Numerical Control Machine Tools Research Articles

A 3-PRS parallel numerical control machine tool swing angle deviation detection and compensation method

A method for detecting and compensating the swing angle deviation of a 3-PRS parallel structure CNC machine tool is proposed. The probes fixed on the machine tool worktable are used to pair two standard ball heads with different lengths and the same diameter installed on the spindle. The coordinates are collected, the machine tool swing angle deviation is calculated and obtained, and the calculation result is automatically written into the machine tool compensation file, which replaces the manual detection and compensation of the swing angle error of the parallel machine tool, improves the accuracy and reliability of the detection data, and improves the automation of flexible production. Degree.

Machine Straightness Error Measurement Based on Optical Fiber Fabry–Pérot Interferometer Monitoring Technique

Abstract In this research, we propose an Error Separation Technique (EST) based on optical fiber sensors for on-machine straightness error measurement. Two fiber optic Fabry–Pérot interferometers have been developed serving as two displacement sensors. The displacement distance is computed according to the reflected spectrum from interferometers, which can achieve a sub-micrometer resolution. The two-point method has been employed to separate the straightness error of the slides and the profile error of a fine-polished standard block. The spacing distance between two interferometers is determined by the diameter of optical fibers so that the EST's resolution has the potential to reach the sub-millimeter scale. In the experiment, the straightness error has been measured on a commercially available computer numerical control machine tool, and the measurement has been conducted on its x-axis. The spacing distance between two optical fiber sensors is 1.5 mm which equals the EST's resolution along the machine tool's x-axis. The separated profile error of the measured standard block is around 30 µm which has been verified by a high precision Coordinate Measurement Machine (CMM). The magnitude of the separated straightness error is around 40 µm. This technique is flexible and simple to be conducted, which can contribute to the micro-machine tool calibration and other straightness error applications.

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time–frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time–frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the time–frequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time–frequency characteristic quantity in real time.

Ranking of Key Components of CNC Machine Tools Based on Complex Network

Complex networks have become a center of interdisciplinary research. The robustness and reliability of complex systems can be determined by analyzing the characteristics of networks. The computer numerical control machine tool (CNCMT) is a typical complex system, and reliability is the key factor that affects the processing quality and performance of the CNCMT. The process of quantitatively determining the key functional components of CNCMT and carrying out reliability growth experiments on them remains difficult. A new importance-ranking method for the key functional components of the CNCMT is proposed in this study. First, the real characteristics of the CNCMT are analyzed, and the complex network of the CNCMT is constructed. Then, we study various indicators of the network and determine that the network has small-world characteristics. Finally, a variety of complex network characteristic indicators are fused, the weight of each indicator is determined based on the analytic hierarchy process (AHP), the importance of each node is quantitatively determined, and the fused indicators are compared with a single indicator.

Energy Saving Design Optimization of CNC Machine Tool Feed System: A Data-Model Hybrid Driven Approach

The moving components and servo control systems of computer numerical control (CNC) machine tools spent a substantial energy but received less attention regarding energy saving. This article presents a novel energy saving methodology of feed system using a data-model hybrid driven design optimization approach for the integrated design of moving component structural geometry and control system. Surrogate models are built based on the design of experimental data to avoid time-consuming finite element analysis. The energy consumption mechanism model is constructed via energy flow analysis method. The data-model hybrid modeling method realizes the collaborative optimization of the machine tool feed system structure and controller, while simultaneously accelerating the optimization process. A multi-objective trade-off model with energy consumption and deformation is established. Three different multi-objective optimization approaches are taken to drive the developed multi-objective optimization model. The control performance of the feed system is demonstrated through simulation analysis. Results indicate that the optimized scheme can reduce energy consumption by over 10% and maximum deformation of the tool holder by over 14%. Note to Practitioners—This article is motivated by the problem that a significant energy consumption occurs when the CNC machine tool feed system continuously drives the moving components to move, accelerate or decelerate. The previous studies to cope with this problem broadly are to separately analyze energy-saving characteristics of the structure or control system of the CNC machine tool feed system. This article suggests a novel data-model hybrid approach for energy saving of machine tool feed system, which simultaneously integrates design of moving component structural geometry and servo control system. In this article, we mathematically derive an energy consumption mechanism model of the CNC machine tool feed system. By using surrogate assisted method, a multi-objective trade-off model with energy consumption and deformation is established. Simulation experiments indicate that this approach is feasible and can significantly reduce the energy consumption of CNC machine tool feed system.

