70, 100, 300, 900, 2000, and 3000 rad/s, and Kg ratios of (1:1), (3:1), (5:1), and (7:1) were analyzed for smart productivity. The results show that the settling times at different Kp values were almost constant when the Kp gain was over 200 rad/s. The maximum overshoots, when the feed-forward gain is over 60 %, almost increased with increasing feed-forward gains. However, the overshoot of the three-axis CNC machine tool decreased as the Kv gain increased until the Kv gain reached 70 rad/s. The settling times at a constant Kg ratio decreased with an increase in the Kp and Kv gains. The characteristic responses of the tuning operations were enabled with connectivity to a cloud network to share the big data, to support decision making, and to adjust operations in real time. [ABSTRACT FROM AUTHOR]

In the technology of thermal error compensation for CNC machine tools, it is particularly important to select modeling variables which can stably reflect the relationship between temperature field and thermal expansion in terms of modeling. This paper analyzes the theories and experiments on the thermal properties of the temperature-sensitive points distributed on one-dimension pole. It is found that the prediction model performs better in prediction accuracy and robustness when established with linear points as independent variables than with nonlinear ones. However, because of the complicated structure of machine tools, it is rather hard to fix the positions of linear points, which consequently lead to the proposal of a comprehensive temperature-feature extraction method that uses feature extraction algorithm and weight optimization to construct linear temperature-sensitive points. Experimental facilities verified the feasibility of its proposal. What’s more, based on the effectiveness of building linear measuring points, it is proposed to arrange the temperature sensors along the deforming direction. With the feeding system of a gantry machine tool as the testing platform, the thermal error model established according to the proposed method is actually tested under different working conditions. The result shows this proposed method has higher prediction precision and robustness. [ABSTRACT FROM AUTHOR]

Aiming at the deficiency of the robustness of thermal error compensation models of CNC machine tools, the mechanism of improving the models' robustness is studied by regarding the Leaderway-V450 machining center as the object. Through the analysis of actual spindle air cutting experimental data on Leaderway-V450 machine, it is found that the temperature-sensitive points used for modeling is volatility, and this volatility directly leads to large changes on the collinear degree among modeling independent variables. Thus, the forecasting accuracy of multivariate regression model is severely affected, and the forecasting robustness becomes poor too. To overcome this effect, a modeling method of establishing thermal error models by using single temperature variable under the jamming of temperature-sensitive points' volatility is put forward. According to the actual data of thermal error measured in different seasons, it is proved that the single temperature variable model can reduce the loss of forecasting accuracy resulted from the volatility of temperature-sensitive points, especially for the prediction of cross quarter data, the improvement of forecasting accuracy is about 5 μm or more. The purpose that improving the robustness of the thermal error models is realized, which can provide a reference for selecting the modeling independent variable in the application of thermal error compensation of CNC machine tools. [ABSTRACT FROM AUTHOR]

Thermal errors are major contributor to dimensional errors of a part during precision machining. Error compensation is an effective method to reduce thermal errors. Accurate modeling of thermal errors is a prerequisite for thermal error compensation. In this paper, five key temperature points of a computer numerical control (CNC) machine tool were selected based on grey relational analysis method (GRAM). One thermal error model based on the five key temperature points was proposed using artificial fish swarm and ant colony algorithm-based back-propagation neural network (AFSACA-BPN). AFS is applied to generate initial pheromone value of ACA, which improves the computational efficiency of BPNs and prediction accuracy of thermal error modeling. One thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. Experiment results show that the diameter error of the workpiece reduced from 23 to 10 μm after compensation. [ABSTRACT FROM AUTHOR]

The motional smoothness and contour precision of the CNC system are the key factors to ensuring the machining accuracy for multi-axis CNC machine tools. To improve the motional smoothness and contour precision for CNC machine tools, a control method based on parametric predictive and variable universe fuzzy control is presented. The model of the multi-axis drive system is established. The multi-axis model of parametric predictive control is proposed, where the model is structured, and output of the model is developed. A new improved quadratic performance index is proposed to guarantee the motional smoothness and contour precision, and the real-time calculation of the optimal control rate is deduced. A control approach of the nonlinear error compensation is presented to reduce contour error between the parametric predictive model and the actual output, where the adaptive error compensation controller is designed, and adaptive adjusting factors of variable universe are developed. The proposed control method is evaluated by simulations and experimentations. Simulation and experimental results have demonstrated that the proposed control approach can significantly improve the motional smoothness and machining precision for CNC machine tools. [ABSTRACT FROM AUTHOR]

