High-speed CNC Machine Research Articles

Optimization of high speed bone drilling with automated osteonecrosis avoidance using an ANN-GA hybrid method

ABSTRACT Efficient high-speed bone drilling with an automated osteonecrosis avoidance strategy is a very fascinating task to the orthopaedic surgeons. Several drilling parameters such as drill diameter, speed, and feed rate can affect the performance of drilling in terms of generated temperature and thrust force. This research is conducted using a high-speed mini CNC machine to investigate bone drilling parameters’ impact on the outputs. Here, a piece of swine bone has been used for testing material and an automated cooling system based on proportional controller (P) was developed to prevent osteonecrosis. Further, a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model has been proposed based on the different experimental findings. ANN predicts output variables, and GA determines the optimal input drilling parameters by minimizing the ANN model of temperature and force. The simulation results revealed that drill diameter of 1.838 mm, speed of 17,880 rpm, and feed rate of 9.973 mm/min minimized the output temperature generated on swine bone. Similarly, diameter of 1.5 mm, speed of 16,582 rpm, and 10 mm/min feed rate helped to minimize the generated force during drilling. The proposed model has been validated by conducting new experiments based on these obtained optimal parameters.

Recent Patents of Thermal Error Modeling and Thermal Error Prediction of CNC Machine Tool Spindle

: With the development of machine tools to high precision and high automation, thermal error has become the key reason to hinder the improvement of thermal stability of machine tools. The spindle electrothermal error is an important factor affecting the machining accuracy of highspeed CNC machine tools. The thermal error model of a high-speed motorized spindle is established to compensate the thermal error, which can effectively improve the influence of thermal error on the machining accuracy of machine tools. The thermal displacement during the operation of motorized spindle is the main reason that affects the machining stability and precision of motorized spindle. The thermal error compensation of CNC machine tool spindle can improve the machining efficiency, the machining quality of parts and the service life of CNC machine tools. This paper summarizes the representative patents and papers on thermal error modeling and thermal error prediction in China. By summarizing a large number of patents and papers on modeling, prediction and compensation of thermal error of spindle of CNC machine tools, the modeling method is analyzed and the development trend is discussed. The improvement of thermal error modeling method of CNC machine tool spindle has an important influence on improving machining accuracy, efficiency, and stability. Therefore, in the future, there will be more and more patents for thermal error modeling of CNC machine tools and spindles.

Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model

Milling tool wear, a ubiquitous challenge in industrial automation and manufacturing, leads to diminished equipment utilization, escalating costs, and a decline in product quality. The prediction of tool wear is a complex and challenging task, as it involves numerous variables. This paper introduces a pioneering hybrid approach, the NCA-SMA-GRU model. It involves the hybridization of three major components and is designed to enhance the precision and expedite the process of tool wear prediction. The NCA component is adept at filtering and retaining the most relevant features associated with milling tool wear from the raw signals, and it also improves the model’s interpretability. Subsequently, SMA optimizes the GRU network’s hyperparameters, including the initial learning rate, hidden layer neurons, network training iterations, and the L2 regularization factor, to identify an optimal combination that bolsters predictive performance. The modeling steps and the development of fitness function are explained in detail. The model’s efficacy is rigorously evaluated using data from the 2010 High Speed CNC Machine Tool Health Prediction Contest (PHM 2010), which encompasses wear data from both single and multiple cutters. The performance is compared using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared (R2), and computational time. To assess the ability and characteristics of the proposed approach, other popular hybrid models are constructed for comparative analysis. The results demonstrate that the proposed model addresses the limitations of traditional prediction methods and provides insights into the development of deep learning and optimization algorithms for tool wear prediction.

Structure Optimization Design of New Five-Axis High Speed CNC Machine Column

Structure Optimization Design of New Five-Axis High Speed CNC Machine Column

Design and optimization of high-precision servo control system in precision positioning technology

Abstract High-precision control technology, as a general technology in the field of manufacturing and assembly, is widely used in high-end manufacturing equipment such as chip packaging equipment and high-speed CNC machining centers. In this thesis, a permanent magnet synchronous motor is used as the actuator motor of a high-precision servo control system, and vector control strategy and sliding mode observer are adopted to study and realize the servo control of permanent magnet synchronous motor based on sliding mode observer. The current, speed, and position simulation experiments of the high-precision servo control method are also carried out to compare the positioning accuracy before and after the use of the servo control method and to study the performance of the method in precision positioning. The experiments prove the correctness and effectiveness of the SMO-based PMSM high-precision servo control method and the current response speed of the servo control in this paper is fast, and the speed and position estimation errors are maintained within 1.6 r/min and 0.15 rad in steady state condition, and within 20 r/min and 20 rad in the sudden-change condition, which is able to contribute to 11.91% and 19.30% of the machine’s X-axis and Y-axis positioning accuracy improvement. The SMO-based PMSM high-precision servo control method has high steady-state accuracy and immunity to disturbances and can be used for the precision positioning of machine systems.

