Search

The core mechanism of existing CNC tool changers is typically a single composite cam swinging rod linkage. This setup has two main drawbacks: amplified vibration errors and high impact forces due to the swinging rod. To address these, this study focuses on CNC tool changers, specifically selecting the up-and-down motion follower and proposing a novel double composite cam push rod type mechanism. Solid modeling for both mechanisms was conducted using Creo and Matlab, and theoretical modeling of the contact forces for each follower type was completed. Comparative analysis shows the push rod follower exhibits lower contact forces than the swinging rod. Using the Adams platform, the dynamic characteristics of both mechanisms were analyzed, leading to an optimized design for the push rod type. An experimental model, scaled to 0.5, was built to compare experimental data with simulation results. The findings verify the accuracy of the models, supporting the feasibility of the push rod type tool changer. [ABSTRACT FROM AUTHOR]

To comprehensively improve the thermal, static and dynamic characteristics and achieve lightweighting for CNC machine tools, this paper proposes a multi-objective joint optimization method based on coupled thermal–mechanical–vibration collaborative analysis. The dual-spindle component of a CNC machine tool is taken as the parameterized model. According to the theories of thermal characteristics, statics, and dynamics, the solution of thermal-mechanical coupling deformation and the solution of vibration characteristics under prestress are repeatedly conducted, that are working collaboratively with each process of parameters sensitivity computing, selection of design variables, central composite design, and multi-objective joint optimization. The response surfaces of the objective functions are established. The optimal parameter combination for improving CNC machine tool performance is effectively obtained. And the multiple objectives of improving the thermal, static and dynamic characteristics, as well as lightweighting, are achieved. The results show that the mass of the optimized component is reduced by 10.1%; the first-order natural frequency is increased by 3.9%; the coupling deformation of the end face of the left spindle seat is reduced by 5.3%; and the coupling deformation of the end face of the right left spindle seat is reduced by 9.0%, while the temperature of the component hardly increases. This indicates that this method can comprehensively improve the performance of CNC machine tool components and provide a reference for the multi-objective joint optimization design of CNC machine tools. [ABSTRACT FROM AUTHOR]

In the study, the effects of tool run-out caused by the CNC spindle mechanism on surface roughness, measurement accuracy, consumable expenses, maintenance costs and cutting tool life that may occur during the cutting process wer e investigated based on metal die manufacturing. Before starting the experimental studies, the machine spindle health was measured. Here, tool pulling force, 300 mm test bar run-out, inner conical run-out and spindle vibration values were examined and evaluated. According to the results obtained, the m3/life relationship was compared with the surfa ce milling operation by eliminating the factors causing tool run-out before and after machine tool maintenance for cut ting tool life. In order to examine the effect of run-out on surface roughness and measurement accuracy, the effect of run-out was examined and compared by applying outer contour and inner contour operations before and after mainte nance with experimental studies. These operations were examined with 1.2379 steel, which is the most commonly use d steel type in the metal die sector in terms of mechanical properties. The results showed that run-out has serious ne gative effects on the cutting process and cutting tool life, measurement accuracy and surface roughness. In operations performed with a healthy spindle after maintenance, the cutting tool life increased by an average of 40%, surface rou ghness and dimensional accuracy reached the desired levels. The stress during the machine operation decreased by an average of 15%. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract 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.

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]

The QCs (quality characteristics) of MAU (meta-action unit) determine the quality of CNC (computer numerical control) machine tools. For improving the accuracy of MAU QCs analysis, and solving the problems that the current methods either only have subjective analysis, or only have objective analysis, or the analysis process that includes both subjective and objective analysis methods is relatively complex, a comprehensive identification method of MAU QCs according to the improved MAHP (multiplicative analytic hierarchy process) method and Shannon entropy is proposed in this paper. Firstly, the QCs of the MAU are divided into quality layer and index layer. Secondly, the expert evaluation weight is introduced to improve the MAHP, then the sub quality layer of the meta-action unit was subjectively analyzed. Thirdly, the index layer of the MAU is objectively analyzed by the Shannon entropy. Finally, the results obtained by WMAHP (weighted multiplication analytic hierarchy process) method and Shannon entropy method are synthesized, and the Pareto principle is adopted to extract the key QCs of the MAU. The proposed method combines the advantages of both subjective and objective analysis, and it can reduce the fluctuation of the analysis results caused by data changes. Meanwhile, the analysis process is relatively simple. Its applicability and superiority have also been confirmed by an experiment and comparisons with various methods. The proposed method is of great significance to improve the quality of CNC machine tools. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Abstract Laser tracers are a three-dimensional coordinate measurement system that are widely used in industrial measurement. We propose a geometric error identification method based on multi-station synchronization laser tracers to enable the rapid and high-precision measurement of geometric errors for gantry-type computer numerical control (CNC) machine tools. This method also improves on the existing measurement efficiency issues in the single-base station measurement method and multi-base station time-sharing measurement method. We consider a three-axis gantry-type CNC machine tool, and the geometric error mathematical model is derived and established based on the combination of screw theory and a topological analysis of the machine kinematic chain. The four-station laser tracers position and measurement points are realized based on the multi-point positioning principle. A self-calibration algorithm is proposed for the coordinate calibration process of a laser tracer using the Levenberg–Marquardt nonlinear least squares method, and the geometric error is solved using Taylor’s first-order linearization iteration. The experimental results show that the geometric error calculated based on this modeling method is comparable to the results from the Etalon laser tracer. For a volume of 800 mm × 1000 mm × 350 mm, the maximum differences of the linear, angular, and spatial position errors were 2.0 μm, 2.7 μrad, and 12.0 μm, respectively, which verifies the accuracy of the proposed algorithm. This research proposes a modeling method for the precise measurement of errors in machine tools, and the applied nature of this study also makes it relevant both to researchers and those in the industrial sector.

