Performance Of Machine Tool Research Articles

Uncertainty propagation of initial conditions in thermal models

The operation of machine tools often demands a highly accurate knowledge of the tool center point’s (TCP) position. The displacement of the TCP over time can be inferred from coupled thermal models, which comprise a set of geometrically coupled heat equations. Each of these equations represents the temperature in part of the machine, and they are often formulated on complicated geometries. The accuracy of the TCP prediction depends highly on the accuracy of the model parameters, such as heat exchange parameters, and the initial temperature. Thus it is of utmost interest to determine the influence of these parameters on the TCP displacement prediction. In turn, the accuracy of the parameter estimate is essentially determined by the measurement accuracy and the sensor placement. Determining the accuracy of a given sensor configuration is a key prerequisite of optimal sensor placement. In this paper, we develop a thermal model for the discretization of realistic machine tool. On top of this model we propose two numerical algorithms to evaluate any given thermal sensor configuration with respect to its accuracy. We compute the posterior variances from the posterior covariance matrix with respect to an uncertain initial temperature field. The full matrix is dense and potentially very large, depending on the size of the discretized model. We first present a way to compute this approximation, which relies on the computation of the model sensitivities with respect to the initial values. Additionally, we present a low-rank tensor method which exploits the underlying system structure. We compare the efficiency of both algorithms with respect to runtime and memory requirements and discuss their respective advantages with regard to optimal sensor placement problems.

Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion

Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature information fusion features are built as inputs to the model, which is based on a self-attention mechanism to assign weights to the temperature information and fuse the features. Secondly, an improved direct normalization-based adaptive matrix approach is proposed, updating the background matrix using an autoencoder and reconstructing the adaptive matrix to realize domain self-adaptation. In addition, for the improved adaptive matrix, a criterion for determining whether the working conditions are transferrable to each other is proposed. The proposed method shows high prediction accuracy while ensuring training efficiency. Finally, thermal error experiments are performed on a VCM850 CNC machine tool.

Post-processing algorithm of five-axis CNC machine tool under rotation angle optimization

During the operation of 5-axis CNC machine tools, the angle deviation of the rotation axis greatly affects the tool path control. Therefore, a post-processing algorithm of five-axis CNC machine tool with optimized rotation angle was proposed. Firstly, the basic The structure of the machine tool was analyzed, the Hausdoff distance was used to obtain the matching error between the actual machined surface and the difference surface, and the error compensation method was used to compensate the obtained error, and the rotation axis angle of the machine tool was completed according to the processing result. Based on the optimization results, the machine tool coordinate system was established, and the machine tool post-processor was developed through the coordinate transformation results and integrated into the relevant software to realize the post-processing of the machine tool. The experimental results show that the proposed method achieves better processing results when used for post processing of machine tools.

A real-time dual NURBS interpolator with optimised control of flexible acceleration and deceleration for five-axis CNC machining

The limited computing capacity makes it difficult to plan a suitable feedrate profile in real-time for high speed and high accuracy machining of five-axis parametric toolpaths. In this paper, a real-time interpolation algorithm with optimised control of flexible acceleration and deceleration (acc-dec) for the dual NURBS toolpath is proposed. The toolpath is marked as subsegments with similar geometric properties by introducing the five-axis curvature. Machine kinematic and toolpath geometry constraints are considered in the kinematic parameter constraint model. Initial feedrate profiles are solved in a dynamic 3D window which preserves the motion performance of machine tools to a great extent. Convolution is used to smooth the initial feedrate profile to achieve a higher order continuity over the global range. Feedrate fluctuations caused by imprecise parameter interpolation are eliminated through modifying each interpolation periods. Resampling adjusts the position of interpolation points and unify the interpolation periods. All operations mentioned are in series and real-time is strictly guaranteed. Effectiveness of the developed algorithm is validated in simulations and also experimentally on a Self-developed-NC controlled 5-axis machine tool.

Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Influence of bolt creep induced pre-tightening force relaxation on dynamic response of the bolted joint system

The bolted joint is an important link that affects the accuracy maintenance of the heavy-duty machine tool. Due to bolts of the crossbeam are under a preload of several hundred kN for an extended period, the bolt gradually creeps and elongates as the machine tool's service time increases. This relaxation of the preload results in a degradation of the bolted stiffness, weakening the dynamic response of the bolted joint system. To reveal that the degradation law of crossbeam bolting performance of the heavy-duty machine tool induced by bolt creep, based on the Norton-Bailey(NB) model, a method for analyzing the creep of bolts with precision threads is proposed, and the influence of thread geometry and friction coefficient on the creep stress relaxation of bolts is studied. Considering the interface contact, an interface contact stiffness model is derived based on fractal theory. The dynamic equation of the bolted beam system is established using the Matrix27 stiffness matrix. The dynamic response of the bolted system, resulting from the degradation of interface stiffness due to bolt creep, is analyzed. The results show that the stress relaxation of bolt creep is not only related to creep parameters, but also related to the mate-thread interaction. The dynamic model with Matrix27 stiffness matrix is comparable to experiment in calculation accuracy and efficiency, which provides technical guidance for machine tool dynamics analysis and effectively solves the scientific problem of beam bolting performance degradation during the service of heavy-duty machine tools.