Application of programmable logic control in the nonlinear machine automation control using numerical control technology

Abstract To explore the application of programmable logic control (PLC) in the automatic control of nonlinear machine tools, the application of PLC in the nonlinear electronic system control is studied. First, the author takes PLC technology in the numerical control machine tool electrical control application as the research equipment, PLC technology in the numerical control machine tool electrical control application is divided into four types. Then, according to the need of management number, the overall structure of PLC is designed and developed, and the concept of operation modularization is adopted to realize the decentralized development of PLC. There are three functional modules: repair, compile, and simulate. Finally, the machining performance of the nc system is evaluated experimentally. The processing time difference of two kinds of machine tools is compared. The average processing time of the original (Numerically controlled) NC system is 3.06 s, and the average processing time of the embedded soft PLC system is 2.01 s, with a gap of 1.04 s between the two systems. The Computer Numerical Control (CNC) machining improvement of 1 s is significant in the processing efficiency of CNC machine tools. The numerical control system embedded in the PLC system is more stable in the machining time, so it can be concluded that the numerical control-embedded soft PLC system has carried on some improvements and upgrades to the operation of machine tools. PLC has relatively strong reliability, stability, and flexibility and is widely used in industrial control systems. In the production process of CNC machine tools, the application of PLC can improve the automatic control level of machine tools. The application of PLC can improve the automatic control level of machine tools and realize the effective control of each link in the production process of CNC machine tools, such as the start-stop program, the automatic replacement of cutting tools in the production process, the automatic diagnosis, and treatment of various faults in the production process, which can be realized through the application of PLC technology.

On-site calibration method of the AACMM based on a high-precision CNC machine tool

An articulated arm coordinate measuring machine (AACMM) is a kind of instrument used to test workpieces in the industrial field, and its measurement uncertainty depends on the on-site environmental factors to a certain extent. If there is a problem with the AACMM during on-site testing, it needs to be re-calibrated to ensure the measurement uncertainty. In this work, a fast on-site calibration method for the AACMM is proposed using a high-precision computer numerical control (CNC) machine tool. The AACMM is fixed on the machine tool table. The calibration artifact is clamped on the machine tool spindle. This calibration artifact can move to any point in the global space of the AACMM along with the operation of the spindle. Then, a virtual calibration artifact with calibration points is achieved using the CNC machine tool. The positions of these points are calculated using the improved Hammersley sequence. The calibration method using a high-precision CNC machine tool is experimentally verified through the single-point articulation performance test of ASME B89.4.22-2004 and the length measurement test of ISO 10360-12. The calibration method proposed in this work only uses an on-site high-precision CNC machine tool, one of the most common machines in many industrial fields, as the calibration instrument for the AACMM calibration. No extra equipment is required, which is convenient and beneficial to calibrate the AACMM. In addition, the CNC machine tool can construct different kinds of virtual structures for the AACMM calibration, such as virtual rods, virtual circles, virtual balls and even non-uniform 3D artifacts with complex curved surfaces. This may give rise to many novel calibration methods and further improve the calibration accuracy.

Feature Points Recognition of Computerized Numerical Control Machining Tool Path Based on Deep Learning

In the processes of feed rate planning, interpolation and tool path optimization of Computerized Numerical Control (CNC) machining, feature point recognition of tool path is an essential operation. Accurate identification of feature points is the premise for partitioning the tool path that affects the accuracy and efficiency of CNC machining. Current research on feature point recognition of tool path mainly relies on the hand-crafted approach, of which the result is sensitive to the values of some manually defined threshold. Therefore the approach heavily relies on the human experience. In this paper, a novel deep learning-based approach is presented that can automatically and precisely recognize the feature point of the tool path. A set of geometric descriptors is first defined for each Cutter Location (CL) point and then conversed to images that feed to the deep learning pipeline. A residual learning-based Convolutional Neural Network (CNN) called Feature Point CNN (FP-CNN) is designed that takes the conversed images as input and the recognized results as output. Extensive experiments on some industrial parts are conducted to validate the effectiveness and the advantage of the proposed network. Results show that the proposed approach has good performance in identifying accuracy and recall, which is much superior to two types of benchmarks and does not involve any human intervention.