As the geometric errors of motion axis can be equivalent to the differential movement, regarded as a differential operator based on its ideal position, a new modeling method for multi-axis CNC machines based on differential transform theory is proposed in this paper. First, the workpiece coordinates is selected to observe the errors of the tool pose. Then, a general geometric error model for multi-axis machines is established. Moreover, the Jacobian matrix is applied to describe the relationship between the tool pose error vector and the compensation error vector. All the elements of the matrix are obtained by computing the differential operators instead of computing the partial derivatives. The compensation errors vector is solved using the pseudo-inverse Jacobian matrix. Finally, an automatic modeling procedure is developed to construct the geometric errors for multi-axis machine tools. An experiment on a five-axis machine tool is conducted to test and verify the proposed method. The results show that the proposed method dramatically improves the overall position accuracy of the test tool path. [ABSTRACT FROM AUTHOR]

Machine lubrication systems are a very important portion of manufacturing and production workshop maintenance. Automatic lubrication systems eliminate the need for often careless manual lubrication, providing a safer, more frequent, and opportune monitored approach to machine lubrication. However, traditional automated lubrication systems have inherent environmental and technical-economic problems. With the goal of higher machining precision, cost-effectiveness, greater reduction in oil consumption, and more flexible performance, an automated lubrication control system is introduced in this research work. The new automated lubrication control system in computer numerical control machine tool guideways is a novel approach in machining technology; failure detection/correction in the lubrication system may be identified by temperature signals from sensitive temperature sensors installed in the machine tool guideways, with the signals reflecting the friction, wear, and loading conditions. Data collected via temperature sensors, data analysis, and preparation of commanding signals are analyzed by a lubrication control unit (LCU). The LCU transmits signals to actuators to trigger oil injection by the oil pump. The display unit presents real-time measured temperature variations along with the pump's operation state. [ABSTRACT FROM AUTHOR]

It has been proved that error compensation is an effective approach to improve machining accuracy of a machine tool cost efficiently. In this paper, the framework of an error compensation software system, which can realize software error compensation via numerical control (NC) programs reconstructing, is investigated. And the algorithms relating to error prediction are discussed in detail, such as positioning movement error compensation, linear interpolation movement, and circular interpolation movement error compensation. To realize the error compensation, NC program is reconstructed according to the predicted errors during virtual machining before it is fed to the actual machining. Two controlled machining experiments were carried out. The results show that error compensation methods via reconstructing NC programs can improve the movement accuracy of a computer numerical control CNC machine tool obviously. [ABSTRACT FROM AUTHOR]

The paper first presents an AND/OR nets approach for planning of a computer numerical control (CNC) machining operation and then describes how an adaptive fuzzy Petri nets (AFPNs) can be used to model and control all activities and events within CNC machine tools. It also demonstrates how product quality specification such as surface roughness and machining process quality can be controlled by utilizing AFPNs. The paper presents an intelligent control architecture based on AFPNs with learning capability for modeling a CNC machining operation and control of machining process quality. In this paper it will be shown that several ideas and approaches proposed in the field of robotic assembly are applicable to the planning procedure modeling with minor modifications. Graph theories, Petri nets, and fuzzy logic are powerful tools which are employed in this research to model different feasible states for performing a process and to obtain the best process performance path using exertion of the process designer’s criteria. [ABSTRACT FROM AUTHOR]

The purpose of this research is to enhance the accuracy of an INDEX-G200 turning center through real-time compensation for the thermal errors. This paper presents the whole process of thermal error testing, temperature variable selecting by grouping approach, thermal error modeling, etc. The analysis results show that the thermal error range for radius direction on the machine is approximately 18 μm larger than expected, the model set by this method is more accurate and robust, and the thermal error is the major error sources for the machine and should be considered seriously by machine builders and users . [ABSTRACT FROM AUTHOR]

In this paper, a measuring device with a planar encoder is developed to test the performance of a CNC machine tool. With the assistance of a PC, this system can be employed for both 2D contouring tests and 3D positioning tests for a CNC machine tool. The structure and the principle of the system, the applications for the general 2D contouring test, the drift test, and the specified geometric part path tests. An actual case study on improving the accuracy of machining a cam are described. Finally, a new 3D positioning method using the optic encoder is demonstrated. [ABSTRACT FROM AUTHOR]