CNC Machine Tools Research Articles

Optimization of machining path for integral impeller side milling based on SA-PSO fusion algorithm in CNC machine tools

The five axis linkage Computer Numerical Control machine tool for integral impeller can achieve blade machining through side milling, which is of great significance for improving the machining accuracy, production efficiency, and long-term stability of integral impeller blades. This study is based on non-uniform rational B-spline curves and aims to reduce the surface over cutting or under cutting of integral turbine blades. The path planning of non deployable ruled surfaces was analyzed in depth through side milling, and the path planning model of the side milling cutter axis was solved through a fusion algorithm of simulated annealing algorithm and particle swarm optimization algorithm, in order to find the optimal path through iterative process. As the number of iterations increased, the error values of particle swarm optimization algorithm and simulated annealing particle swarm optimization fusion algorithm gradually decreased, with convergence times of about 7 and 6, respectively. The stable error value of the fusion algorithm was 0.253, which is 30.45% lower than that of the particle swarm optimization algorithm. The optimal number of iterations for solving the model using particle swarm optimization algorithm and fusion algorithm was the 7th, with range values of 0.0213 and 0.0165 mm, respectively. The tool axis trajectory surface optimized by the fusion algorithm was closer to the tool axis motion state compared to the initial tool axis trajectory surface. The range of the sum of mean squared deviations for single and global cutting was 0.0011–0.0198 and 0.046–0.0341, but the overall error value was relatively small. This study effectively reduces the envelope error of machining tools and improves machining accuracy, thereby solving the principle error of non expandable ruled surfaces in the motion trajectory of the blade axis of the integral turbine. This provides new research ideas for the intelligent development of Computer Numerical Control machining technology.

Multi-verse optimizer for thermal error modeling approach of spindle system based on thermal image

Since the spindle thermal error of CNC machine tools has a significant impact on machining precision, this paper introduces a unique approach for modeling spindle thermal error. Several key steps are involved in the proposed approach. First, the Fluke thermal imaging camera is employed for acquiring thermal image information of the spindle system. Second, the Gaussian filter is employed to denoise the thermal image sequence. Next, the temperature values at the measurement points are extracted from the thermal image sequence according to the mapping relationship between the grayscale value and the temperature value. Subsequently, critical temperature points are identified from thermal images using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the correlation coefficient method. Finally, the multi-verse optimized NARX neural network is employed to investigate the nonlinear prediction of thermal deformation. The research is conducted on the VMC-850E vertical machining center as the subject of study. The performance of the model is validated under conditions of idle spindle and 5000 r/min, comparing prediction results against traditional algorithms. The findings demonstrate that the non-contact measurement method based on thermal imaging successfully establishes the thermal error model, achieving a prediction accuracy of 0.1517 μm for the MVO-NARX model.

Multivariable Iterative Learning Control Design for Precision Control of Flexible Feed Drives.

Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment.

Application of a Hybrid Improved Particle Swarm Algorithm for Prediction of Cutting Energy Consumption in CNC Machine Tools

Application of a Hybrid Improved Particle Swarm Algorithm for Prediction of Cutting Energy Consumption in CNC Machine Tools

Energy consumption forecast model of CNC machine tools based on support vector regression optimized by improved artificial hummingbird algorithm

With the development of the manufacturing industry, energy consumption is growing rapidly, which makes the energy crisis and environmental problems become more and more serious. CNC machine tools play an essential role and are the primary energy consumption devices in the manufacturing industry. The accurate prediction of machine tool energy consumption can provide support for energy production plans and reduce energy waste. This paper proposes a novel energy consumption prediction model based on support vector regression (SVR) optimized by an improved artificial hummingbird algorithm (IAHA). Firstly, as the artificial hummingbird algorithm (AHA) may easily get trapped in a local optimum, an improved AHA based on chaotic mapping and local backtracking exploitation strategy is proposed. The chaotic mapping is used to initialize individual positions, which is good for maintaining population diversity. The local backtracking exploitation strategy is employed to improve the local optimization ability. The effectiveness and feasibility of the IAHA algorithm have been verified through 12 benchmark functions. Then, the IAHA algorithm is employed to optimize the parameters of the SVR, and the IAHA-SVR energy consumption prediction model is established. Finally, the effectiveness and feasibility of the IAHA-SVR model are verified through a case study.