A comprehensive review on metallic implant biomaterials and their subtractive manufacturing.

There is a tremendous increase in the demand for converting biomaterials into high-quality industrially manufactured human body parts, also known as medical implants. Drug delivery systems, bone plates, screws, cranial, and dental devices are the popular examples of these implants - the potential alternatives for human life survival. However, the processing techniques of an engineered implant largely determine its preciseness, surface characteristics, and interactive ability with the adjacent tissue(s) in a particular biological environment. Moreover, the high cost-effective manufacturing of an implant under tight tolerances remains a challenge. In this regard, several subtractive or additive manufacturing techniques are employed to manufacture patient-specific implants, depending primarily on the required biocompatibility, bioactivity, surface integrity, and fatigue strength. The present paper reviews numerous non-degradable and degradable metallic implant biomaterials such as stainless steel (SS), titanium (Ti)-based, cobalt (Co)-based, nickel-titanium (NiTi), and magnesium (Mg)-based alloys, followed by their processing via traditional turning, drilling, and milling including the high-speed multi-axis CNC machining, and non-traditional abrasive water jet machining (AWJM), laser beam machining (LBM), ultrasonic machining (USM), and electric discharge machining (EDM) types of subtractive manufacturing techniques. However, the review further funnels down its primary focus on Mg, NiTi, and Ti-based alloys on the basis of the increasing trend of their implant applications in the last decade due to some of their outstanding properties. In the recent years, the incorporation of cryogenic coolant-assisted traditional subtraction of biomaterials has gained researchers’ attention due to its sustainability, environment-friendly nature, performance, and superior biocompatible and functional outcomes fitting for medical applications. However, some of the latest studies reported that the medical implant manufacturing requirements could be more remarkably met using the non-traditional subtractive manufacturing approaches. Altogether, cryogenic machining among the traditional routes and EDM among the non-traditional means along with their variants, were identified as some of the most effective subtractive manufacturing techniques for achieving the dimensionally accurate and biocompatible metallic medical implants with significantly modified surfaces.

Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling

Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

An integrated curvature surface inspection and prediction system for 5-axis synchronization machining

There is an urgent demand for free-form products in industry at the present time because of their superior appearance and the wide variety of functions they perform. Five-axis high-speed CNC machining technology has developed to satisfy this demand, but further improvement in surface quality metric inspection technology is the big challenge it now faces. In this study, the effects of jerk on the performance of five-axis synchronous high-speed CNC ball nose end mills on a freeform turbine mold were investigated. The relationships of characteristics of the images of 14 jerk-cluster finished workpieces with different jerk setting values were established, allowing surface texture features to be analyzed and surface roughness predicted. In addition, machine learning methods were integrated with the surface feature analysis to construct a virtual machining module that acts as a performance prediction system, merging the virtual machine tool functions, surface texture processor, and AI roughness prediction processor. Using the geometric information of the workpiece, cutting parameters and machine tool parameters as inputs, product performance metrics combining surface roughness and machining time can be predicted as outputs of the system. The integrated system provides users with a way to evaluate manufacturing performance before performing actual operations and to reduce test time for cutting parameter development. The model is suitable for complex surface finishes as well as for the production of small batches with high parametric variance. In addition, the partial set of image processing and roughness prediction modules can be used alone as an effective intelligent surface quality inspection system.

A Sequence-to-Sequence Model With Attention and Monotonicity Loss for Tool Wear Monitoring and Prediction

Recently, deep learning has been successfully applied in tool wear monitoring system. However, since the tool wear accumulates in the cutting process, the state of the cutting tool shows a degradation trend, which has not been fully exploited by the current deep learning models. In this paper, an end-to-end deep learning model, named Sequence-to-Sequence Model with Attention and Monotonicity Loss (SMAML) is proposed to simultaneously monitor and predict the tool wear. In the proposed SMAML, the encoder is utilized to extract features from signals collected by different sensors using the group convolution, while the decoder is designed to produce sequential outputs including the results of tool wear monitoring and multi-step-ahead prediction. Besides, a monotonicity loss function is proposed to capture the degeneration characters in these sequential outputs. The experiments are conducted on real-world datasets, which were collected from a high-speed CNC machine. The experimental results demonstrate the effectiveness of the proposed model which outperforms other conventional machine learning and deep learning models.

