Numerical Control Machine Research Articles

Analytical Calculation of Air Gap Magnetic Field of SPMSM with Eccentrically Cut Poles Based on Magnetic Pole Division

In the design process of surface-mounted permanent magnet motor (SPMSM) for industrial robots and computer numerical control (CNC) machine tools, to pursue the sinusoidal nature of the back electromotive force, the magnetic poles in the form of eccentric pole cutting structure are often used. To analyze the no-load air gap magnetic field of the SPMSM with eccentrically cut poles simply and accurately, a subdomain model magnetic field analytical calculation method based on equal-area integral block processing of permanent magnets is proposed. The problem that the traditional subdomain analysis model cannot be directly applied to the SPMSM with eccentrically cut poles of unequal thickness is solved. The proposed method considers the influence of stator slotting and the actual permeability of permanent magnets, and directly obtains the fundamental wave and harmonic components of the no-load air gap flux density by solving the subdomain model. The finite element method (FEM) is used to directly calculate the air gap magnetic field for verification. The results of the analytical method and the no-load air gap magnetic density calculated by the FEM are consistent, which verifies the accuracy of the proposed analytical method and can quickly guide the design of the SPMSM with eccentrically cut poles.

Surface modification improving the biological activity and osteogenic ability of 3D printing porous dental implants

Objective: To explore the mechanical properties, biological activity, and osteogenic ability of 3D printed TC4 titanium (Ti) alloy dental implants treated with surface modification.Methods: Dental implants with 30% porosity were manufactured using selective laser melting (SLM) technology (group 3D), while traditional numerically-controlled machine tools (CNC) were used to manufacture implants without porosity (group SL). The implants were then surface modified through sandblasting and acid etching (groups 3DA1 and SLA1), and then alkali etching (groups 3DA2 and SLA2). The physicochemical properties of the implants were measured using a Vickers hardness instrument, scanning electron microscope (SEM), X-ray diffraction (XRD), energy dispersive spectrometer (EDS), and profilograph before and after surface modification. Next, the biocompatibility, bioactivity, and osteogenic ability of the implants were evaluated using apatite deposition experiments, alkaline phosphatase (ALP) activity, and semiquantitative analysis of extracellular matrix mineralization.Results: There were significant differences in morphology, geometric accuracy, mechanical properties, surface roughness, and hydrophilicity between groups 3D and SL. Furthermore, surface modification improved the physicochemical properties of the porous implants. Implants with sandblasting, acid etching, and alkali etching demonstrated better biocompatibility, bioactivity, and osteogenic ability than implants without surface modification in both groups 3D and SL. Additionally, the implants of groups 3D have higher bioactivity than that of groups SL.Conclusion: Surface modification and the macroporous structure of implants can improve their bioactivity and osteogenic ability, enhancing the application of Ti alloy dental implants.

Development of Pitch Cycle-Based Iterative Learning Contour Control for Thread Milling Operations in CNC Machine Tools

The helical contour motion accuracy of feed drive axes is important for thread milling operations in computer numerical control (CNC) machine tools. However, the motion dynamics and external disturbances significantly affect the contour motion results, while the feed drive axes perform helical motions in thread milling operations. Although existing iterative learning contour control (ILCC) methods can improve contour motion accuracy, the problems of data recording and processing on memory usage and computational burden in control systems, wasted materials, and increased costs in thread manufacturing still limit the practical applications of ILCC. Therefore, considering the similar motion dynamics and external disturbances of the feed drive axes during the pitch cycle motions of a helical path, this study developed a pitch cycle-based iterative learning contour control (PCB-ILCC) method to address the control system and thread manufacturing problems caused by the use of ILCC. For PCB-ILCC, this study adopted contour error vector estimation by referring to the interpolated positions on the pitch cycle of the helical path to simplify the computational complexity and designed the ILCC using the cycle learning method to easily implement the ILCC structure. Thus, this study developed a permanent magnet synchronous motor (PMSM) driving control utilizing the robust control method to mitigate the problems of motion dynamics and external disturbances on the feed drive axes. Thread milling experiments performed on a five-axis CNC machining center demonstrated the feasibility of the PCB-ILCC and validated that it can significantly improve the helical contour motion accuracy of the feed drive axes and achieve an 80% contour error reduction rate in comparison with the proportional–proportional–integral control, which is extensively used in commercialized PMSM drivers and CNC controllers.

