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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.

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.

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.

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.

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.

A good feedrate profile for NURBS tool paths should be able to consider both machining efficiency and motion limits. In other words, the feedrate profile can lead to the machining time as small as possible and can satisfy various path and axis motion constraints. However, owing to the complicated relationship between trajectory motion and axes, it is difficult and time-consuming to check axis motion constraints (e.g., axis velocity, axis acceleration, axis jerk). In addition, the path length is not easy to calculate, so the machining time cannot be accurately estimated. This paper presents a feedrate profile planning method that can easily include, delete or organize the constraints and minimize the machining time for NURBS tool paths. The key idea is to use the same parameter $u$ to represent both NURBS tool path and feedrate profile. When designing the feedrate profile with $u$ , an analytical form of machining time can be obtained, and the constraints are converted into functions of $u$ (including feedrate, tangential acceleration, tangential jerk, axis velocity, axis acceleration, axis jerk, chord error, and centripetal acceleration), which can be quickly checked. Particle swam optimization algorithm is employed to eliminate the solutions with long machining time or do not meet the constraints, and gradually optimize the feedrate profile. In the simulation, a star-shaped NURBS curve is selected to demonstrate the effectiveness of the proposed method. The results show that the proposed method can achieve not only smooth and high-speed machining under various constraints, but also high accuracy with the minimization of position error at the final interpolation point.

In the intelligent optimization process of aerospace thin-walled parts, there are issues such as solidification of core knowledge base, high system coupling degree, and real-time evaluation and optimization feedback required for the knowledge base. These problems make it difficult to expand the functions of the digital twin system and meet the growing processing needs, ultimately hindering the application of digital twin technology. To address these issues, a digital twin system for controlling processing errors in thin-walled parts was built using a microservices architecture. In addition, a method for building a digital twin system at the processing unit level with the best coupling degree was proposed, mainly targeting the dynamic characteristics analysis knowledge base of thin-walled parts. Furthermore, to meet the requirements for backward compatibility of the processing unit level digital twin system, a comprehensive solution including the construction, operation, evaluation, optimization, and visualization of a knowledge base for the dynamic characteristics of the processing unit was proposed, providing guidance for the digital transformation and upgrading of CNC machine tools and the optimization of processing technology based on digital twin technology.

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.

A straight line or G01 is the most common path in computer numerical control (CNC) contour machining. However, non-smooth corners introduce discontinuous movement along each axis during machining. The commonly used strategy is to redesign a corner into a smooth corner path. Therefore, optimal corner smoothing should simultaneously find an optimal corner path and a corner speed that satisfied the axis motion limits. To achieve this goal, we use particle swarm optimization (PSO) to find the optimal Bézier curve and the optimal corner speed, then convert the axis constraints into polynomial inequalities and use fitness selection to deal with axis acceleration and jerk limits. For real-time operation, we establish a look-up table with the optimal corner speed and corner path models for use by a CNC controller. In the experiment, the YCM NXV560A vertical machining center was used to conduct a real machining test on a Taiwan contour, and the feasibility of the controller to generate the optimal corner strategy for various linear paths was verified.

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.

The robustness of the control algorithm plays a crucial role in the precision manufacturing and measurement of the CNC machine tool. This paper proposes a fuzzy PID controller based on a sparse fuzzy rule base (S-FPID), which can effectively control the position of a nonlinear CNC machine tool servo system consisting of a rotating motor and ball screw. In order to deal with the influences of both the internal and external uncertainties in the servo system, fuzzy logic is used to adjust the proportion, and integral and differential parameters in real-time to improve the robustness of the system. In the fuzzy inference engine of FPID, a sparse fuzzy rule base is used instead of a full-order fuzzy rule base, which significantly improves the computational efficiency of FPID and saves a lot of RAM storage space. The sensitivity analysis of S-FPID verifies the self-tuning ability of its parameters. Furthermore, the proposed S-FPID has been compared with the PID and FPID via simulation and experiment. The results show that compared with the classical PID controller, the overshoot of the S-FPID controller is reduced by 74.29%, and the anti-interference ability is increased by 62.43%; compared with FPID algorithm, the efficiency of the SPID is improved by 87.25% on the premise of a slight loss in robustness.

The paper shows the development of a 3-axis CNC machine tool for rapid prototyping by subtracting materials, with two translational and one rotating axis, whose control is based on the LinuxCNC system, with an integrated virtual machine as a digital twin. The concepts of machines with one rotary axis were analyzed and the concept of a machine for realization was adopted, which is completely configured in a CAD/CAM environment. For successful verification of the programming system, a virtual machine has been configured in the CAD/CAM system, as well as in Vericut. The final validation of the machine design was achieved by its trial work on characteristic examples.