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan's machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product's functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness. [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]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The machining accuracy of CNC machine tools is significantly affected by the thermal deformation of the feed system. The ball screw feed system is extensively used as a transmission component in precise CNC machine tools, responsible for converting rotational motion into linear motion or converting torque into repetitive axial force. This study presents a multi-physical coupling analysis model for the ball screw feed system, considering internal thermal generation, intending to reduce the influence of screw-induced thermal deformation on machining accuracy. This model utilizes the Fourier thermal conduction law and the principle of energy conservation. By performing calculations, the thermal source and thermal transfer coefficient of the ball screw feed system are determined. Moreover, the thermal characteristics of the ball screw feed system are effectively analyzed through the utilization of finite element analysis. To validate the proposed analysis model for the ball screw feed system, a dedicated test platform is designed and constructed specifically to investigate the thermal characteristics of the ball screw feed system in CNC machine tools. By selecting specific CNC machine tools as the subjects of investigation, a comprehensive study is conducted on the thermal characteristics of the ball screw feed system. The analysis entails evaluating parameters like temperature field distribution, thermal deformation, thermal stress, and thermal equilibrium state of the ball screw feed system. By comparing the simulation results from the analysis model with the experimental test results, the study yields the following findings: The maximum absolute error between the simulated and experimental temperatures at each measuring point of the feed system components is 2.4°C, with a maximum relative error of 8.7%. The maximum absolute error between the simulated and experimental temperatures at the measuring point on the lead screw is 2.0°C, with a maximum relative error of 6.8%. The thermal characteristics obtained from the steady-state thermal analysis model of the feed system exhibit a prominent level of agreement with the experimental results. The research outcomes presented in this paper provide valuable insights for the development of ball screw feed systems and offer guidance for the thermal design of machine tools. [ABSTRACT FROM AUTHOR]

Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model's predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production. [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 thermal characteristics of the spindle system for CNC machine tools are influenced by multiple factors which are nonlinear and time-varying. In this paper, a nonlinear time-varying thermal characteristics solving model for the spindle system was established based on the numerical solution method. Through theoretical deduction and data fitting, mathematical models of nonlinear time-varying factors including the friction torque generated by lubricants, convective heat transfer coefficient, and coolant and ambient temperature are constructed. The temperature and displacement of the spindle system at each time step are solved by considering the comprehensive effect of multiple nonlinear time-varying factors. And the actual temperature and axial deformation data of the spindle system are obtained through thermal characteristics detection experiments. By comparing solution results affected by multiple nonlinear time-varying factors and by non time-varying factors with experimental data, it can be concluded that the nonlinear time-varying thermal characteristics model has advantages in reflecting the trend of numerical changes and the accuracy of result solving over a method considering non time-varying factors and the solution values of temperature affected by multiple nonlinear time-varying factors are almost consistent with detection values and the relative errors are all within ±3%. The relative error of axial deformation between the value solved by the model and the detection value is close to −1%. This conclusion demonstrates the rationality and accuracy of the thermal characteristics solving model and the construction of nonlinear time-varying factors. This study is of great significance for exploring the thermal characteristics of the spindle system and improving CNC machine tool performance in depth. [ABSTRACT FROM AUTHOR]

This paper explores the application of various machine learning techniques to model the optimal measurement time required after machining with a probe on CNC machine tools. Specifically, the research employs four different machine learning models: Elastic Net, Neural Networks, Decision Trees, and Support Vector Machines, each chosen for their unique strengths in addressing different aspects of predictive modeling in an industrial context. The study examines as input parameters such as material type, post-processing wall thickness, cutting depth, and rotational speed over measurement time. This approach ensures that the models account for the variables that significantly affect CNC machine operations. Regression value, mean square error, root mean square error, mean absolute percentage error, and mean absolute error were used to evaluate the quality of the obtained models. As a result of the analyses, the best modeling results were obtained using neural networks. Their ability to accurately predict measurement times can significantly increase operational efficiency by optimizing schedules and reducing downtime in machining processes.