Optimal toolpath planning strategy prediction using machine learning technique

Finding the best toolpath planning strategy for Computer Numerical Control (CNC) machining requires a trial-and-error approach. One needs to follow a number of steps: generating the toolpath, performing simulations/machining, measuring various parameters to quantify the quality of the toolpath, and then selecting the best toolpath. This conventional iterative approach is time-consuming and often error-prone, which is a bottleneck in the current CNC machining industry. This paper presents a novel framework to create a machine learning based system for choosing the best toolpath planning strategy for CNC machining (finishing) of complex freeform surfaces directly from the CAD model. Three tool path planning strategies are considered: Adaptive planar, Iso-scallop, and Hybrid. At first, a novel toolpath analysis module is presented to evaluate the quality of the toolpath considering three performance parameters: surface finish, toolpath length, and smoothness. This quality measurement technique is extensively tested for robustness and accuracy. It is then used to analyze and label a large number of CAD models to create a dataset for supervised learning. Finally, a Convolutional Neural Network (CNN) is designed and trained using this dataset to predict the best toolpath planning strategy. Compared to the conventional approaches, the developed system uses multiple performance parameters and chooses the best strategy directly from the CAD model. Results show that the proposed data-driven model achieved 96.8% test accuracy.

Increasing the Performance of Computer Numerical Control Machine via the Dhouib-Matrix-4 Metaheuristic

The Computer Numerical Control (CNC) machine represents a turning point in today's production which has high requirements for product accuracy. The CNC machine enables a high flexibility in work and time saving and also reduces the time required for product accuracy control. Moreover, the CNC machine are used for several activities, most often for turning, drilling and milling operations. Usually, the productivity of any CNC machine can be increased thanks to the minimization of the non-productive of tool movement. In this paper, the results of a new metaheuristic named Dhouib-Matrix-4 (DM4) with an application on the NP-hard problem based on the Travelling Salesman Problem are presented. DM4 is used for increasing the performance of the CNC Machine by optimizing a tool path length in the drilling process performed on the CNC milling machine. The proposed algorithm (DM4) achieves a solution closed to the optimum, compared with the results obtained with the Ant Colony Optimization algorithm and the results found with the manual programming in G code by using a control unit for the selected CNC milling machine.

Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks

Thermal errors are one key impact factor on the processing accuracy of numerical control machine. This study targeted at a certain vertical processing center presents a new algorithm for predictive modeling of thermal errors in numerical control machine. This algorithm is founded on back-propagation neural networks (BPNNs) and adopts beetle antennae search (BAS) to find the best weights and thresholds of BPNNs. It avoids the local minimization due to local extremums faced by traditional BPNNs. The intermingling rate and arithmetic computation efficiency of neural network algorithms are further improved. Then, a BAS-BP thermal error prediction model is built with the machine temperature changes and thermal errors as the input data. Compared with conventional BPNNs, the BPNN after particle swarm optimization suggests the convergence rate of BAS-BP is improved by 85%, the leftover mistakes between the genuine information and the anticipated information are under 1 um, and the overall prediction precision is above 90%. Thus, the new model has high precision, high anti-disturbance ability and strong robustness.

Numerical Control Machining Process Parameters of Nickel-based Alloy Casing

As a major component of engine, aviation casing is usually machined from superalloy blanks because it needs to withstand high temperature, high pressure and other environments during use. Surface integrity indexes such as surface roughness and residual stress will seriously restrict the performance and service life of such parts. In this paper, by studying the processing technology of the casing, Deform-3d software was applied to conduct three-dimensional dynamic modelling for the rough milling process of the casing made of nickel-based alloy GH4169, and the milling simulation analysis was carried out. With milling force and milling temperature as evaluation indexes, the variation rules of force and temperature under different coaxial and radial milling depths were obtained through simulation analysis. At the same time, the single factor optimization was carried out for different coaxial and radial milling depths according to evaluation indexes, and a set of optimal milling parameters were obtained, which provided theoretical guidance for practical machining.