The temperature-sensitive point is the input variable of the thermal error compensation model of computer numerical control (CNC) machine tools. At present, the most commonly used selection method is to measure the multipoint temperature and thermal error of the machine tool synchronously and select several temperature measurement points with the highest thermal error correlation as temperature-sensitive points according to the measurement data. This study reveals that this method sometimes has obvious mis-selection and causes the model to fail. The reason is that the weak correlation temperature measurement point away from the machine tool heat source amplifies the volatility of the correlation evaluation result, owing to the small overall change. If the calculated result exceeds the true strong correlation temperature measurement point, it will be incorrectly selected as the temperature-sensitive point. This scenario has been termed herein as the pseudocorrelation problem. With the gradual popularization of thermal error compensation technology for CNC machine tools, pseudocorrelation will seriously affect the mass production pass rate. Therefore, the study analyzes and rigorously proves the mathematical mechanism of this problem, and the temperature measurement point preselection algorithm based on the correlation coefficient volatility determination factor (CCVDF) is proposed to eliminate potential pseudocorrelation temperature points before selecting temperature-sensitive points. After 1000 random simulation experiments, the failure rate of Z-direction thermal error modeling after preselection decreased from 5.1% to 0.5%, and the Y-direction error decreased from 33.7% to 1.5%. This algorithm can greatly improve the qualification rate of large-scale equipment of thermal error compensation technology.

The modeling and compensation method is a common method for reducing the influence of thermal error on the accuracy of machine tools. The prediction accuracy and robustness of the thermal error model are two key performance measures for evaluating the compensation effect. However, it is difficult to maintain the prediction accuracy and robustness at the desired level when the ambient temperature exhibits strong seasonal variations. Therefore, a year-round thermal error modeling and compensation method for the spindle of machine tools based on ambient temperature intervals (ATIs) is proposed in this paper. First, the ATIs applicable to the thermal error prediction models (TEPMs) under different ambient temperatures are investigated, where the C-Means clustering algorithm is utilized to determine ATIs. Furthermore, the prediction effect of different numbers of ATIs is analyzed to obtain the optimal number of ATIs. Then, the TEPMs corresponding to different ATIs in the annual ambient temperature range are established. Finally, the established TEPMs of ATIs are used to predict the experimental data of the entire year, and the prediction accuracy and robustness of the proposed ATI model are analyzed and compared with those of the low and high ambient temperature models. The prediction accuracies of the ATI model are 20.6% and 41.7% higher than those of the low and high ambient temperature models, respectively, and the robustness is improved by 48.8% and 62.0%, respectively. This indicates that the proposed ATI method can achieve high prediction accuracy and robustness regardless of the seasonal temperature variations throughout the year.

Dynamic characteristics have an important impact on the reliability of computer numerical control (CNC) machine tool. As the basis of dynamic characteristics analysis, the modal analysis should be conducted. However, the current modal analysis methods have some shortcomings. For example, the selected research object cannot reflect the dynamic characteristics of the CNC machine tool, the selection of research object lacks a scientific basis, and the influence of coupling factors is also often ignored. To address these problems, the concepts of meta-action failure mode and the latest research results about meta-action were given herein. Meta-action unit is the smallest structural unit to ensure the normal operation of the machine tool, and it can fully reflect the dynamic characteristics of a machine tool, so it is taken as the research object. In order to reduce the blindness of selecting the analytic object when implementing the reliability improvement measures, we put forward the concept of a key meta-action unit and gave the corresponding extraction method. Meanwhile, electromechanical coupling was considered when built the dynamic model of a key meta-action unit. A CNC machine tool made in China was taken as an example, and a key meta-action unit was extracted and analyzed, then the corresponding reliability improvement measures were also given. The results verify the applicability and effectiveness of this method. The proposed method can solve the shortcomings of the current methods, which lays a foundation for further research of mechanical properties and reliability based on machine tool meta-action unit.

Reliability of the CNC machine tool (abbreviated as machine tool) relates to the product's reliability and has great influence on the manufacturing process. The traditional reliability statistical evaluation method of the machine tool neglects the influence of the mission profile and load profile (M&L), it causes that the evaluation result is not accurate enough to provide accurate references for the reliability-related works under specific M&Ls such as the preventive maintenance, product improvement, etc. To address these defects, this paper proposes an improved reliability statistical evaluation method considering the M&L. Firstly, the machine tool is decomposed by the meta-action decomposition method, and the mission profile of the machine tool is represented by the meta-action chain (MC). Secondly, load profile representation indicators of the machine tool are extracted based on the load composition and cutting force calculation. Then, the mapping model between the M&L and the machine tool's reliability is established using the radial basis function (RBF) neural network. Finally, the improved reliability statistical evaluation method is illustrated and validated by the engineering practical application. Comparing the evaluation results of the two statistical evaluation methods, it shows that the improved reliability statistical evaluation method is more accurate than the traditional reliability statistical evaluation method, under specific M&Ls, so that it can provide more accurate references for the reliability-related works.

