Computer Numerical Control Machine Tools Research Articles

Virtual instrumentation-based data collection and analysis for CNC machining process

Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

Geometric Error Identification of Gantry-Type CNC Machine Tool Based on Multi-Station Synchronization Laser Tracers

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.

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

Adaptation of Symbolic Discrete Control Synthesis for Energy-Efficient Multi-Pocket Milling

In engineering, cost minimization, especially in Computer Numerical Control (CNC) machining like pocket milling, is crucial. Existing tool path definition software often lacks optimization, particularly at critical starting and ending points. This study optimizes CNC machine tool paths for energy-efficient multi-pocket milling, utilizing the Symbolic Discrete Control Synthesis (SDCS) method for formal correctness. In our work, the tool path generation is formulated as a traveling salesman problem. We introduce a modeling framework to adapt SDCS to multi-pocket-milling processes, aiming to enhance precision and efficiency for potential cost savings, including energy and time, in engineering applications. This study reports experimental and comparative results, where comparative evaluations were made using metaheuristic algorithms. Our proposed approach improves CNC machining processes for multi-pocket milling. We experimentally evaluate our control algorithms and demonstrate and validate our approach through case studies.

Energy-saving strategy and method of spindle deceleration during no-load operation of machine tools for energy lean management

Computer Numerical Control (CNC) machine tools undergo periods of no-load motion, such as tool feeding, tool withdrawing, and tool changing, during workpiece processing. These intervals result in energy wastage as the machine tools remain idle without performing cutting operations. To address this issue, we propose a novel energy-saving strategy and method involving spindle deceleration during the no-load operation of machine tools for energy lean management. Specifically, we conducted three tasks: (1) analyzed the energy consumption characteristics of the CNC machine tool during no-load operation; (2) established a theoretical decision-making model for the energy-saving method of spindle deceleration; (3) discussed the energy-saving critical time for energy savings and the energy-saving effect of the deceleration strategy for the machine tool spindle through three machining cases. Furthermore, we conducted a case study using the CK6153i lathe to verify the effectiveness and feasibility of the proposed method. The results demonstrated significant energy savings of 1826.65 J, 2948.85 J, and 7650.05 J, along with reductions in energy waste of 14.99 %, 20.43 %, and 53.01 %, respectively, in the three machining cases through the implementation of the spindle deceleration strategy. We believe that the outcomes of this research can better assist engineers in the energy lean management of machine tools.

An optimization method of acceleration and deceleration time of feed system based on load inertia

The acceleration and deceleration time is usually a constant value in the process of computer numerical control (CNC) machine tool processing, which cannot adapt to the change of external load and greatly affects processing efficiency. This paper proposes an optimization method for the acceleration and deceleration time of the feed system based on load inertia, which provides the basis for the adaptive adjustment of the acceleration and deceleration time of the feed system. Firstly, by establishing the dynamic model of the servo system, the acceleration and deceleration method is used to identify the external load inertia under different working conditions. The prediction model of the current variance based on load inertia and acceleration and deceleration time parameters is established by using the response surface method, and then the multi-objective particle swarm optimization algorithm is used to build the acceleration and deceleration time optimization model based on the load inertia. At the same time, the inertia identification part is compared with the model reference adaptive system method and the empirical formula estimation method based on current and velocity, the simulation and cutting experiment results show that the inertia identification method based on acceleration and deceleration optimizes the other method. Finally, the machining experiments are carried out on three-axis and five-axis machine tools with the same machine tool type, and by adding different counterweight blocks to change the external load. The test proves that the acceleration and deceleration adaptive adjustment strategy based on load inertia can effectively improve processing efficiency and reduce the fluctuation of the processing load.

Metrological characterisation of tool pre-setting optical systems based on 2D imaging

Accurate tool geometry is vital in modern computer numerical control machine tools for ensuring the accuracy of the manufactured parts in machining processes. The tool pre-setting optical systems play an important role by establishing accurate, efficient, and repeatable tool data for cutting tools varying from macro to micro size tools. The tool data is later utilised by the numerical control programs to accomplish precision machining operations with the required accuracy. However, the traceability of tool pre-setting optical systems is not guaranteed due to the lack of specific international standards, and there is a strong need of establishing a traceable measurement process which requires calibrated reference artefacts. In this work, we have established the metrological characteristics of an image-based tool pre-setting optical system based on ISO 15530 part 3. For this purpose, an artefact with sharp cutting edges was manufactured from a calibrated cylindrical gauge pin, measured on a coordinate measuring machine by two different approaches, and the impact of the edge radius is incorporated in the measurement process. The artefact was later employed in the performance verification of the optical system for on-machine tool characterization analogous to the production workshop environment, and the measured outcome of the optical sensor was evaluated with the developed metrological approaches. Experiments have shown the applicability of the measurement procedures and the relevance of the proposed reference artefact for the characterisation of image-based tool pre-setting optical systems.

