High-precision Machining Research Articles

Real-time surrogate compensation architecture for machine-tool thermal error compensation with high-performance model

Thermal error compensation based on high-performance (high-accuracy prediction of thermal errors under complex machining conditions and environments) models is of great significance to high-precision CNC machining. Currently, the high-performance thermal error models can only run at the edge computer due to the computing power limitation of CNC system under real-time constraints, Therefore, the timeliness of the thermal error compensation instruction cannot be guaranteed; delayed and mutated compensation instructions cause defects in the machining surface, which is the main obstacle that restricts the application of thermal error compensation based on high-performance models. In order to solve this challenge, this research proposes a novel real-time surrogate compensation architecture for machine tool thermal error compensation with high-performance model. Based on the look-ahead machine tool control command, the thermal error of next stage could be predicted according to the high-performance thermal error prediction model running on the edge, and the parameters of thermal error surrogate compensation model running on the CNC system could be fitted for high accuracy real-time compensation of the thermal error during the processing process. The verification experimental results on the CK40S CNC lathe show that the proposed surrogate method can retain more than 95% of the accuracy of the original high performance thermal error prediction model. With the surrogate thermal error compensation, the machining surface quality of the machine tool is flawless, and the batch machining error has been reduced from 18.3 μm to 4.5 μm. Compared with the traditional Multi Liner Regression (MLR) model compensation method, the machining accuracy under complex working conditions is further improved by 50%.

Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning method

To explore the impact of environmental factors on soil organic carbon (SOC) with machine learning (ML) model is of great significance for mitigating climate change and soil carbon sequestration and emission reduction. However, the traditional ML model is limited by the hyperparameter adjustment of artificially trial-and-error experimentation and the inexplicability of fitting process, and the precision and performance of ML model cannot be fully utilized. For the end, this study developed a tree-structured Parzen estimator-extreme gradient boosting (TPE-XGBoost) method based on SHapley additive explanations (SHAP) analysis to analyze the response of climate, human activities, soil properties and terrain for SOC (0-200cm) in different land use types of China. The results of descriptive statistics described the order of SOC content: forest land > grassland > cultivated land > unused land. With the increase of soil depth, the SOC content of all land types decreased continuously, and the values indicate a left-skewed non-normal distribution. The fitting accuracy (R2) of TPE-XGBoost model for SOC content was greater than 0.8. At the depth of 0-5cm, the prediction accuracy of cultivated land (R2 = 0.96), grassland (R2 = 0.93), forest land (R2 = 0.95) and unused land (R2 = 0.95) was the highest. The result of SHAP analysis showed that the factors that contributed the most to the fitting accuracy of cultivated land, grassland, forest land and unused land in all depths were temperature, soil pH, temperature and elevation. From surface to deep soil, the mean SHAP value showed a downward trend, indicating that the driving force of environmental factors on the content of SOC gradually weakened. The individual explanations of the variance partitioning (VP) analysis of climate, terrain, and soil property for cultivated land (0-200cm), forest land (30-60cm), and unused land (0-200cm) was as high as 0.32, 0.17, and 0.16, respectively, which indicated that these environmental factors had a high response to SOC content. It is found that the appropriate temperature not only promotes plant roots to obtain nutrients, but also interacts with soil pH on microorganisms, thereby increasing the SOC content. The results confirm that the TPE-XGBoost model based on SHAP analysis can reliably explain the nonlinear driving effect of environmental factors on the SOC, which provides credible decision support for accounting carbon budget and carbon sequestration in large-scale regions.

Rapid high-precision machining of LTCC utilizing water-jet guided laser technology: Expanding the horizons of functional ceramic processing

Rapid high-precision machining of LTCC utilizing water-jet guided laser technology: Expanding the horizons of functional ceramic processing

Reproduction of Three-Dimensional Microstructures on Inconel 718 Utilizing Laser-Textured PECM Cathodes

Abstract Pulse electrochemical machining (PECM) is an unconventional material removal process, widely applied to machine or shape difficult-to-machine materials. A new concept of reproducing three-dimensional (3D) microstructures has been developed and elaborated. Picosecond laser was utilized to produce concave or convex patterns on PECM cathodes, and pattern replication was carried out to PECM anodes. The laser achieved high machining precision and excellent surface integrity; both patterns were successfully replicated on the anodes. However, considerable deviations were found in the reproduced concave patterns on the anodes, possibly due to the rounding and widening effects.