Research on the Magnetorheological Finishing Technology of a High-Steepness Optical Element Based on the Virtual-Axis and Spiral Scanning Path.

Magnetorheological finishing (MRF) of aspherical optical elements usually requires the coordination between the translational axes and the oscillating axes of the machine tool to realize the processing. For aspheric optical elements whose steepness exceeds the machining stroke of the equipment, there is still no better method to achieve high-precision and high-efficiency error convergence. To solve this problem, an MRF method combining virtual-axis technology and a spiral scanning path is proposed in this paper. Firstly, the distribution law of the magnetic induction intensity inside the polishing wheel is analyzed by simulation, the stability of the removal efficiency of the removal function within the ±7∘ angle of the normal angle of the polishing wheel is determined, and MRF is expanded from traditional single-point processing to circular arc segment processing. Secondly, the spiral scanning path is proposed for aspherical rotational symmetric optical elements, which can reduce the requirements of the number of machine tool axes and the dynamic performance of machine tools. Finally, an aspherical fused silica optical element with a curvature radius of 400 mm, K value of -1, and aperture of 100 mm is processed. The PV value of this optical element converges from 189.2 nm to 24.85 nm, and the RMS value converges from 24.85 nm to 5.74 nm. The experimental results show that the proposed combined process has the ability to modify curved optical elements and can be applied to ultra-precision machining of high-steepness optical elements.

Condition monitoring of a CNC hobbing cutter using machine learning approach

The state of cutting tools profoundly influences the efficiency of the machining processes within the manufacturing industry. Cutting tool faults are highly undesirable and can adversely impact the performance of machine tools, leading to a shortened operational lifespan. Consequently, it is imperative to minimize power consumption by closely monitoring the condition of cutting tools. This necessitates implementing an effective supervision system to continually assess and predict potential faults. In simpler terms, this entails identifying issues that could compromise the lifespan of cutting tools before they escalate into problems like wear, breakage, or complete failure. This proactive approach ensures the optimal and efficient utilization of cutting tools, reduces the need for maintenance and repair, and enhances process stability, among other benefits. In this paper, a novel approach of machine learning for the condition monitoring of hobbing cutters used in Computer Numerical Control Machines (CNCs) was built. Vibration signals from hobbing blades were recorded under various conditions, including healthy and faulty states. The histogram presents a comprehensive overview of the statistical distribution for five variables related to the studied dataset. MATLAB code and scripts are utilized for extracting relevant statistical features, and for identification of the most relevant features, decision tree algorithms were used. For training the ML algorithms the hyperparameters were selected by the Grid Search Method and the Principal component analysis (PCA) was enabled for the reduction of dimensionality and to simplify the data set. The various conditions of hobbing cutters were then classified using tree-based classification models, giving 100% classification accuracy. It helps to develop a novel condition monitoring system for CNC hobbing cutters using machine learning methods to identify problems in hobbing blades. This would ultimately lead to lower power consumption and enhanced performance of machine tools.

Intelligent Solutions for Machine Tools Using System Sinumerik

The management of computerized numerical control (CNC) machines in production processes often involves complexities associated with programming efficiency and precision. This study investigates whether the Sinumerik system can enhance the productivity and flexibility of CNC machine programming. The integration of the Sinumerik system into CNC machines will lead to increased programming efficiency and reduced errors, thereby improving overall productivity in manufacturing operations. The study utilized a comparative analysis approach, examining the programming capabilities of CNC machines with and without the Sinumerik system. This involved the use of the Sinutrain Operate training program and the ShopTurn software environment to simulate and analyse different programming methods and their impacts on machine tool performance. Results indicate that the Sinumerik system significantly enhances the programming flexibility and efficiency of CNC machines. Machines equipped with Sinumerik demonstrated a higher precision in component production with considerably reduced programming times. The use of advanced managerial systems and interfaces provided by Sinumerik allowed for a streamlined programming process and minimized human error, confirming the system's effectiveness in improving manufacturing productivity.

