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This paper presents the development of a methodology for monitoring the part machining process on CNC machine tools using a virtual machine model in the MATLAB/Simulink environment. The focus is on monitoring the components of cutting forces that occur during the milling process. A vertical milling machine was used for model validation. By integrating the existing cutting force model and the virtual machine model in MATLAB/Simulink, the visualization of real-time cutting force values, which depend on the change of the current engagement map along the programmed toolpath, is enabled. The development of the presented simulation environment forms the basis for the implementation of new functions in G-code, contributing to the stability and optimization of the machining process. [ABSTRACT FROM AUTHOR]

In this study, a system for measuring the positional accuracy of industrial robots using 3D vision and networking technology has been proposed. The 3D vision system comprises a charge-coupled device (CCD) module, a reference sphere module, and a signal processing module. The reference sphere module serves as the sensing object, while the CCD module captures displacement images of the reference sphere module. The signal processing module receives and processes signals from the CCD module. It utilizes a self-developed Visual C + + interface to calculate the X, Y, Z, θx, and θy (five-degree-of-freedom, 5-DOF) positional accuracy of the industrial robot. Experimental results demonstrate that the linear accuracy (X, Y, Z) and angular accuracy (θx, θy) of the 3D vision system are approximately 3 μm and 0.15°, respectively. Additionally, a networking architecture connects the controller of the machine tool with the industrial robot to enable synchronized motion path planning. The industrial robot can be calibrated using the high-precision machine tool equipped with the 3D vision-based positional accuracy measurement system. [ABSTRACT FROM AUTHOR]

The pre-compensation of contouring error can improve the machining quality effectively, but the pre-compensation values are not optimal by the existing methods. To obtain a better pre-compensation value of the contouring error for five-axis machine tools, this paper proposes a data-driven iterative pre-compensation method. In detail, a data-driven prediction model of tracking error is proposed first to avoid the negative impact of modeling error on the pre-compensation value. The output of the prediction model consists of two parts: a linear part and a nonlinear part. The linear part is obtained by the identified model of the drive system, while the nonlinear part, which is caused by the uncertainty of the drive system, is the output of a trained neural network. With the predicted tracking error, the contouring error can be further predicted through forward kinematic. Then, the pre-compensation value is calculated by predicting and accumulating the contouring error iteratively, which is close to being optimal. Through the iterative operation, the potential contour error is suppressed effectively. The convergence of the proposed iterative process is proved theoretically. Finally, the reference position command of each axis is modified before machining by using the calculated optimal value. The experiments were conducted on a self-constructed five-axis machine tool. The experimental results consistently indicate that, by the proposed pre-compensation method, the contouring error is predicted accurately and reduced significantly. [ABSTRACT FROM AUTHOR]

High precision requirements in mechanical products have led machine tools to continuously improve their machining accuracy. However, the existence of geometric errors significantly affects the accuracy of machine tools. On-machine measurement (OMM) has become increasingly available for machine tools, offering an alternative method for identifying geometric errors as compared to the commonly applied traditional interferometry-based techniques. A method to identify the geometric errors based on on-machine measurement of a calibrated artefact is proposed. The mapping relation between the multiple sets of reference points on the artefact and the various types of geometric errors is elaborated on in detail. Geometric error identification experiments and the verification experiment by a laser tracer are conducted. The experiments indicate that the identification results from the proposed method and verification match well with each other, which confirms the effectiveness of the proposed identification method. Moreover, the impact of setup errors on the identification is analyzed, which suggests that the influence induced by setup errors on the identification result can be reasonably controlled. Periodic verification of machine tool geometric errors, as described in ISO 230–2, suggests continuous monitoring of machine tool accuracy and prompt detection of any degradation in accuracy. The OMM-based identification method is more easily automated and more efficient for periodic verification of machine tool accuracy. [ABSTRACT FROM AUTHOR]