Modeling and characterizing of surface texture produced by scraping process

Abstract Scraping technology is widely used in the guideways manufacturing of high-precision machine tools, yet the scraped surface texture formation mechanism is still not fully understood owing to the complexity and randomness of the scraping process, which limits the study of the tribological properties of scraped bond surfaces. To reveal the formation mechanism of the scraped surface texture, this study adopts the stratified theory to model and characterize scraped surfaces based on the characteristics of the scraping process. The research found tri-Gaussian stratification features in a scraped surface texture and establishes surface stratified model. Tri-Gaussian segmented separation and continuous separation methods are proposed to calculate the component surface parameters for characterizing the scraped surface texture. Segmented and continuous separation methods are respectively applied to analyze and reconstruct simulated tri-Gaussian stratified surfaces and experimental scraped surfaces. The results indicate that the continuous separation method is superior to the segmented separation method and leads to almost the same height and process parameters as the measured surface in the surface reconstruction. This verifies the feasibility and accuracy of the scraped surface texture model and characterization methods. This paper reveals the surface texture formation mechanism from the viewpoint of the scraping process and provides new insights for modeling and characterizing research on scraped surface texture.

A calibration method of line laser 3D gear measurement system

It is of great importance to ensure the accuracy of measurements taken by the line laser three-dimensional (3D) gear measurement system through the calibration of system in an accurate manner. This paper established a relevant measurement model based on the principle of line laser 3D gear measurement using generalized polar coordinates, designed a dedicated Polyhedral Artefact (PA), and proposed an accurate calibration method to address the challenges posed by the representation of 6 degrees of freedom (DOF) parameters for line laser sensor data in measurement space. Among them, the PA includes features such as cylindrical plane, planar plane, and V-groove etc., which can meet the requirements of high-precision machining while maintaining consistency with gear installation accuracy to further improve the accuracy of sensor pose parameters. The calibration method uses nonlinear optimization equations with point cloud data to simultaneously solve the required parameters for accurate measurement. Calibration and gear measurement experiments reveal that the standard deviation and range deviation of the total tooth profile deviation, obtained from 10 repeated measurements of a single tooth flank, are 0.244 μm and 0.86 μm, respectively. The results of the tooth profile measurements after the calibration of the system show good consistency compared with the contact measurements, which verifies the effectiveness of the method.

Key feature identification of internal kink mode using machine learning

The internal kink mode is one of the crucial factors affecting the stability of magnetically confined fusion devices. This paper explores the key features influencing the growth rate of internal kink modes using machine learning techniques such as Random Forest, Extreme Gradient Boosting (XGboost), Permutation, and SHapley Additive exPlanations (SHAP). We conduct an in-depth analysis of the significant physical mechanisms by which these key features impact the growth rate of internal kink modes. Numerical simulation data were used to train high-precision machine learning models, namely Random Forest and XGBoost, which achieved coefficients of determination values of 95.07% and 94.57%, respectively, demonstrating their capability to accurately predict the growth rate of internal kink modes. Based on these models, key feature analysis was systematically performed with Permutation and SHAP methods. The results indicate that resistance, pressure at the magnetic axis, viscosity, and plasma rotation are the primary features influencing the growth rate of internal kink modes. Specifically, resistance affects the evolution of internal kink modes by altering current distribution and magnetic field structure; pressure at the magnetic axis impacts the driving force of internal kink modes through the pressure gradient directly related to plasma stability; viscosity modifies the dynamic behavior of internal kink modes by regulating plasma flow; and plasma rotation introduces additional shear forces, affecting the stability and growth rate of internal kink modes. This paper describes the mechanisms by which these four key features influence the growth rate of internal kink modes, providing essential theoretical insights into the behavior of internal kink modes in magnetically confined fusion devices.

Fundamental study on dielectric oil viscosity for high-performance wire EDM

Dielectric oil has been recently used in wire electrical discharge machining (wire EDM) for achieving high-precision machining. However, the influence of dielectric oil properties on wire EDM characteristics has not been clarified sufficiently. This study fundamentally investigated the influence of dielectric oil viscosity on wire EDM characteristics. In this study, three types of dielectric oil differing only in viscosity were prepared and examined. Linear cutting experiment results showed that the removal rate and the machined surface roughness increase with dielectric oil viscosity. Then, the influence of viscosity on crater removal volume was investigated to discuss the cause of the change in removal rate, and it was found that crater removal volume increases with the viscosity. Furthermore, CFD (computational fluid dynamics) analysis results and discharge observation results showed that the debris exclusion becomes worse in higher viscosity oil, and the discharge concentration occurs frequently, resulting in the deterioration in machined surface roughness. Next, the difference in machining accuracy with dielectric oil viscosity was investigated. It was found that the corner shape accuracy deteriorates with the increase of viscosity while the machined surface waviness and straightness improve slightly. Structural analysis results showed that wire deflection increases with viscosity, resulting in the deterioration in corner shape accuracy. On the other hand, high-speed observation of wire vibration was carried out, and it was found that the wire vibration decreases in the case of higher viscosity, which leads to the improvement in machined surface waviness and straightness.