Prediction of thermal errors in a motorized spindle in CNC machine tools by applying loads based on heat flux

The thermal errors of a motorized spindle are seen as a source of significant errors in computer numerical control (CNC) machine tools, and motion errors, geometrical errors, among others, that are consequences of the thermal errors in the spindle. A predictive model of the thermal errors of a motorized spindle is proposed using a loading method based on heat flux. At first, a finite element model for the steady-state and transient thermal-structure coupling analysis of a motorized spindle is established, in which the loading method based on heat flux is adopted to assign the total amount of heat to the ball, an inner race, and an outer race of angular-contact ball bearing proportionally. The response values of sample points are obtained by simulation, and then a response surface model for the prediction of the steady-state elongation of the spindle thermal deformation under the influences of four factors including rotational speed of a bearing, coolant temperature, ambient temperature, and preload is constructed. The simulation and experimental results indicate that predictive error of the steady-state elongation of the spindle thermal deformation is only 3.81 μm, and the thermal equilibrium time in the transient thermal-structure coupling analysis is almost same to the experimental result. The response surface model can predict steady-state errors arising from the changes in working conditions and corresponding changes of the spindle.

Eco-friendly MoS2/waste coconut oil nanofluid for machining of magnesium implants

Abstract The cost of the coolant and its disposal cost are significant issues in metal machining processes. In biocompatible magnesium alloy-based medical implants and instrument manufacturing, the cost hikes are owing to the use of unconventional machining processes and computerised numerical control machines. This research aims to improve machinability performance and optimize process parameters for biocompatible magnesium implant manufacturing for biomedical applications using eco-friendly nanofluid of MoS2 nanoparticles suspended in waste coconut oil. The nanofluid was prepared from the multiple times used waste coconut oil (waste) and was mixed with MoS2 nanoparticles. The orthogonal array L16, Taguchi analysis, and analysis of variance were employed in experimental design and statistical optimization. The machinability performance was determined by measuring and comparing the responses like cutting force, feed force, surface roughness, cutting zone temperature, and tool wear. They were compared with machining using a nanofluid and conventional commercial coolant. The results reveal that the proposed method of machining improved machinability performance appreciably; therefore, the observations of the proposed method were used and the process parameters were optimized. Mathematical models were developed for the prediction of process parameters. The proposed method exhibited the average reduction of the cutting force by 68.23167 N, feed force requirements by 34.180 N, the cutting zone temperature by 60.435°C, the surface roughness by 0.118908 µm, and the tool wear by 039938 mg·h−1.

Study on Thermal Error Modeling for CNC Machine Tools Based on the Improved Radial Basis Function Neural Network

The thermal error modeling technology of computer numerical control (CNC) machine tools is the core of thermal error compensation, and the machining accuracy of CNC machine tools can be improved effectively by the high-precision prediction model of thermal errors. This paper analyzes several methods related to thermal error modeling in the latest research applications, summarizes their deficiencies, and proposes a thermal error modeling method of CNC machine tool based on the improved particle swarm optimization (PSO) algorithm and radial basis function (RBF) neural network, named as IPSO-RBFNN. By introducing a compression factor to make the PSO algorithm balance between global and local search, the structure parameters of RBF neural network are optimized. Furthermore, in order to pick up the temperature-sensitive variables, an improved model, which combines the K-means clustering algorithm and correlation analysis method based on back propagation (BP) neural network is proposed. After the temperature-sensitive variables are selected, the IPSO-RBFNN method is adopted to establish the thermal error model for CNC machine tool. Based on the experimental data of the CNC machine tool under the name of DMG-DMU65, the predictive accuracy of the IPSO-RBFNN model in Z direction reaches 2.05 μm. Compared with other neural network method, it is improved by 10.48%, which indicates that it has better prediction ability. At last, the experiment verification for different thermal error terms at different velocities proves that this model has stronger robustness.

Comparison between Eight-Axis Articulated Robot and Five-Axis CNC Gantry Laser Metal Deposition Machines for Fabricating Large Components

Laser metal deposition (LMD) is an additive manufacturing (AM) process capable of producing large components for the aerospace and oil and gas industries. This is achieved by mounting the deposition head on a motion system, such as an articulated robot or a gantry computer numerical control (CNC) machine, which can scan large volumes. Articulated robots are more flexible and less expensive than CNC machines, which on the other hand, are more accurate. This study compares two LMD systems with different motion architectures (i.e., an eight-axis articulated robot and a five-axis CNC gantry machine) in producing a large gas turbine axisymmetric component. The same process parameters were applied to both machines. The deposited components show no significant differences in geometry, indicating that the different performances in terms of accuracy of the two machines do not influence the outcome. The findings indicate that LMD can consistently produce large-scale axisymmetric metal components with both types of equipment. For such an application, the user has the option of using an articulated robot when flexibility and cost are essential, such as in a research context, or a CNC machine where ease of programming and process standardization are important elements, such as in an industrial environment.