Industrial robot machining systems are used extensively in processing and manufacturing sectors including aerospace, large ships, rail transit, engineering machinery, semiconductor processing, medical equipment, and new energy vehicles. However, chatter during the machining process can considerably diminish the machining accuracy and surface quality. In severe cases, it can cause damage to the tool and the machining robot. Robot machining chatter research is required to improve the stability of the machining process for high-efficiency, high-quality robotic machining. This review comprehensively summarizes research progress in three areas: robot machining chatter mechanisms, chatter stability prediction and online monitoring, and chatter suppression strategies. First, the generation mechanisms of regenerative chattering and modal-coupling chattering are clarified. Second, the methods of chattering stability prediction and online monitoring of robot machining are summarized. The differences between passive suppression, semi-active suppression, and active suppression strategies are explained. Existing problems and future directions in robot machining chattering research are summarized and prospected. [ABSTRACT FROM AUTHOR]

The static and dynamic performance of the machine tool directly affects the machining accuracy, dimensional stability, and service reliability. In order to analyze the mechanical performances of a precision horizontal machine center, a finite element (FE) model was established using ANSYS software. The deformation curve of the guide rail was obtained through static analysis of nine different working conditions. The dynamic characteristics of the precision horizontal machining center were obtained through dynamic analysis. And modal hammer impact tests were conducted on the machining center to verify the simulation results. The results showed that the maximum deformation of the guide rail was 0.0134 mm (situation 6), and the experimental measurement values of the first five natural frequencies of the whole machine were 96 Hz, 116 Hz, 198 Hz, 213 Hz, and 229 Hz. And it was found that adding a column structure can significantly improve the dynamic characteristics of precision horizontal machining centers. This study provides a theoretical basis and technical support for the improvement and optimization of the dynamic and static performance of machine tools. [ABSTRACT FROM AUTHOR]

Thermal errors account for a significant part of the dimensional errors of components produced by precision machine tools. These errors are commonly compensated using predictions from temperature-based empirical models. The accuracy and robustness of these predictions are affected by the locations of temperature sensors used to obtain the model's input data. Methods for sensor selection found in literature are often difficult to replicate and automate because they require tuning of several hyperparameters. This work presents a sensor and model selection approach that uses proper orthogonal decomposition (POD) and QR pivoting to select a subset of sensors that have been preinstalled in the machine tool as possible model inputs. These sensors are then sorted according to their correlation with the thermal error being modelled. The final set of inputs and thermal error model structure is chosen using Bayesian Information Criterion (BIC) to limit model overfitting. The approach was tested by modelling the Y-axis thermal error measured from air-cutting experiments performed under different spindle speeds and feed rates. This enabled determination of the structure and the choice inputs for Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) thermal error models. The accuracy of these models was compared to that of models trained using inputs selected by conventional approaches: the least absolute shrinkage and selection operator (LASSO), fuzzy c-means clustering (FCM), and principal component regression (PCR). The presented approach had better or comparable results to the conventional approaches while using fewer inputs. The presented approach is also well suited for automation compared to conventional approaches that require expert input. [ABSTRACT FROM AUTHOR]

Robotic manipulators play an innovative role as a new method for high-precision, large-scale manufacturing of composite components. However, machining composite materials with these systems presents unique challenges. Unlike traditional monolithic materials, composites exhibit complex behaviour and inconsistent results during machining. Additionally, robotic manipulator as a machine tool often associates with stiffness and vibration issues which adds another layer of complexity to this approach. By employing a comprehensive analysis and a combination of quantitative and qualitative review methodology, this review paper aims to survey diverse properties of composite materials by different categories and their interaction with machining processes. Subsequently, a survey of manufacturing techniques for composite machining following with a review in various modeling practices to capture material machining behaviour under a systematic framework is presented. Thereafter, the reviewed literature examines the errors inherent in robotic systems, alongside ongoing research efforts in modeling to characterise robot behaviour and enhance its performance. Afterward, the paper explores the application of data-driven modelling methods, with a primary focus on digital twins, in enabling real-time monitoring and process optimisation. Finally, this paper aims to identify the gap in this field and suggests the potential routes for future research and application as well as their challenges. [ABSTRACT FROM AUTHOR]