Effect of vibration frequency on tribological characteristics of vibration-assisted micro-nano machining

Experimental findings regarding the influence of vibration frequency on the machining outcomes of vibration-assisted machining (VAM) have been inconsistent. This work aims to address this issue through both experimental and theoretical analyses. The results indicate that the circuit's frequency response is the primary source of these discrepancies. When the vibration frequency is less than half the cutoff frequency, the friction force reaches a minimum while the wear volume peaks. The solution of the heat conduction equation demonstrated that the acoustic softening effect drives the softening mechanism in nanoscale VAM. Molecular dynamics (MD) simulations further elucidate the damage mechanisms during the wear process, showing that high-frequency vibrations contribute to achieving high-precision machining.

The intelligent operation and maintenance method and experimental research of CNC machine tool bearings

As the core components of high-precision rotary machine tools, machine tool bearings are prone to failure or damage, and their running state directly affects the safety and reliability of the entire equipment. Therefore, the paper proposes an intelligent operation and health management method for machine tool bearings based on Bayes-VMD-KPCA, Bayes-CNN-SVM, and health status evaluation index. Aiming at the complex working environment and high nonlinear degree of fault features of machine tool bearings, the Bayes-VMD-KPCA algorithm is used to perform variational mode decomposition of the original signal of the bearing fault, which avoids the influence of background noise and solves the problem of high dimension of multi-domain fault feature set. Second, the CNN-SVM fault diagnosis model uses a convolutional neural network (CNN) to deeply mine the extracted multi-domain fault features, and a support vector machine (SVM) can be used as a fault classifier to complete the diagnosis of various fault types of machine tool bearings. At the same time, the Bayes algorithm is introduced to tune the hyperparameters in the CNN-SVM model to further improve the prediction performance of the CNC machine tool-bearing fault diagnosis model. The results show that the intelligent operation and maintenance and health management method of CNC machine tool bearings proposed in this paper can effectively complete the fault type diagnosis and health status assessment of bearings, and its accuracy index reaches 99.33%. Compared with the single SVM model, Bayes-CNN model, Bayes-SVM model, and CNN-SVM model, the health effect is evaluated. The accuracy is increased by 6.33%, 4.33%, 2.66%, and 1%, respectively. It can be seen that the proposed method can more effectively realize the monitoring and health management of bearing fault.

Development of an external system for monitoring the couch speed in radiotherapy using continuous bed movement.

Total body irradiation before bone marrow transplantation for hematological malignancies using Radixact, a high-precision radiotherapy machine, can potentially reduce side effects and the risk of secondary malignancies. However, stable control of couch speed is critical, and direct assessment methods outlined in quality assurance guidelines are lacking. This study aims to develop a real-time couch speed verification system for the Radixact. The developed system used a linear encoder to measure couch speed directly. Accuracy was verified via a linear stage, comparing measurements with a laser distance sensor. After placing a phantom simulating the human body on the Radixact couch, the couch speed was verified using predefined speed plans. Operating the linear stage at 0.1, 0.5, and 1.0mm/s revealed that the maximum position error of the developed verification system compared to the laser distance sensor was nearly equivalent to the distance resolution of the system (0.05mm/pulse), with negligible average speed error. When the Radixact couch operated at 0.1, 0.5, and 1.0mm/s, the values obtained by the verification system agreed with the theoretical values within the sampling period (0.01s) and distance resolution (0.05mm). The verification system developed provides real-time monitoring of the speed of the Radixact table, ensuring treatment effectiveness and patient safety. It would guarantee the couch speed's soundness and contribute to the "visualization" of safety.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Progress in the Study of Quality and Mechanical Properties of Selective Laser Melting Molded Parts