Tool Wear Monitoring In Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter

In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service.

The Application of Uniform Design and Fuzzy Analysis to CNC Machine Tool Leveling Accuracy Adjustment

The Application of Uniform Design and Fuzzy Analysis to CNC Machine Tool Leveling Accuracy Adjustment

Research on thermal characteristics modeling of CNC machine tools based on submodel method

To improve the accuracy of thermal characteristics analysis of CNC machine tools, a modeling method for the thermal characteristics of CNC machine tools based on submodel method is proposed in this paper. In this method, the thermal-structural coupling calculation of the feed system and spindle system is initially completed in ABAQUS to obtain temperature and deformation results of the submodels. Subsequently, User Define Function (UDF) in Fluent is used to extract the temperature information from the surface of the submodels. The obtained surface temperature data is then imported into the overall machine model for fluid-structure coupling heat transfer calculation. The model developed in this study takes into account factors such as the dynamic heat generation of the heat source, the cooling fan system, and the secondary heat source effect of the cutting fluid. To validate the approach, a thermal characteristics experiment was conducted, and the research results demonstrate that the thermal analysis model established in this paper exhibits both high accuracy and efficiency. During the 180 min of machine operation, the thermal displacement between the tool tip and the workpiece increased. The maximum thermal deformation of the machine tool tip was 40.83 [Formula: see text], primarily observed in the X and Y directions.

Evidence‐based fault tree analysis of the hydraulic system in CNC machine tools

AbstractThe hydraulic system is an integral part of CNC machine tools. In analyzing the reliability of machine tool hydraulic systems, their failures are influenced by both aleatory and epistemic uncertainties. This paper utilizes the fault tree analysis method to address failure modes subject to epistemic uncertainty, using the interval rough number scoring method to evaluate the probability of such failures occurring. The resulting reliability calculation is termed as “subjective reliability”. For failure modes influenced by aleatory uncertainty, objective data combined with the Dempster–Shafer evidence theory is used to determine their failure probability, with the corresponding reliability calculation referred to as “objective reliability”. Finally, a comprehensive calculation of both subjective and objective reliability is conducted to determine the overall reliability of the hydraulic system, along with the ranking of the importance of basic events of fault tree. This methodology covers scenarios with small samples, sufficient data, and their combinations, offering extensive application prospects.

A Novel Temperature Rise Prediction Method of Multi-component Feed System for CNC Machine Tool Based on Multi-source Fusion of Heterogeneous Correlation Information

A Novel Temperature Rise Prediction Method of Multi-component Feed System for CNC Machine Tool Based on Multi-source Fusion of Heterogeneous Correlation Information

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.

Intelligent in-process enhancement technique for machining efficiency in CNC machine tools based on spindle power

In the realm of mechanical machining, adaptive machining techniques offer an efficient method. However, existing adaptive machining technologies cannot automatically identify the type of workpiece to be machined, nor can they set different control targets based on tool load-bearing capacity. This often requires manual and cumbersome operations, making the application of adaptive machining technology difficult to popularize. To address these challenges, this paper proposes an intelligent and efficient adaptive machining method. Specifically, a workpiece automatic classification algorithm based on machine tool spindle power is introduced. This algorithm classifies the machined workpieces according to the collected spindle power data, enhancing the convenience of adaptive machining technology. Also based on spindle power data, a time-series segmentation method is proposed, dividing spindle power into different subsequences according to the tools used in machining, which enhances the accuracy of adaptive machining technology. Furthermore, an adaptive feed rate control algorithm is designed based on fuzzy control theory, realizing intelligent and efficient adaptive machining of workpieces to improve machining efficiency. Finally, experiments are conducted to validate the effectiveness of the intelligent and efficient adaptive machining method. The proposed method effectively addresses the drawbacks of existing adaptive machining method, which require complex parameter settings. It simplifies the operational process, enhances the intelligence, and improves the practical applicability of adaptive machining techniques. This represents an innovative contribution to the field of adaptive machining techniques.