Thermal Error Model of Linear Motor Feed System Based on Bayesian Neural Network

The linear motor feed system has been in service in complex working conditions for a long time, thus causing the nonuniform distribution of the temperature field distribution. Thus, the thermal error has become a key factor affecting system motion accuracy. In order to maximize the accuracy and efficiency of thermal error compensation for linear motor feed systems, an improved modeling method for the thermal error of the linear motor feed system based on Bayesian neural networks is proposed in combination with the strong generalization performance and avoidance of overfitting of Bayesian neural networks. And the specific modeling ideas are as follows: Firstly, the X-Y cross-type two-axis linear motor feed system is taken as the test object. Due to the traditional neural network’s slow convergence, overfitting, and underfitting problems, the Bayesian neural network is used to model the thermal error of the linear motor feed system. Secondly, to avoid the influence of multicollinearity data on the final results, the grey relation analysis method is used to screen the temperature measuring points. The data with a large relation degree is selected for modeling to ensure the prediction accuracy of the neural network. Thirdly, the temperature variables of sensitive points and thermal positioning errors are taken as data input samples. Fourthly, a Bayesian neural network model is established. Fifthly, the hyperparameters of the Bayesian neural network are determined by a calculating method of Hessian matrix by Gauss-Newton approximation. And finally, a thermal error prediction model is established. The comparison and analysis with the neural network constructed by the ordinary Levenberg-Marquardt algorithm after a series of experimental demonstrations see that the prediction accuracy of the proposed method can be enhanced by up to 10%. It also shows that the prediction model has the advantages of high precision, strong generalization ability, anti-disturbance solid ability, and strong robustness, etc. Therefore, the prediction model is expected to be widely used in predicting and compensating thermal error of the feed system of high-speed CNC machine tools in practical machining occasions.

Dynamic Analysis of High Speed Spindle

Assessment of the thermo-mechanical behaviour of machine-tool spindles is a requisite for the reliable operation of high-speed CNC machine tools. The thermal behaviour and applied loads affect the performance of these high-speed spindles. The primary source of heat generation in the spindle is due to friction torque in angular contact ball bearings, cutting forces, centrifugal force, gyratory effects and bearing preload. The spindle shaft is treated as beam element with one translational and one rotational degree-of-freedom at each node. Angular contact ball bearings are modelled to predict heat generation due to load, viscous, and spin-related effects. Mutual interlinkage between the thermal and structural behaviour of both spindle shaft and bearings is modelled using the effects caused due to the thermal expansion and rate of heat generation. The bearing preload is pushed up due to centrifugal force and temperature rise due to high spindle speed. Components matrices are assembled to configure a finite element model for the thermo-mechanical analysis of spindle-bearing systems using Euler-Bernoulli beam theory. Further this work presents ANSYS simulations on CNC spindle system with angular contact ball bearings using 103 Cr1steel material and hybrid type to predict thermal stress. Static and dynamic analysis is carried out on the spindle with and without thermal load and cutting load to predict the displacement and slope.

Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

Recent Patents on the Structure of High-Speed Motorized Spindle

Background: As one of the core components of high-speed CNC machine tool, high-speed motorized spindle is the core functional component of high precision CNC machine tool, which has become the key research and development object of the world. Objective: By comparing and discussing the patents of high-speed motorized spindle, some valuable conclusions have been drawn to predict the future research and development of high-speed motorized spindle. Methods: By analyzing the characteristics of high-speed motorized spindle structure, the influence of high-speed motorized spindle on high-speed machining technology was explicated. Combining with the key technology of high-speed motorized spindle, the patents related to high-speed motorized spindle structure were used for investigation. Results: With the rapid development of high-speed cutting and numerical control technology and the need of practical application, the requirement for high-speed spindle performance has increased. Motorized spindle technology has the characteristics of high speed, high strength, high power, high torque and low speed, high precision, high reliability and long life, offering diversified bearing and lubrication cooling methods and serving as an intelligent system. Conclusion: The different levels of improvement and renovation of the structure with high-speed motorized spindle, by adding lubrication and cooling device to the spindle have improve the performance of spindle, addressing the loopholes in the technology and making it more practical.