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]

In machine tools, the trajectory error of the tool center point (TCP) is caused due to the dynamic positioning error of feed drive systems according to the relationship in spatial structure and motion. The dynamic positioning error of the feed drive system includes axial dynamic error and lateral dynamic error. To analyze the influence of the lateral dynamic error on the trajectory error of the TCP of multi-axis machine tools, in this study, circular and butterfly trajectories were designed as the set trajectories of a machine tool with three linear axes. In addition, the tracking error models were constructed both considering the lateral dynamic error and without considering the lateral dynamic error, and the difference between the two models was quantified. The Sobol method was then used to analyze the sensitivity of the lateral dynamic error of the feed drive system to the tracking error of the TCP. Finally, experiments were performed on a three-axis machine tool. The results show the maximum lateral dynamic error in the direction perpendicular to the motion direction is 0.026 mm. The lateral dynamic error detrimentally affects the tracking error and contour error of the TCP in multi-axis machine tools. [ABSTRACT FROM AUTHOR]

Gear drives are widely used in various fields and applications due to their properties and capacity. Their versatility, durability, and ability to transmit high torques as well as precision and reliability make them extremely useful in many fields of technology. They are widely used in industrial and energy machinery, vehicle drive systems, aerospace, medical devices, and many other areas. Gears can be manufactured using many technologies. This work focuses mainly on machining with particular emphasis on high-performance new technologies. The process of mathematical modeling of the gear and the machined profile is strongly related to CNC machining technologies. A robust correlation of systems supporting the design and modeling of sliding gears needed for the manufacturing process is presented in the article. It is very important to properly assess gears with correct manufacturing in accordance with a specific standard. The article presents an analysis of available methods for controlling gears using coordinate measurement techniques. Gear machining methods were assessed in terms of the technologies used as well as their productivity and manufacturing tolerance. [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]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies. [ABSTRACT FROM AUTHOR]

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product’s functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness.

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The helical contour motion accuracy of feed drive axes is important for thread milling operations in computer numerical control (CNC) machine tools. However, the motion dynamics and external disturbances significantly affect the contour motion results, while the feed drive axes perform helical motions in thread milling operations. Although existing iterative learning contour control (ILCC) methods can improve contour motion accuracy, the problems of data recording and processing on memory usage and computational burden in control systems, wasted materials, and increased costs in thread manufacturing still limit the practical applications of ILCC. Therefore, considering the similar motion dynamics and external disturbances of the feed drive axes during the pitch cycle motions of a helical path, this study developed a pitch cycle-based iterative learning contour control (PCB-ILCC) method to address the control system and thread manufacturing problems caused by the use of ILCC. For PCB-ILCC, this study adopted contour error vector estimation by referring to the interpolated positions on the pitch cycle of the helical path to simplify the computational complexity and designed the ILCC using the cycle learning method to easily implement the ILCC structure. Thus, this study developed a permanent magnet synchronous motor (PMSM) driving control utilizing the robust control method to mitigate the problems of motion dynamics and external disturbances on the feed drive axes. Thread milling experiments performed on a five-axis CNC machining center demonstrated the feasibility of the PCB-ILCC and validated that it can significantly improve the helical contour motion accuracy of the feed drive axes and achieve an 80% contour error reduction rate in comparison with the proportional–proportional–integral control, which is extensively used in commercialized PMSM drivers and CNC controllers. [ABSTRACT FROM AUTHOR]

In order to solve the problem of extracting machine tool design knowledge in the construction of computer numerical control( CNC) machine tool design knowledge graph, the classification standard and labeling strategy of knowledge in the field of CNC machine tools are formulated and a domain data set was constructed. The entity recognition method based on robustly optimized BERT pretraining approach(RoBERTa) for CNC machine tool design was proposed. Firstly, RoBERTa was fine-tuned by using the data set in the field of CNC machine tools, and then the text was encoded by RoBERTa to generate a vector representation. Secondly, the bidirectional long short-term memory (BiLSTM) network was used to extract the vector features. Finally, the conditional random field (CRF) was used to infer the optimal result. The experimental results show that the F1 value of the model on the test data set is 86. 139%, the F1 value of most key entity is greater than 85%. Compared with other models, it is improved by 2% ~18%. It can be seen that this method has obvious advantages in the recognition of CNC machine tool design entities, and can identify the key entities in the machine tool design knowledge, providing a data basis for the knowledge graph. [ABSTRACT FROM AUTHOR]

Copyright of Large Electric Machine & Hydraulic Turbine is the property of Large Electric Machine & Hydraulic Turbine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large cylindrical worm gear set of ISO type I are manufactured using endmill tools on universal CNC machine tools. This manufacturing method requires neither special gear-generating machines nor special tools. The tooth flank forms of ISO type I cylindrical worm gears are involute helicoids as a standard. The targeted theoretical tooth flanks of the worm and the mating worm wheel are determined based on a tooth contact analysis (TCA) of such worm gear set. The cutting conditions of the worm are determined after the offset distance between the worm axis, and the central axis of the endmill tool is calculated. Afterward, the worm is manufactured by controlling only two axes on machine tools using a swarf milling method by use of the side of the endmill tool under the determined conditions. Meanwhile, the targeted theoretical tooth flanks of the mating worm wheel are modeled in 3-dimensional computer-aided design software, and the worm wheel is manufactured by a swarf milling method in a computer-aided manufacturing process. The comparison of experimental and analytical tooth contact patterns indicates almost no difference between the two tooth contact patterns. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)