Development and design of intelligent lubricating equipment for feed systems

Recently, the lubricating devices for the feed systems of computer numerical control (CNC) machine tools supply oil periodically because programmable logic controller and macro are adopted for designing oil supply for the present CNC machine tools, causing lack or overdose lubrication. This study attempts to discuss and design the optimum lubrication cycle timing for the feed system under the experimental conditions of different loads and feed rates. The parameters of the servomotor are monitored by utilizing an intelligent module, the changes in the current and torque of the servomotor are obtained, and then a lubrication module is created with the Stribeck friction model, a modeling that utilizes response surface methodology. The increase in feed rate will cause increasing current and torque, and the current and torque for the load 35 kg were 11 and 13%, respectively. Finally, different loads and feed rates are imported to determine optimal lubrication timing. According to a long-term validation, the oil supply frequency is improved by over 80% without influencing the feed system accuracy, and the traditional lubrication module is compared with the lubrication module. Long time test is 8 h, and the rate of improvement is 87.5%, the cost of lubricating oil is reduced, and green manufacturing is met. An appropriate oil supply system is provided for the circle.

Computer numerical control machine tool wear monitoring through a data-driven approach

The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methods.

Smoothing interpolation of five-axis tool path with less feedrate fluctuation and higher computation efficiency

Five-axis machine tools are widely used in aerospace, die and mold industries. Most of the time, the tool path is described by connecting a series of short linear segments. The tangency discontinuity of the linear segments leads to the discontinuity of the kinematic profiles, hence generating discontinuous interpolation points, and resulting in fluctuation of machine motion, which reduces the machining efficiency and causes excessive tracking and contouring errors. This paper aims to generate smooth interpolation points at servo sample intervals with the proposed G2 continuous five-axis corner rounding algorithm suitable for reducing feedrate fluctuation along the smoothed toolpath and improving machining efficiency, and the modified jerk-limited feedrate planning algorithm suitable for eliminating feedrate fluctuation within the feedrate planning units and improving interpolation computation efficiency. In this paper, the five-axis linear segments are first smoothed by curvature-optimized cubic Bezier splines, which reduces the number of the feedrate planning units along the smoothed toolpath and improves machining efficiency. Then, the jerk-limited feedrate planning algorithm is modified to ensure that the time interval of each different scheduled feedrate phase is an integer multiple of the axis position control loop closure time (i.e. servo sample interval), which eliminates the feedrate fluctuation within each feedrate planning unit and reduces the interpolation computation load of position command in each interpolation period (i.e. servo sample interval). At last, the effectiveness of the proposed algorithm is demonstrated with simulations and experiments following a five-axis, curved tool path on a CNC (Computer Numerical Control) machine tool.

A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM

Given motorized spindles’ extensive periods of prolonged high-velocity operation, they are prone to temperature changes, which leads to the problem of thermal error, leading to diminished precision in machining operations. To address the thermal error issue in motorized spindles of computer numerical control (CNC) machine tools, this study proposes a pelican optimization algorithm (POA)-optimized convolutional neural network (CNN)–long short-term memory (LSTM) hybrid neural network model (POA-CNN-LSTMNN). Initially, the identification of temperature-sensitive locations in the spindle system is performed using a combination of hierarchical clustering, the K-medoids algorithm, and Pearson’s coefficient calculation. Subsequently, the temperature data from these identified points, along with real-time collected spindle thermal error data, are employed to construct the model. The Pelican optimization algorithm is used to enhance the model parameters to achieve the best performance. Finally, the proposed model is subjected to a comparative analysis with other thermal error prediction models. Drawing from the experimental findings, it is evident that the POA-CNN-LSTMNN model exhibits superior prediction performance.