An adaptive sampling method for STL free-form surfaces based on the quasi-Gauss curvature grid

ABSTRACT The contact on-machine measurement can be directly used to detect and analyze the computerized numerical control machining quality of parts. To solve the problems of unreasonable distribution of sampling points and omission of sampling area in traditional sampling methods, an adaptive sampling method of the stereolithography (STL) free-form surfaces based on the quasi-Gauss curvature grid is proposed. First, on the quasi-conformal rectangular mapping region of the triangular mesh surface, a globally optimal centroidal Voronoi tessellation is generated with discrete Gauss curvature as the region density. Then, based on the quasi-Gauss curvature grid generated by subdividing the centroid of the centroidal Voronoi tessellation through a quadtree method, several types of feature points are reasonably taken as measuring points to realize the adaptive sampling. Finally, the experiments show that, for the on-machine measurement of STL free-form surfaces, the quasi-Gauss curvature grid sampling strategy can make the distribution of measuring points more reasonable and practical.

Improving the designing of mechanical technologies through optimized dimensional structures

Modern trends in technological development related to the use of numerical control machine tools are manifested by minimizing the number of technological operations, installations, operation elements. One of the most effective ways to improve the design of mechanical processing technologies is to create optimal technological dimensional structures. In the paper, optimality requirements are formulated in relation to the elementary dimensional structures and the entire structure. It is shown that a technological dimensional structure is optimal if it is similar to the constructive dimensional structure. Real constructive dimensional structures are very complex and varied, so that the optimality of some sequences of the structure excludes ensuring the optimality of other sequences. Reciprocal structural adaptations are required, which can be done by constructively resizing the equivalent part. As a criterion for the resizing, the non-admission of the increase in the tolerance of the machining allowance is manifested.

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters.

A signal-filtering and feature-enhancement method based on ensemble local mean decomposition and adaptive morphological filtering

The bearing fault signals from the spindle motors of computer numerical control machines are complex and non-linear due to being coupled to multiple subsystems. The complexity of industrial signals, with increased industrial noise, and the difference in fault features in different life cycles and different individual signals bring great challenges for fault feature extraction. In this paper, a signal-filtering and feature-enhancement method based on an ensemble local mean decomposition and adaptive morphological filtering (ELMD-AMF) method is proposed. First, the original vibration signal of the bearing is reconstructed by ELMD to reducing interference from background noise. Next, an improved feature-enhancement process based on AMF is constructed, a particle swarm optimization with maximum-weighted spectral kurtosis as an optimization objective is used to adaptively construct the size of the structural element, and a morphology hat product operator one is adapted to extract the periodic impulse features. Finally, the effectiveness of the method is proved by using an actual three-phase induction motor matched with an NTN ceramic bearing and a FAG metal bearing, respectively. Further, compared with minimum entropy deconvolution and fast kurtogram methods, the result proves that the proposed method has better performance for both early-failure and late-failure scenarios under real-world engineering conditions.

Research Progress of Hydrostatic Bearing and Hydrostatic-Hydrodynamic Hybrid Bearing in High-End Computer Numerical Control Machine Equipment

Research Progress of Hydrostatic Bearing and Hydrostatic-Hydrodynamic Hybrid Bearing in High-End Computer Numerical Control Machine Equipment

A Novel Architecture of a Six Degrees of Freedom Parallel Platform

With the rapid development of the manufacturing industry, industrial automation equipment represented by computer numerical control (CNC) machine tools has put forward higher and higher requirements for the machining accuracy of parts. Compared with the multi-axis serial platform solution, the parallel platform solution is theoretically more suitable for high-precision machining equipment. There are many parallel platform solutions, but not one can provide a common physical platform to test the effectiveness of a variety of control algorithms. To achieve the goals, this paper is based on the Stewart six degrees of freedom parallel platform, and it mainly studies the platform construction. This study completed the mechanical structure design of the parallel platform. Based on the microprogrammed control unit (MCU) + pre-driver chip + three-phase full bridge solution, we have completed the circuit design of the motor driver. We wrote the program of MCU to drive six parallel robotic arms as well as the program of the parallel platform control center on the PC, and we completed the system joint debugging. The closed-loop control effect of the parallel platform workspace pose is realized.