To meet the requirement for enhancing the machining precision of electric spindles in high-speed precision machine tools, we have optimized and designed a new type of electric spindle using a cage-type asynchronous AC motor. Thermal properties have been improved by optimizing the integration of heat sources and the cooling system to address heat accumulation in bearing components. Experimentation confirmed that, at speeds ranging from 6000 to 10,000 revolutions per minute (rpm), the new spindle exhibited a 19.61% decrease in axial thermal displacement and a 59.52% reduction in radial thermal displacement compared to conventional spindles. In order to further control thermal error accurately, a new type of thermal error prediction model for electric spindles was constructed by integrating the Improved Sine Cosine Algorithm (ISCA) and Generalized Regression Neural Network (GRNN). Using the experimental data within the aforementioned speed range, the thermal errors of the new electrospindle were predicted. Compared with the unimproved SCA-GRNN prediction model, the ISCA-GRNN prediction model reduces the mean absolute error by 38.90% and the root mean square error by 27.47%, signifying a significant performance enhancement that verifies the validity of the proposed model. [ABSTRACT FROM AUTHOR]

The Frequency Response Function (FRF) at the tool-tip is an important input for machining dynamics prediction, but its measurement is a time-intensive process. The receptance coupling technique offers a faster alternative, suitable for industrial applications where there is experimentally identified receptance matrix at machine-tool holder interface containing enough degrees of freedom to allow connection with various compliant tools modeled using finite element analysis (FEA). The presented approach to the technique further develops simplification of the inverse RCSA by using a single artifact proposed by Montevecchi, presenting a simpler and more general mathematical formulation of the receptance matrix identification. The study also looked into how simplifying tool models affects their accuracy and performance in FEA, aiming to find a good balance between detail and efficiency. To prove the proposed technique, thorough tests on two dynamically different machine tools were conducted with various tool geometries. [ABSTRACT FROM AUTHOR]

The blisks are complex thin-walled parts with specific structures that have narrow channels and a large degree of bowed-twisted blades. These parts are typically machined using 5-axis machining. However, conventional feedrate scheduling of the tool tip can cause flutter and reduced machining accuracy when dealing with very small radii of curvature and dramatic changes in the tool axis vector. This is because there is a significant difference in moving speed between the tool tip and cutting contact points. To address this issue, we establish an optimization model for feedrate under constraints such as process-allowed cutting speed and machine tool drive along the toolpath. For sections of short toolpaths in the regions of leading and trailing edges with drastic changes in curvature and tool orientations, we schedule a constant feedrate for the tool contact point. For other sections of the toolpath, we construct time-optimal acceleration and deceleration velocity curves using parallel computing technology, ensuring no abrupt changes in acceleration or acceleration velocity at boundary points. Additionally, we smooth the feedrate profile curves for the entire toolpath using the parallel bidirectional scanning method. This approach improves both the efficiency of feedrate scheduling and cutting stability within the regions corresponding to the leading and trailing edges of blisks. [ABSTRACT FROM AUTHOR]

Structural chatter vibrations of machine tools are very complex nonlinear dynamic behaviors, which limit the machine tool productivity. Variable spindle speed milling is an efficient way to suppress chatter. This paper proposes a novel method for the stability analysis and parameter optimization of milling processes with periodic spindle speed variation. With the aid of the 3rd-order Newton-Cotes formula, the time-varying delay differential equation is disassembled into two parts, and the relationship between the two parts is constructed by the transition matrix. The iterative calculation of the transition matrix only depends on the spindle speed. By comparison with other methods, such as the semi-discretization method and the reconstructed semi-discretization method, the proposed method has the advantages of high computational efficiency and prediction accuracy. Finally, milling stability boundaries, chatter frequency analysis, and modulation parameter optimization are carried out to further investigate the dynamic characteristics of variable spindle speed milling proposed in this work. [ABSTRACT FROM AUTHOR]