Analysis of precision loss of double-nut ball screw considering dimensional error and preload degradation

Abstract A ball screw is a critical drive component capable of converting force into motion, with widespread applications in the high-precision mechanisms of various machine tools. The degradation in precision attributed to ball screw wear significantly impacts machine tool accuracy. Current ball screw wear models lack consideration for the ramifications of radial forces and ball size errors.This study introduces a pioneering approach by formulating a coupled model that integrates screw wear, ball wear, and preload degradation. Through rigorous analysis, we discern the multifaceted influences of axial force, radial force, preload adjustments, and ball size precision on the positional accuracy of ball screws.By elucidating the intricate interplay among these parameters, this study offers crucial theoretical insights into the selection and optimization of ball screws, thereby fostering advancements in machine tool design and performance.

Construction and Analysis of Cutting Force and Stability Prediction Model in CNC Milling Thin-Walled Parts Machining

Thin-walled parts are widely used in precision instruments, automobiles and other fields for their high strength, light weight and other characteristics. However, in the process of CNC milling, as the thickness of the parts decreases, their rigidity gradually decreases, resulting in the machining process is very easy to produce vibration, and even chatter, which seriously affects the accuracy and surface quality of the parts. Traditional manufacturing methods usually use conservative cutting dosage to reduce vibration, but this greatly limits the performance of CNC machine tools and cutting tools. In order to solve this problem, this paper thoroughly investigates the dynamic characteristics of the cutting process of thin-walled parts, and constructs a cutting force and stability prediction model. Through a combination of theoretical analysis, experimental verification and numerical simulation, this paper comprehensively analyses the impact of cutting parameters, tool geometry, workpiece material properties and other factors on cutting force and machining stability. The research results not only help to improve the cutting theory of thin-walled parts, but also provide an important technical support for the realisation of high-efficiency and high-precision machining. The results of this paper show that by optimising the cutting parameters and tool geometry, the cutting force can be effectively reduced and the machining stability can be improved, so as to achieve efficient and high-precision machining of thin-walled parts. In addition, this paper also explores the causes and mechanisms of vibration in the machining process, providing a theoretical basis for the development of targeted vibration control measures. The research in this paper is important for promoting the development of thin-walled parts machining technology, not only helps to improve the processing efficiency and quality of parts, but also helps to promote the manufacturing industry to a more efficient, more precise direction.

Self-powered sensor for online monitoring of eccentricity faults in rotating machinery and its application in spindle eccentricity monitoring of machine tools

With their unique structures and functionalities, rotating machineries are widely employed in various high-end industries. However, eccentric faults of rotors during continuous high-speed operation represent a critical factor in causing safety accidents. Therefore, real-time monitoring of rotor health is crucial for promptly identifying potential safety hazards and fault indications, enabling preventative maintenance, and enhancing safety performance. Nonetheless, traditional monitoring methods rely on sensors installed on rotating components, which are prone to interference in rotational environments, involve complex installation and maintenance procedures, and pose significant challenges to rotor health monitoring. In this study, a self-powered rotor eccentricity monitoring sensor (RE-TENGs) is designed and fabricated, integrating triboelectric nanogenerator technology. This sensor utilizes the rotor as the self-sensing entity, requiring no external power supply and featuring simplified installation, enabling real-time monitoring of rotor health during rotational machinery operation. A linear relationship between the RE-TENG's eccentric response and electrical characteristics was established through dynamic testing. Furthermore, the actual eccentricity orientation of the rotor was successfully determined by employing vector analysis and synthesis methods with multiple RE-TENGs coupled. To further validate the practicality and accuracy of RE-TENGs, a monitoring testbed for eccentric faults of the spindle rotor in a CNC machine tool was constructed, with the spindle rotor's eccentric fault simulated through misaligned installation. Experimental results demonstrate that RE-TENGs can effectively detect rotor faults induced by eccentric forces during the actual operation of machine tools, accurately determining the eccentric direction and displacement. With an actual angle recognition error of less than ±0.824° and an eccentric distance recognition accuracy exceeding 98 %, the exceptional performance of RE-TENGs in monitoring and precise identification of eccentric faults in rotating machinery rotors is unequivocally verified. By integrating triboelectric nanogenerator (TENG) technology, this study realizes real-time monitoring and precise identification of eccentric faults in rotating machinery rotors. It provides a novel solution for health condition monitoring of rotating machinery and offers a reference for overcoming the cumbersome installation and complex wiring of traditional sensors in practical applications.

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

Effect of Machine Structure on Cutting Force Considering Different Working Conditions in Micro-End-Milling

Machine structure determines the performance of machine tools. The study of machine deformation contributes to the stability of machining processes, which in turn improves machining accuracy and surface quality. Vertical structure is the weak part of the horizontal micro-milling machine, which is the main source of deformation. Considering that the micro-milling cutting parameters and machine deformations are at the micron scale, it remains to be investigated whether such deformations cause changes in the cutting volume and thus affect the cutting forces. For this purpose, this paper investigates the deformation of the machine structure caused by various operating conditions and its effect on the cutting force. The finite element method (FEM) was used to calculate the deformation [Formula: see text][Formula: see text] of the clamp-workpiece. To verify the effect of machine structure on cutting force under different working conditions, the Al 6061 full immersion slot cutting experiments were conducted by considering different cutting parameters. The results show that the deformation of the machine structure has an effect on the cutting forces, and the degree of effect varies depending on the working conditions.