Abstract: Selective laser melting (SLM) is considered to be a widely promising additive manufacturing technology with the advantages of high machining precision, high manufacturing freedom, and short cycle time, which is widely used in aerospace, the integration of medicine, and industry, chemical, and other fields. The research progress on the temperature field, stress field, forming quality, and mechanical properties during the SLM manufacturing process is reviewed. The study aims to systematically analyze how SLM process parameters affect the temperature field, stress field, forming quality, and mechanical properties, and to discuss the importance of the selection of process parameters and performance regulation to achieve high-quality, highperformance metal parts. The effects of SLM process parameters on temperature field, stress field, surface roughness, densification, hardness, strength, and fatigue properties are analyzed and summarized. The importance of process parameters in the SLM forming procedure in the quality of formed components is emphasized, and conducting an in-depth study on the optimization and performance regulation of the process parameters is of great significance in achieving the high quality and performance of metal parts. With the rapid advancement of technology, the potential of SLM technology in terms of molding quality and mechanical performance has become increasingly significant, heralding significant breakthroughs. These potential breakthroughs will greatly promote the widespread application of SLM technology in various industries, thus more efficiently meeting the growing needs and expectations of industries such as petrochemicals, transportation, aerospace, nuclear energy, as well as food and medical sectors.

CFD Research for Air Bearing with Gradient-Depth Recesses

Ultra-precision measurement and manufacturing need high-precision machines, just as a photolithography machine needs air bearings. In gas lubrication, the use of compound restrictors with recesses has been widely proven to be an effective method to improve stiffness, which directly affects the accuracy of the machine. However, determination of the structural parameters of recesses is lacking in theoretical models. This paper has established a mechanical property model for a small-scale guideway, which can respond to the variation in force caused by micron-level changes in the recesses’ depth. To meet the requirements of high positioning accuracy and movement accuracy, this paper puts forward a high-stiffness guideway without an air tube. In order to improve rotational stiffness and determine the structural parameters of recesses, this paper found a guideway with the optimal gradient depth of recesses. Both AFVM (adaptive finite volume method) research and experimental results show that the gradient depth of recesses could significantly improve the rotational stiffness of guideways without air tubes.

УТОЧНЕНА МОДЕЛЬ ДВОКАНАЛЬНОГО ЕЛЕКТРОПРИВОДА ПОДАЧІ З ПІДСУМОВУВАННЯМ РУХІВ НА ХОДОВІЙ ГАЙЦІ ДЛЯ ВИСОКОТОЧНИХ МЕТАЛОРІЗАЛЬНИХ ВЕРСТАТІВ

The iterative two-channel feed electric drive (ED) with the summation of movements on the rotating sliding nut (RSN) is designed to increase the speed and accuracy of traditional single-channel ED feed mechanisms (FM) of metal-cutting machines with an inertial working tool (WT). A refined generalized mathematical model of movement of the two-channel ED with RSN was obtained, which was built for WT FM of the high-precision coordinate metal-cutting ma-chine of the 24К60АФ4 model. The model takes into account the main static moments of resistance during metalwork-ing, as well as the nonlinear nature of the sliding friction forces in the machine WT FM. Convenient recurrence rela-tions are obtained for the calculation and modeling of sections of the linearized friction characteristic. The structural-algorithmic diagram of the compensated iterative two-channel feed ED with subordinate configuration of control chan-nels is proposed, which makes it possible to compensate in steady-state conditions the negative impact on the accuracy of WT feed of the main static moments of resistance and nonlinearities of sliding friction forces in the drive load. Ref. 9, fig. 3, table.

Research on the supporting mechanism of internal feedback hydrostatic hybrid bearing considering flow rate correction

The internal feedback hydrostatic bearing features a distinctive structural design, playing a pivotal role in high-precision machine tools. Its performance significantly influences the improvement of machining accuracy and stiffness. In this study, the working principle of the internal feedback closed hydrostatic bearing is described, the equivalent liquid resistance model of oil circuit considering internal flow effect is established, an analysis method for evaluating the support performance of the internal feedback hydrostatic bearing is proposed, flow rate correction methods are proposed, and the influence of load-carrying capacity and flow rate due to inlet pressure, oil film thickness, and the size of the restrictor is discussed. The accuracy of the method is verified by simulation, and insights for the future development of hydrostatic bearings are provided.