Identification of the model parameter for milling process simulation with sensor-integrated disturbance observer

This study presents a novel parameter identification method using a disturbance observer for computer numerical control (CNC) machine tools with ballscrew-type feed drive systems, which are generally adopted in industrial applications. In the proposed method, the disturbance force acting on the feed drive system was estimated using a multi-inertial disturbance observer using both CNC servo and accelerometer signals. Model-based milling simulation is performed with reference to the estimated disturbance forces, and hence model parameter identification can be systematically achieved. In the milling process model, the structural vibration and cutting forces caused by machine-process interactions are considered. Furthermore, the model is considered the disturbance forces transmitted to the feed drive system via a mechanical structure. To verify the proposed method, a basic end-milling experiment was conducted. It was confirmed that the multi-inertial disturbance observer assisted by the accelerometer is equivalent to the dynamometer in terms of the disturbance force estimation within the frequency range of 600 Hz and less. The model parameters identified using the proposed method were almost identical to those obtained using conventional methods. However, the identification results of the modal parameters and tool eccentricity were slightly different from those estimated using the conventional methods. The milling simulation using the identified parameters agreed with the measurement results obtained using the dynamometer. Hence, inverse analysis of the in-process data can effectively improve the identification accuracy of the model parameters.

An Improved Robust Thermal Error Prediction Approach for CNC Machine Tools

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, the adaptive LASSO method enjoys the oracle property of selecting temperature-sensitive variables. After the temperature-sensitive variable selection, the XGBoost algorithm is further adopted to model and predict thermal errors. Since the XGBoost algorithm is decision tree based, it has natural advantages to address the multicollinearity and provide interpretable results. Furthermore, based on the experimental data from the Vcenter-55 type 3-axis vertical machining center, the proposed algorithm is compared with benchmark methods to demonstrate its superior performance on prediction accuracy with 7.05 μm (over 14.5% improvement), robustness with 5.61 μm (over 12.9% improvement), worst-case scenario predictions with 16.49 μm (over 25.0% improvement), and percentage errors with 13.33% (over 10.7% improvement). Finally, the real-world applicability of the proposed model is verified through thermal error compensation experiments.

Design and research of digital twin machine tool simulation and monitoring system

The numerical control machine tool is an important part of manufacturing. The speed and accuracy of the machine tool simulation and the monitoring of machine physical model movement, model collision, and operation health are the most common bottlenecks. The previous studies on these problems are discrete and poor in real time. In this paper, combined with the digital twin (DT) technology, a perception-monitor-feedback system architecture is constructed. Based on the improved Gilbert–Johnson–Keerthi (GJK) distance algorithm, the collision information of tool and machine tool can be better detected during the simulation and monitoring of machine tool movement, and the more real workpiece shape can be displayed. Based on the model synchronous motion driven by the production perception data and its potential information extraction, the tool wear online monitoring is carried out. The tool wear online monitoring is performed based on the production perception data–driven model synchronous movement and extraction of its potential information. Finally, the usability and efficiency of the system are verified by an example of a typical shaft part processed by a numerical control machine tool.

Analysis and Design of Cooperative Control Solutions for Electrical Systems of Laser CNC Machines

In order to further improve the collaborative control efficiency of the electrical system of the laser CNC machine tool, an electrical system based on the ultrafast laser CNC machine tool is proposed. The design of the collaborative control scheme is discussed from three aspects: the overall control scheme design, hardware structure design, and control software implementation of the electrical system of the laser numerical control machine tool. The experimental results show that the length of the metal sheet processed by the electrical system of the laser CNC machine tool is 5.3 cm and the width is about 3.6 cm, and the actual processing aperture size will also change according to the set number of variable radius turns, and more precise processing can be enabled in the online test. The research results show that this electrical collaborative control system scheme has strong practicability and feasibility. With the help of system upgrades and optimization, the efficient integration of laser processing technology and numerical control technology can be better achieved. It should be noted that the machining process of the machine tool is also required to make in-depth improvements, which can maximize the operating efficiency and machining accuracy of the electrical system of the CNC machine tool. The laser CNC machine tool researched and developed in this paper verifies the rationality and feasibility of the collaborative control scheme through practical experiments, which can meet the processing requirements and realize the integration of laser processing technology and numerical control technology. High machining efficiency and machining accuracy.

Simultaneous measurement of 5DOF spindle error motions in CNC machine tools.