Online identification and feed-forward compensation of nonlinear friction in servo system based on RBF neural network model

In this paper, an online identification and compensation method of nonlinear friction based on radial basis function (RBF) neural network model is proposed for the influence of nonlinear friction on machining accuracy in the low speed process of servo feed system of CNC machine tools. First, a three-layer single-input-output RBF neural network model is established for describing the nonlinear friction of servo feeding system. Second, the neural network online learning algorithm is improved based on adaptive gain, which improves the stability and accuracy of the algorithm. Finally, experiments were carried out on a three-axis milling machine to compensate the friction in the servo feed system in real time based on the online identification results. The results show that the method can effectively improve the online identification accuracy and convergence rate, and effectively improved the low-speed performance of the servo feed system.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.

Reliability of probes for CNC machine tools

So far, the issue of degradation of touch-trigger probe accuracy during measurements on CNC machines has not been addressed. In the article, for the first time, a procedure for testing and predicting the accuracy and reliability of probes used in machine tools was proposed and experimentally verified. Reliability test procedure covering 30 million triggers was performed under laboratory conditions. Based on the obtained results, a concept of the probe reliability testing procedure was proposed, and an attempt was made to assess the impact of the operational process on the probe's metrological parameters. An important issue of this work is to develop a concept for the analysis and interpretation of the results of such a process by determining which parameters of the probe description provide the most information about the degradation process and how to predict the operating time of such a device.

A ball screw all-round error compensation technology based on novel hybrid deep learning for CNC machine tool

A ball screw all-round error compensation technology based on novel hybrid deep learning for CNC machine tool

FCE-based health status evaluation and IGM-based failure prediction of tool magazine manipulator for CNC machine tools

The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator’s health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator’s health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model’s prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability.

Application of Computer Virtual Reality Technology in Welding Simulation Training of Complex Structures Based on Unity3D

A virtual reality welding robot simulation system for practical application is developed based on the training and application of welding robot for practical application, which has important theoretical and practical application value for promoting the development of industrial robots in China. The simulation system is synchronized with the welding simulation training platform to achieve the purpose of VR simulation of the welding robot. By modeling the welding process, the process parameters can be inferred automatically, and the digital twin knowledge base for practical application is built. The OPC UA protocol is used to realize real-time communication with mobile devices. The digital twinning technology of CNC machine tool is studied through the simulation experiment of weld motion. This system lays a foundation for realizing the intelligentization of CNC machine tools.

Multi-objective optimization design for the table of a CNC machine tool based on bionic structures

To improve the static and dynamic performance of CNC machine tool tables, a multi-objective optimization design method based on bionic structures is proposed, which involves bionic design and size optimization design. Based on the bionics, four types of bionic structure tables for the original XK5032 machine tool were designed, namely bamboo cross-section bionic structure table, spider web bionic structure table, honeycomb bionic structure table, and prairie rushes bionic structure table. Static and modal analysis of the original and four types of bionic tables were conducted using finite element simulation methods validated through modal experiments. The results show that the overall mechanics of the four bionic tables have been improved compared to the original table. Using the entropy weight TOPSIS method, the prairie rushes bionic structure table was selected from four types of bionic tables. Conducting multi-objective optimization design on the prairie rushes bionic structure table, three optimization candidate schemes were obtained. The entropy-weighted TOPSIS method was used again to select the optimal solution from these three schemes, resulting in a 0.8% reduction in mass, an increase in first-order natural frequency by 2.8%, a decrease in maximum displacement by 3.2%, and a significant 21.6% reduction in maximum equivalent stress compared to the original table.

Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method

Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method