On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

Abstract On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

A machine learning system for artificial ligaments with desired mechanical properties in ACL reconstruction applications

The anterior cruciate ligament is one of the important tissues to maintain the stability of the human knee joint, but it is difficult for this ligament to self-heal after injury. Consequently, transplantation of artificial ligaments (ALs) has gained widespread attention as an important alternative treatment method in recent years. However, accurately predicting the intricate mechanical properties of ALs remains a formidable challenge, particularly when employing theoretical frameworks such as braiding theory. This obstacle presents a significant impediment to achieving optimal AL design. Therefore, in this study, a high-precision machine learning model based on an artificial neural network was developed to rapidly and accurately predict the mechanical properties of ALs. The results showed that the proposed model achieved a reduction of 45.22% and 50.17% in the normalized root mean square error on the testing set when compared to traditional machine learning models (Random Forest and Support Vector Machine), demonstrating its higher accuracy. In addition, the design of ALs with desired mechanical properties was achieved by optimizing the braiding parameters, and its effectiveness was verified through experiments. The mechanical properties of the prepared ALs were able to fully meet the desired targets and were at least 2% higher. Finally, the influence weights of different braiding parameters on the mechanical properties of ALs were analyzed by feature importance.

Innovative Structural Optimization and Dynamic Performance Enhancement of High-Precision Five-Axis Machine Tools

To satisfy the requirements of five-axis processing quality, this article improves and optimizes the machine tool structure design to produce improved dynamic characteristics. This study focuses on the investigation of five-axis machine tools’ static and dynamic stiffness as well as structural integrity. We also include performance optimization and experimental verification. We use the finite element approach as a structural analysis tool to evaluate and compare the individual parts of the machine created in this study, primarily the saddle, slide table, column, spindle head, and worktable. We discuss the precision of the machine tool model and relative space distortion at each location. To meet the requirements of the actual machine, we optimize the structure of the five-axis machine tool based on the parameters and boundary conditions of each component. The machine’s weight was 15% less than in the original design model, the material it was subjected to was not strained, and the area of the structure where the force was considerably deformed was strengthened. We evaluate the AM machine’s impact resistance to compare the vibrational deformation observed in real time with the analytical findings. During modal analysis, all the order of frequencies were determined to be 97.5, 110.4, 115.6, and 129.6 Hz. The modal test yielded the following orders of frequencies: 104, 118, 125, and 133 Hz. Based on the analytical results, the top three order error percentages are +6.6%, +6.8%, +8.1%, and +2.6%. In ME’scope, the findings of the modal test were compared with the modal assurance criteria (MAC) analysis. According to the static stiffness analysis’s findings, the main shaft and screw have quite substantial major deformations, with a maximum deformation of 33.2 µm. Force flow explore provides the relative deformation amount of 26.98 µm from the rotating base (C) to the tool base, when a force of 1000 N is applied in the X-axis direction, which is more than other relative deformation amounts. We also performed cutting transient analysis, cutting spectrum analysis, steady-state thermal analysis, and analysis of different locations of the machine tool. All of these improvements may effectively increase the stiffness of the machine structure as well improve the machine’s dynamic characteristics and increases its machining accuracy. The topology optimization method checks how the saddle affects the machine’s stability and accuracy. This research will boost smart manufacturing in the machine tool sector, leading to notable advantages and technical innovations.

Construction and Analysis of Cutting Force and Stability Prediction Model in CNC Milling Thin-Walled Parts Machining

Thin-walled parts are widely used in precision instruments, automobiles and other fields for their high strength, light weight and other characteristics. However, in the process of CNC milling, as the thickness of the parts decreases, their rigidity gradually decreases, resulting in the machining process is very easy to produce vibration, and even chatter, which seriously affects the accuracy and surface quality of the parts. Traditional manufacturing methods usually use conservative cutting dosage to reduce vibration, but this greatly limits the performance of CNC machine tools and cutting tools. In order to solve this problem, this paper thoroughly investigates the dynamic characteristics of the cutting process of thin-walled parts, and constructs a cutting force and stability prediction model. Through a combination of theoretical analysis, experimental verification and numerical simulation, this paper comprehensively analyses the impact of cutting parameters, tool geometry, workpiece material properties and other factors on cutting force and machining stability. The research results not only help to improve the cutting theory of thin-walled parts, but also provide an important technical support for the realisation of high-efficiency and high-precision machining. The results of this paper show that by optimising the cutting parameters and tool geometry, the cutting force can be effectively reduced and the machining stability can be improved, so as to achieve efficient and high-precision machining of thin-walled parts. In addition, this paper also explores the causes and mechanisms of vibration in the machining process, providing a theoretical basis for the development of targeted vibration control measures. The research in this paper is important for promoting the development of thin-walled parts machining technology, not only helps to improve the processing efficiency and quality of parts, but also helps to promote the manufacturing industry to a more efficient, more precise direction.