Indoor Particulate Matter Transfer in CNC Machining Workshop and The Influence of Ventilation Strategies—A Case Study

Particulate matter in Computer Numerical Control (CNC) machining workshop is harmful to workers’ health. This paper studies particulate matter transfer and the performance of various ventilation strategies in a CNC machining workshop. To obtain the boundary condition of the particle field, instruments were installed to obtain the particle size attenuation characteristics and source strength, respectively. The results show that the 99% cumulative mass concentration of particles is distributed within 1.5 μm, and the release rate of particles from the full enclosure. Next, the indoor flow field and particle field were simulated by numerical simulation with the measured boundary conditions. The working area’s age of air, particle concentration, and ventilation efficiency were compared between four displacement ventilation methods and one mixed ventilation method. The results show that the working area’s mean particle concentration and ventilation efficiency under longitudinal displacement ventilation is better than other methods. At the same time, the mean age of air is slightly worse. In addition, mixed ventilation can obtain lower mean age of air, but the particle concentration is higher in the working area. The bilateral longitudinal ventilation can be improved by placing axial circulation fans with vertical upward outlets in the center of the workshop.

Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using regression analysis and fuzzy inference.

During the machining process, the computer numerical control machine is susceptible to variations in ambient temperature, cutting heat, and friction within the transmission parts, which generate different heat sources. These heat sources affect the machine structure in different ways, causing deformation of the machine and displacement of the tooltip and workpiece position, ultimately resulting in deviations in machining accuracy. The amount of thermal drift depends on several factors, including the material of the machine components, the cutting conditions, the duration of the machining process, and the environment. This study proposes a hybrid optimization algorithm to optimize the thermal variables of computer numerical control machine tool spindles. The proposed approach combines regression analysis and fuzzy inference to model the thermal behavior of the spindle. Spindle speed and 16 temperature measurement points distributed on the machine are input factors, while the spindle's axial thermal error is considered an output factor. This study develops a regression equation for each speed to account for the different temperature rise slopes and spindle thermal variations at different speeds. The experimental results show that the hybrid thermal displacement compensation framework proposed in this study effectively reduces the thermal displacement error caused by spindle temperature variation. Furthermore, the study finds that the model can be adapted to significant variations in environmental conditions by limiting the machining speed range, which significantly reduces the amount of data needed for model adaptation and shortens the adaptation time of the thermal displacement compensation model. As a result, this framework can indirectly improve product yield. The effects observed in this study are remarkable.

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

In this study, an approach for artificial neural network (ANN) was presented to predict and control arithmetical mean surface roughness value (Ra), machining properties of wood materials densified by compressing in a computer numerical control (CNC) machine. Black poplar (Populus nigra L.) tree species were used as the experimental material. After specimens were densified by Thermo-Mechanical (TM) method at 0%, 20%, and 40% ratios, machining process of specimens were performed at 1000, 1500, and 2000 mm/min feed speeds and in 12000, 15000, 18000 rpm rotation speed on a CNC vertical wood machining center by using two different cutters. Data used for the training and testing of an ANN. Cutter type, compression ratio, feed rate, and spindle speed were selected as Four parameters. While hidden layer of the Ra model has ten neurons, one hidden layer was used, Compression ratio is the most significant parameter, followed by feed speed for Ra values. surface roughness increases with increased feed rate. Ra values in training, validation, and testing the data set for Ra were 0.97122, 0.8538, and 0.76685, respectively. The Mean Square Error (MSE) value was determined as 0.0019914 test of the network. The proposed ANN model came to agreement with the measured values in predicting surface roughness Ra values of MAPE. The MAPE value was calculated as 6.61, which can be considered a very good prediction (MAPE< 10 % = very good prediction). The study showed that obtained ANN prediction model is a practical and efficient tool to model the Ra of wood. For reducing energy, time and cost in the wood industry (densification and CNC wood machining), current research results can be implemented.