Tool Wear Predicting Based on Multisensory Raw Signals Fusion by Reshaped Time Series Convolutional Neural Network in Manufacturing

Tool wear monitoring is a typical multi-sensor information fusion task. The handcrafted features may be a suboptimal choice that will lower the monitoring accuracy and require significant computational costs that hinder the real-time applications. In order to solve these problems, this paper proposed a new multisensory data-driven tool wear predicting method based on reshaped time series convolutional neural network (RTSCNN). In this method, the reshaped time series layer is introduced to represent the multisensory raw signals, the alternately convolutional and pooling layers is employed to adaptively learn distinctive characteristics of tool wear directly from multisensory raw signals while the multi-layer perceptron with regression layer performs automatic tool wear prediction. In addition, three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under milling operations are used to experimentally demonstrate the performance of the proposed RTSCNN-based multisensory data-driven tool wear predicting method. The experimental results show that the prediction error of the RTSCNN-based data-driven method is observably lower than other state-of-art methods.

Error constraint optimization for corner smoothing algorithms in high-speed CNC machine tools

As corner smoothing algorithm is crucial to high-speed CNC machining, a new error constraint optimization method for corner smoothing algorithm is proposed in this paper. Traditional corner smoothing algorithms are all based on a toolpath to generate motion trajectory, and the same allowable error is used for all smoothed corners. However, in using this method, the contour error of workpiece will be invalidated because of the cutting tool radius in real cutting processing, thereby resulting in over-cutting or inefficiency. In this study, the tool offset and corner smoothing algorithm are combined in order to optimize the error constraint. Besides, a cutting process geometry model was established to analyze the error relationship. On the basis of this model, the smoothing error constraints of each corner can be solved according to the allowable contour error of the workpiece. Finally, motion trajectory is planned through a multi-constraint planning strategy, which can balance the contour error in workpiece at each corner and can guarantee the cutting accuracy, compared with the traditional corner smoothing algorithm. Moreover, a test workpiece is designed to verify the correctness of the proposed algorithm through simulation and actual experiments. The experimental outcome proves that the proposed method improves the workpiece cutting accuracy in high-speed CNC machine tool.

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

Evaluation of a micro-channel reactor for steam reforming of ethylene glycol: A comparative study of catalytic activity of Pt or/and Ni supported γ-alumina catalysts

Steam reforming of ethylene glycol (EG) was studied using γ-alumina supported 12%Ni, 3%Pt and 3%Pt12%Ni catalysts, in a micro-channel reactor. The parallel micro-channels were etched on a stainless steel plate using micro-milling technique with high speed CNC machine. The catalysts were prepared by the incipient wetness impregnation method and were characterized by using XRD, BET, FE-SEM, H2-TPR and TGA analyses. The effects of reaction temperature and feed flow rate on the EG conversion, hydrogen yield and selectivities of the gaseous products were investigated. Experimental findings revealed that 3%Pt12%Ni/γ-alumina catalyst can provide the highest EG conversion (96.1%) with 76.6% hydrogen yield and 5.3% CO selectivity at 450 °C temperature and 4 mL h−1 feed flow rate. Furthermore, continuous EG steam reforming identified 3%Pt12%Ni/γ-alumina as the most stable catalyst. This catalyst can remain stable after being on stream for more than 20 h.

A novel dynamic contour error estimation and control in high-speed CNC

High-speed CNC machining requires high-precision servo response and dynamic performance of feed drives with better control parameters matched in coupled axis. In this paper, an electromechanical coupling system of multi-axis CNC machine tool feed drive model is established for servo parameter optimization and high-precision contour error improvement. The influences of the joint stiffness and the friction disturbance in servo dynamic have been considered. The disturbance parameter and joint stiffness have been identified by unbiased least squares scheme. The electromechanical coupling model is used to study the contour error estimation and compensation technology in high-speed machining. The multi-axis contour error is estimated by using Ferguson curve to approximate the curve between the interpolation points. The calculation is fast and accurate enough for contour error pre-compensation. Experiments prove that five-axis contour error can be effectively reduced by dynamics pre-compensation in high-speed CNC with better processing quality and stability improvement.

Simulation Analysis on the Static Characteristics of High Speed Milling Motorized Spindle

The structure design and the static characteristics of the motorized spindle determine the machining quality and cutting performance of high speed CNC machine tools. In this paper, the ANSYS finite element software is used to analyze the static characteristics of the high speed milling motorized spindle, so as to verify the rationality of the structure design.

The Machining Process and Programming of Hardened Steel Based on Concurrent Engineering

High speed machining can not only greatly improve production efficiency, but also can further improve the machining precision and surface quality, and solve some efficient processing problem in conventional machining hardened steel and other special materials. In high speed machining process, the use of concurrent engineering can solve the specific problems of processing technology and programming technology, provides practical basis for high speed CNC machining of materials that are difficult to machine as hardened steel. Practice affirms the effect.