This research proposes a method for auto-monitoring the air cutting condition of a machining process, so the air cutting time can be further improved to enhance the machining efficiency. A two-way data communication module with a CNC controller was established to achieve real-time monitoring and control function via TCP/IP protocol. The module enables the identification of the executing NC block and the cutting time during machining. The spindle load current and cutting vibration signals were used to on-line diagnose the cutting state (effective cutting or air cutting), and their associated NC codes were identified and recorded at the same time, based on the executing NC block in the CNC controller. An algorithm adopting this state change information to determine effective cutting time and air cutting time was developed and used to build an intelligent air cutting monitoring system, with a friendly human–machine interface. The system can detect air cutting time, effective cutting time, machine idle time, as well as the total machining time to improve the air cutting time to enhance the machining efficiency. Verification experiments were conducted, and the results showed the proposed method can accurately detect the air cutting occurrence and its associated NC blocks in the NC program.

The effectiveness of a Computer Numerical Control (CNC) machine tool to produce high precision products is significantly dependent on the contouring accuracy of the individual axes. The motion accuracy of such machines is influenced by the Stick-Slip Friction (SSF) between two sliding bodies. In this study, a contact force model employing relative coordinate systems for Multi-Body Dynamic (MBD) systems is used to model and simulate contact conditions at the sliding surfaces of CNC machine tools. MBD formulations and smooth SSF force model parameters in MBD are first explored. Then, computational simulations that combine MBD simulation and control system are performed on a three axes CNC machine tool model with a pure Proportional-Integral-Derivative (PID) controller to illustrate the proposed method. In the numerical simulations, the displacement of the moving part and the driving force of the controller are considered in cases of no friction, friction without stiction effect (sliding friction), and stick-slip friction. From these conditions, the influence of the friction on the sliding stability of the controller and the motion accuracy of the machine tool are analyzed and predicted. The results show that the controller must produce a higher driving force to control the MBD model when stiction occurs. The performances of the machine under different working conditions are also implemented to prove the use of embedding the smooth SSF model in co-simulate an MBD model with the control system. Hence, the main purpose of this research paper is to propose a computational co-simulation tool that can help the R&D engineers or scholars in considering SSF characteristics when analyzing and designing a machine tool.

To improve the control performance of the CNC machine tool feed servo system with nonlinear disturbances, a model reference adaptive control (MRAC) based on PID control is proposed. The method is based on the traditional three-loop control, the output signal of the speed loop controller is used as the input signal of the model reference adaptive controller, and the adaptive law is derived on the basis of Lyapunov stability theory. To verify the effectiveness, this paper analyzed the method through simulation and experiment. Results showed that the following error caused by nonlinear friction was significantly reduced when the speed was reversed, and the influence of noise and mechanical resonance was effectively suppressed. The proposed control method in this paper improves the motion-control accuracy and anti-interference ability of the control system.

As one kind of significant manufacturing equipment, computer numerical control (CNC) machine tools have to be endowed with new functions to meet the requirements of processing devices in the era of “Industry 4.0.”Inter-connection and intelligence are the fundamental characteristics of CNC machine tools in this era. To make CNC machine tools be more accessible and promote them to a higher level of intelligence, this paper presents a new architecture of CNC machine tools based on a cyberphysical system and fog computing, named as a fog computing-based cyber-physical machine tool system (FC-CPMTS). The definition, functions, and hierarchical structure of the FC-CPMTS are described respectively. CNC machine tools, cyber space, and human beings are connected closely through sensing, computing, communicating, and controlling in the FC-CPMTS. The application of fog computing enhances autonomy and collaboration of CNC machine tools. It also reduces network traffic and calculation workload of the cloud platform in the FC-CPMTS. To demonstrate the rationality and feasibility of the FC-CPMTS, an FC-CPMTS for a heavy-duty CNC machine tool is taken as a case study. The result shows that autonomy, intelligence, interconnection, and interoperability of the CNC machine tool are improved.

Law requires from employers to assess occupational risk in workplaces. A well-executed occupational risk assessment promotes the improve-ment of working conditions and reduces a negative impacts on employees’ health and life. Employers by conducting occupational risk assess-ment not only meet legal requirements but also benefit from it in two ways. They eliminate loss of an enterprise connected with employees’ absence, and they also decrease the probability of high compensation pay due to situations that have a destructive influence on the employees' health. However, in order to benefit from such actions, firstly, it is necessary to select a risk assessment method properly, then to apply it and implement preventive measures. The paper presents the results of the occupational risk assessment carried out on the example of a CNC ma-chine operator by means three-stage methods in accordance with the PN-N-18001, PHA and Risc Score. Despite the subjectivity of theassessment, there were differences in the results. Thus, it is advisable to choose a risk assessment method skilfully, for example by comparing several similar methods.