We present a method for the simultaneous measurement of 5 degree-of-freedom (DOF) spindle error motions in computer numerical control (CNC) machine tools and develop a measurement system. The measurement system uses polarization-maintaining fiber coupled with a dual-frequency laser as the light source. The axial error motion of the spindle is measured by heterodyne interferometry, and other 4DOF error motions are obtained by collimation measurement. Based on the measurement system, a comprehensive measurement model of spindle error motion was established. The influence of system error on the measurement accuracy of the spindle tilt error motion and radial error motion was analyzed. According to the measurement model, a corresponding data-processing method is proposed, which removes some systematic errors. A series of experiments was conducted to verify the feasibility and effectiveness of the proposed measurement system and the corresponding measurement model.

Intelligent Health Monitoring of Machine Tools Using a Bayesian Multibranch Neural Network

Online health monitoring of machine tools is an essential technique for tool life extension, manufacturing productivity improvement and product quality improvement. In the era of industrial big data, numerous deep learning (DL)-based methods have been proposed to achieve these goals. However, in complex and dynamic manufacturing processes, practical concerns such as uncertainty quantification and anti-noise capabilities of the DL-based methods are rarely considered. Thus, in this study, a novel multi-branch Bayesian Neural Network (BNN) is developed for the reliable and robust online health monitoring of Computer Numerical Control (CNC) machine tools. With the proposed model, the heterogeneous fault information extracted from multiple sensors can be simultaneously integrated in a deep convolutional neural network (DCNN)-multiple layer perceptron (MLP)-based multi-branch neural network to enhance the health monitoring accuracy and robustness. Furthermore, the proposed multi-branch neural network is extended into a BNN to improve its uncertainty quantification capabilities. The proposed method is evaluated on the tool wear tests of three cutting tools. Tool wear estimation results indicate that the proposed method outperforms comparative methods and achieves the best prediction accuracy and robustness on all three health monitoring tasks investigated in this study. We also found that the proposed method can accurately classify tool wear stages and reach up to 95% mean classification accuracy, which is the best among comparative methods. Also, measures, such as coverage probability of estimation interval (EICP) and normalized mean estimation interval width (NMEIW), are used to assess the capability of quantifying the confidence intervals (CIs) of the tool wear estimations. Results show that the proposed method achieves superior CIs quantification performance with the average EICP and NMEIW values of 95.77% and 0.27 on all three health monitoring tasks.

A zone-layered trimming method for ceramic core of aero-engine blade based on an advanced reconfigurable laser processing system

Ceramic structural parts are one of the most widely utilized structural parts in the industry. However, they usually contain defects following the pressing process, such as burrs. Therefore, additional trimming is usually required, despite the deformation challenges and difficulty in positioning. This paper proposes an ultrafast laser processing system for trimming complex ceramic structural parts. Opto-electromechanical cooperative control software is developed to control the laser processing system. The trimming problem of the ceramic cores used in aero engines is studied. The regional registration method is introduced based on the iterative closest point algorithm to register the path extracted from the computer-aided design model with the deformed ceramic core. A zonal and layering processing method for three-dimensional contours on complex surfaces is proposed to generate the working data of high-speed scanning galvanometer and the computer numerical control machine tool, respectively. The results show that the laser system and the method proposed in this paper are suitable for trimming complex non-datum parts such as ceramic cores. Compared with the results of manual trimming, the method proposed in this paper has higher accuracy, efficiency, and yield. The method mentioned above has been used in practical application with satisfactory results.

Reliability allocation method of comprehensive weight computer numerical control machine tool based on failure correlation and factor correlation

AbstractAccording to the shortage of independent hypothesis allocation models between influence factors and subsystems, a reliability allocation method considering influence factor correlation and subsystem failure correlation is proposed. The CNC (Computer Numerical Control) machine tool is taken as the research object. Using Copula function to analyze failure correlation analysis between subsystems, establish subsystem complete reliability model and integrated importance measure model under failure correlation. The four factors of influencing reliability allocation are subsystem complexity, integrated importance measure of subsystem based on failure correlation, failure frequency, and specific gravity ratio of failure downtime. The correlation analysis among the influencing factors was carried out by using gray correlation theory, and the subjective weight of the subsystem was determined. The IEW‐VIKOR (Information Entropy Weight‐Vlsekriterijumska Optimizacija I Kompromisno Resenje) method is used to determine the objective weights of subsystems. The comprehensive weight of the subsystem is also obtained through combinatorial empowerment. The reliability allocation of the CNC machine tool subsystem was carried out according to the failure correlation reliability model, and the results of failure independence and failure correlation hypothesis allocation were compared and analyzed. When the reliability index is the same, the reliability allocation method proposed in this paper can assign lower reliability to each subsystem, which is of great significance to improve the reliability of CNC machine tools and reduce the difficulty of reliability design

Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools

Thermal errors are one of the main factors affecting the accuracy of high-precision computer numerical control machine tools. Modeling and compensation are the most common approaches for reducing the influence of thermal error on machine tool accuracy. Accuracy and robustness are the key indicators of machine tool thermal error prediction models, especially under different working conditions. Existing thermal error modeling algorithms provide only point predictions of the thermal error; however, interval predictions of the thermal error are important for understanding the stochastic nature of the thermal error prediction and analysis of reliable risk. To address these challenges, this study proposes a novel thermal error modeling method based on Gaussian process regression (GPR) that provides interval predictions of thermal error and achieves high prediction accuracy and robustness. First, multiple batches of experimental data are used to establish the GPR thermal error model to ensure sufficient modeling information. Second, while existing methods select temperature-sensitive points (TSPs) before modeling, the GPR algorithm can adaptively select TSPs during training of the thermal error GPR prediction model. Third, the proposed model provides interval predictions of thermal errors for evaluating the thermal error prediction reliability. The prediction effects of the GPR model are compared with those of existing thermal error models. The experimental results indicate that the proposed model has the highest prediction accuracy and robustness under different working conditions of the tested compensation models. Furthermore, thermal error compensation experiments are conducted to verify the effectiveness of the proposed model.

Development of low-cost heterodyne interferometer with virtual electronic phasemeter

Commercial laser interferometers are conventionally used to measure the positioning error of a long linear stage in multiaxis computer numerical control machine tools. However, commercial laser interferometers are costly and difficult to use. Therefore, a low-cost photodetector-based heterodyne interferometer combined with an electronic phasemeter module was proposed for precise measurement of the positioning error of a long linear stage. The proposed heterodyne interferometer was combined with a virtual electronic phasemeter that employs a self-developed signal-processing technique. Our core algorithm and proposed photoelectric-signal-processing technique were developed using the LabVIEW human–machine interface. Moreover, to verify the performance of the proposed heterodyne interferometer, a laboratory-built prototype was constructed and used to measure the positioning error of a long linear stage. The experimental results indicated that the positioning accuracy of the proposed interferometer was ±4.5 μm for a linear stage with a displacement of 250 mm; the results obtained were comparable to those obtained with a commercially available laser interferometer. The proposed heterodyne interferometer can thus be used in other applications related to precision engineering.

Theoretical Research on Flow and Heat Transfer Characteristics of Hydrostatic Oil Film in Flat Microfluidic Boundary Layer

The hydrostatic bearing is the core component of ultra-precision computer numerical control (CNC) machine tools. Because the temperature rise in the oil film of hydrostatic bearings seriously affects the working accuracy of the bearings, it is important to study the flow and heat transfer characteristics of the oil film. Based on the physical model of an incompressible viscous fluid flowing in a flat microfluidic boundary layer, velocity, temperature and heat flux distribution equations of oil film are constructed by theories of heat transfer and hydrodynamics. Then, the effects of several parameters on velocity distribution, temperature distribution and heat flux distribution are analyzed, such as the upper plate velocity, the channel length, and so on. The results show that the dimensionless velocity of the oil film decreases with the increase in the upper plate velocity and the channel length. The oil film temperature distribution can be divided into three zones: the increasing zone, stabilizing zone and decreasing zone. The heat flux decreases linearly with the increase in the plate thickness, and increases linearly with the increase in the temperature difference.

Research on Machining Error Analysis and Traceability Method of Globoidal Indexing Cam Profile

The profile of the globoidal indexing cam is a spatially undevelopable surface. It needs a special computer numerical control (CNC) machine tool to finish batch production, and its machining quality will be affected by the motion error of each part of the machine tool and the clamped positioning error of the workpiece. Firstly, the mathematical model of the error of the machine tool for machining the globoidal cam surface is derived, and the influence of the error of the machine tool for machining the globoidal cam surface is given. Secondly, an error tracking method for globoidal cam profile machining error based on error sensitivity coefficient grouping is proposed, which improves the data processing speed and the accuracy of the tracking results. Finally, the error analysis and traceability method of the globoidal cam is verified by experiments, and the error traceability results are fed back to the processing link. The machining quality of globoidal cam is improved by the error compensation, which provides the key technology for the integration of the design, manufacture, and measurement of the globoidal cam.