Influence of Machine Tool Operating Conditions on the Resulting Circularity and Positioning Accuracy

The operating conditions of the production process significantly influence the resulting dimensional and form accuracy of the workpiece. The operating conditions include the position of the workpiece location, with internal and external heat sources influenced not only by the machine location but also by its operation. In addition, there are the cutting conditions and the feed rate requirements of CNC machine tools. These changes, such as workpiece position, feed rates, and machine heat load, are further reflected in the ability of the machine to run at the position required and interpolate within the given tolerances of circularity. For the accuracy and repeatability of positioning, the machine was set up according to ISO 230-2 and for the circular interpolation tests according to ISO 230-4. The obtained results show the importance of attention to the appropriate setting of the operating conditions of the production process, where the knowledge of the geometric accuracy of the CNC machine tool in its working space can systematically increase the manufacturing accuracy itself or be another tool suitable for predicting the dimensional and form accuracy of workpieces.

A Physics-Based Model-Data-Driven Method for Spindle Health Diagnosis, Part I: Modeling of Geometric Faults

Abstract The spindle determines the performance of machine tools; hence, monitoring its health is essential to maintain machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location are correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyze the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III.

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools.

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40-60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.

An approach for crucial geometric error analysis and accuracy enhancement of gantry milling machines based on generalized correlation sensitivity

Geometric errors in gantry milling machines with long-span beams and large travel can seriously degrade workpiece accuracy. Sensitivity analysis and compensation of geometric errors with coupling and fluctuation can significantly improve the machining performance of machine tools. An accuracy enhancement approach for gantry milling machines is proposed in this paper. Firstly, the geometry error analytic fluctuation eigenfunctions are established by using the high-order Fourier series fitting. Secondly, a non-linear generalized correlation analysis model is proposed to identify the crucial errors that significantly affect the machining accuracy. Thirdly, the motion commands compensated for geometric errors are calculated. Specifically, compared to traditional inverse kinematics, the proposed error compensation process is simple and suitable for most machine tools with orthogonal linear axes. Finally, comparative cutting experiments are performed. The accuracy of the test piece is improved by approximately 34.7 % with error compensation. The effectiveness of the proposed method is validated.

Sustainable manufacturing through empowering machine tools with IoT innovations: a literature review

In the 21st century, sustainability takes the front seat as one of the main objectives while designing and developing new technologies. Machine tools have significant contributions in fuelling all four industrial revolutions. One of the major stepping stones to the fifth Industrial Revolution is making these machine tools sustainable. In this paper, we review the major developments driven by the integration of internet of things (IoT) in the field of machine tools which contribute to achieving a sustainable future. This integration of IoT in machine tools is also significant in contributing to different sustainable development goals of the United Nations. Through this work, we aim to reach a better understanding of current improvements in machine tool operations and ecosystem achieved through IoT innovations. The future research directions that are opening in this domain and their scope and limitations are also covered.

A methodology for laser tool setters calibration and its precise mathematical model

On-machine tool setting is a pivotal approach in achieving intelligent manufacturing, and laser tool setters have become a crucial component of smart machine tools. Laser tool setters play a crucial role in precisely measuring the dimensions of cutting tools during the part machining process, focusing on tool length and diameter. As a measuring instrument, the positions of the laser axis of the laser tool setter need to be accurately calibrated before use. However, in actual calibration scenarios, traditional calibration methods face challenges due to installation errors in the tool setter and geometric errors in the measuring rod. To address this issue, this study proposes a novel calibration method. Initially, the calibration mechanism of the laser beam axis is established. Based on the accurate mathematical model of the laser beam and the measuring rod, and using the polygon clipping algorithm, the mathematical mechanism of the laser tool setter's work is established. Then, a novel method is introduced to calculate the compensation distance between the laser beam reference point and the rod bottom center point at each moment during calibration. Furthermore, by utilizing the kinematic chain of the tool setter calibration system, a new calibration method is developed to accurately calibrate the position of the laser beam axis in the machine tool coordinate system. Finally, the accuracy of the calibration method is verified through simulation experiments and calibration tests. This method improves the calibration accuracy of the tool setter, and the mathematical model of the laser tool setter can be extended to the measurement of tools, thereby improving the precision of tool measurements. This research significantly improves the efficient production performance of smart machine tools.