Law requires from employers to assess occupational risk in workplaces. A well-executed occupational risk assessment promotes the improve-ment of working conditions and reduces a negative impacts on employees’ health and life. Employers by conducting occupational risk assess-ment not only meet legal requirements but also benefit from it in two ways. They eliminate loss of an enterprise connected with employees’ absence, and they also decrease the probability of high compensation pay due to situations that have a destructive influence on the employees’ health. However, in order to benefit from such actions, firstly, it is necessary to select a risk assessment method properly, then to apply it and implement preventive measures. The paper presents the results of the occupational risk assessment carried out on the example of a CNC ma-chine operator by means three-stage methods in accordance with the PN-N-18001, PHA and Risc Score. Despite the subjectivity of the assess-ment, there were differences in the results. Thus, it is advisable to choose a risk assessment method skilfully, for example by comparing several similar methods.

In this research, we propose a method to evaluate both speed and accuracy performance of CNC machine tools at the same time. An important facet of the contouring performance of machine tools with computer numerical control (CNC) is the machining of workpieces within the desired accuracy and within as short a time as possible. For this reason, a method for evaluating speed and accuracy in the two dimensions of speed and error based on the actual trajectory, which is the actual movement trajectory with respect to the linear axes of CNC machine tools, has been proposed. In this research, we explain the method proposed for linear axes and propose a method to evaluate the speed and accuracy of a rotary axis and a linear axis in the two dimensions of speed and error by introducing a cylindrical surface to the combined movement of the rotary and linear axes. With experiments, we quantitatively evaluated the speed and accuracy of multiple CNC machine tools using two-dimensional representation with graphs of the actual speed and maximum error for the combined movement of a rotary axis and a linear axis.

The need to improve the accuracy of positioning of a servo-drive was the stimulus for research on a new sensorless method for compensation of thermal deformations of ball screws, enabling predictive compensation of the elongation of such a screw based on historical data. Models have been developed for the predictive compensation of thermal deformations of ball screws in CNC machines, in the form of single-directional multi-layered neural networks with error back-propagation (MLP), radial basis function neural networks (RBF) and Kohonen networks. Neural networks were developed with different structures and learning parameters, and these networks were compared. Models were evaluated in terms of the effectiveness of operation of the networks. The models were tested on real data.

In this study, a novel sensorless method is developed to estimate the friction in a ball screw system using asynchronous experimental data for a worktable position (WP) and the servo motor torque current (SMTC), which is collected by the FOCAS library functions from a FANUC digital control system. The corresponding time stamps are retrieved by a high-resolution performance counter in Windows. The fluctuations in friction over the whole travel distance of the worktable (WTDOW) are analyzed using a piecewise polynomial fitting algorithm to extract the instantaneous average friction value (IAFV) and the friction fluctuating signal (FFS). The IAFVs can describe the effect of the WP and the pitch errors of the shaft on the friction. The FFT results of the FFS show that the friction depends on the rolling tool, the pitch of the shaft and the refeeding of the balls in the ball screw and linear ball rail guides. The experimental results show that the estimated friction can capture the characteristic spectra of the FFS. The effects of the feed velocity and direction, the effects of the WP on the IAFV, and the frequencies and amplitudes of the significant FFT components are discussed. The nonuniform pitch errors of the shaft impact the repeatability of the IAFVs at a given WP. The variations in the mean value of the IAFVs with the feed velocity for tests at a fixed WP show nonconventional Stribeck behavior. These results also demonstrate that the friction force varies with the WP, which is not captured by Stribeck characteristics. This study provides an effective method to evaluate the performance of a ball screw system and to predict the friction in that system without the use of sensors.

Directed Energy Deposition (DED) of metal materials has been widely studied with Inconel 718 using in aerospace industry. Void generation in a deposited part causes deterioration of the mechanical strength. Hence, void should be avoided in the DED process. In this study, a mechanism of void generation was investigated through the dynamic observation by high-speed camera, the thermal observation and the evaluation of the porosity. The experimental result shows that solidification time has an influence on the void generation.

This paper presents results of 3-axis CNC machine tool diagnostics performed with XL-80 laser interferometer and XC-80 environmental compensation unit, including pressure, humidity and temperature sensors. Furthermore, the paper includes the methodology and results of conducted measurements of linear positioning errors, which supplied data for further analysis. The conclusion section presents important results of conducted experiments. Measurement results were presented in figures, charts and tables.