The Basic Concept and Application Status of Laser Induced Forward Transfer

Laser Induced Forward Transfer (LIFT) is a new type of transfer method. It uses high-energy laser as an energy source to irradiate solid or liquid materials, allowing them to be emitted onto the receiving substrate under the force of steam to achieve the transfer of material. Nowadays, although most of the work on LIFT is still in the laboratory stage, compared to the traditional process of LIFT, its nozzle free characteristics, pixel-based transfer method, make it have high production efficiency, universality of materials, and compatibility with Computer numerical control (CNC) machine tools. In this review, the fundamental principle of LIFT is introduced and the great potential of LIFT in many fields is presented and analyzed, including Three-dimensional (3D) bioprinting, industrial manufacturing, energy storage material manufacturing, new-type energy and so on. The conclusion is that if the application of LIFT in the manufacturing field can be achieved, the efficiency and flexibility of modern digital production will be greatly improved. The LIFT method has great development prospects in the manufacturing field.

Spindle Thermal Error Prediction Based on Back Propagation Neural Network and Hybrid Taguchi Genetic Algorithm for a Computer Numerical Control Machine Tool

Spindle Thermal Error Prediction Based on Back Propagation Neural Network and Hybrid Taguchi Genetic Algorithm for a Computer Numerical Control Machine Tool

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Abstract The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control.

In order to reduce the effect of nonlinear friction and time-varying factors on the servo system of a computer numerical control (CNC) machine tool and improve its motion control accuracy, this paper uses an adaptive sliding mode control (ASMC) method based on model reference adaptive control (MRAC). The method adopts ASMC in the control outer loop and obtains the optimal control parameters by making the sliding mode control (SMC) law continuous and adaptively estimating the control parameters. At the same time, MRAC is used in the control inner loop to enhance the "invariance" of the controlled object so that the switching gain of SMC can satisfy the disturbance matching condition even under lesser conditions. Simulation and experimental results show that compared with the traditional SMC, the ASMC based on MRAC proposed in this paper effectively reduces the influence of nonlinear friction on the system performance, and the reduction in following error reaches 71.2%, which significantly improves the motion control accuracy of the control system. The spectral analysis of the following errors shows that the maximum magnitude reduction rate of the high-frequency chattering is 89.02%, which significantly reduces the effect of the high-frequency chattering and effectively improves the stability performance of the control system.

Data Acquisition and Performance Analysis of Image-Based Photonic Encoder Using Field-Programmable Gate Array (FPGA)

With the continuous advancement of numerical control technology, the requirements for the position detection resolution, precision, and size of photoelectric encoders in computer numerical control machine tools are increasingly stringent. In the pursuit of high resolution and precision, this work investigates the principles of electronic subdivision and embedded hardware. It designs a high-precision image-based photonic encoder using a Field-Programmable Gate Array (FPGA). This photonic encoder captures the pattern of a rotating code disk using a complementary metal-oxide-semiconductor (CMOS) image sensor. The encoder’s core is the XC6SLX25T chip from the Spartan-6 series, with peripheral circuits including only A/D sampling and low-pass signal processing circuits. The FPGA module handles the digital signal reception, waveform conversion, quadrature frequency coarse count calculation, fine count subdivision calculation, and final position calculation of the encoder. In experiments, the output signal of the photonic encoder contains many impurities. After processing by the signal processing module, the A and B phase signals are not affected by previous interference, with a phase difference of 90°, meeting the requirements for subsequent signal processing modules. After fine count subdivision processing, the waveform graph significantly increases within one cycle, and after quadrupling the frequency, 30 subdivisions are performed within each cycle. Noise is introduced into graphic positioning or graphics are positioned under different noise conditions. Experimental results show that utilizing an improved centroid algorithm helps further suppress noise and enhance measurement accuracy in the design of image-based photonic encoders.

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.

Comprehensive extraction technology of key quality characteristics of meta-action unit for CNC machine tools design

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

Reliability evaluation of a complex system that considers the influence of the fluctuation degree of the performance degradation trajectory

AbstractIn this paper, a reliability evaluation method based on the hidden Markov model (HMM) considers the influence of degradation trajectory fluctuation. To address the problem in which the performance degradation trajectory modelling of complex systems is limited by sampling time points, this method constructs an approximate degradation trajectory model based on effective discrete data points through a radial basis function to realize the degradation data expansion of small samples. Then, the average degradation rate is defined, and the fluctuation degree of the degradation trajectory is quantitatively evaluated to obtain the evaluation function of the fluctuation degree of the degradation trajectory. In addition, to determine the relationship between the fluctuation degree of the degradation trajectory and the operating state of the system, the method combines HMM with the evaluation function of the fluctuation degree of the degradation trajectory and obtains the transition probability matrix of different operating states of the system affected by the fluctuation degree of the degradation trajectory. On the basis of the probability matrix of the failure state, the reliability change curve of the system is derived. Finally, the effectiveness of the proposed method is verified via an example of a computer numerical control machine tool.