To improve the surface roughness (Ra) of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Initially, factors affecting the surface roughness are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. A BP neural network is then used to establish a nonlinear mapping relationship between the polishing process parameters and the surface roughness of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters.According to the optimization results, the process parameters of the abrasive belt polishing machine model are modified as follows: polishing arm cylinder pressure of 0.8 MPa, rotational axis output speed of 140 rpm, abrasive belt model B with grit size 800#, Coolant type concentration of 9%, and polishing time of 20 s. In comparison to the initial scheme parameters of the abrasive belt polishing machine, this combination of parameters resulted in a 25.2% increase in surface roughness at the connecting rod journal and saved 20% of the polishing time. Through the process parameter optimization method in this paper, it will contribute to improving the production performance of the crankshaft abrasive belt polishing machine, effectively enhancing the surface roughness at the processed connecting rod journal and saving polishing time. [ABSTRACT FROM AUTHOR]

Tool wear is an important issue to be considered in machining analysis and improvement. The non-uniform flank wear of ball-end tool is a commonly existing phenomenon in ball-end milling but has been less concerned so far. In this paper, the non-uniform flank wear of ball-end tools in 5-axis machining based on real cutting length calculation is discussed. Firstly, the flank wear model of differential cutting edge is constructed considering the real cutting length (RCL). Secondly, the RCL is obtained with intersection point calculation according to the cutter workpiece contact analysis. Then, the distributions of accumulated RCL and flank wear are analyzed and the similarity between them is found, based on which a tool orientation optimization method is given to improve the tool wear conditions. Lastly, machining experiments are carried out to validate the efficiency and accuracy of the prediction method. The results of the calculation and experiment show that the tool orientation optimization can help to reduce the maximum flank wear. [ABSTRACT FROM AUTHOR]

In the machining process of computer numerical control (CNC) machine tools, both dynamic and static errors significantly impact the machining accuracy. The ability to accurately estimate the machining quality of products in real time is of great significance for the closed-loop control of machining accuracy of CNC machine tools. For this purpose, this paper presents a digital twin synchronous evolution method of CNC machine tools by associating dynamic and static errors. Initially, a digital twin intrinsic model of CNC machine tools with static geometric errors is constructed in the virtual space, and the high-fidelity mapping of digital twin of CNC machine tools oriented to product processing path is realized. Subsequently, an acquisition method of multi-scene perception data of CNC machine tools is described, and the unified representation of multi-source heterogeneous data is achieved based on semantic mapping. Finally, dynamic errors such as spindle thermal deformation and tool wear are analyzed in real time using integrated multi-scene sensing data. These errors, along with the static geometric errors of the tool, are reflected to the workpiece through the tool machining trajectory, which provides a basis for the closed-loop control of digital twin oriented to product quality. The proposed method is integrated into the digital twin platform of CNC machine tools, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) materials have gained significant attention in the machining industry due to their exceptional strength-to-weight ratio and vibration characteristics. In the poor rigidity machining tool system, such as plunge milling, deep drilling, and boring, the large overhanging shaft increases the requirement of chatter stability. In this study, a solution of carbon fiber reinforced shaft was proposed for the poor rigidity machining tool system. The vibration characteristic model was established by theoretical calculation, FE analysis, and modal testing. The relationship between fiber arrangement and vibration characteristics was precisely described, and the optimization scheme of fiber reinforcement was proposed considering both the theoretical analysis and procedure technique. The machining performance of the optimal HSS-CFRP shaft was compared with that of the traditional HSS shaft in the same tool system. The main aspects that significantly affect the machining performance were thoroughly evaluated, including tool weight, energy consumption, dynamic characteristics, and chatter stability. The effect of fiber reinforcement was fully discussed, which provided a reliable solution for the poor rigidity machining system and other potential applications with large overhanging shaft. [ABSTRACT FROM AUTHOR]

Modeling and compensating for geometric errors are important steps for improving the accuracy of three-axis machine tools. The kinematic link between the tool and the workpiece is established with a series of homogeneous transformation matrices (HTMs). The volumetric error and compensation values are then calculated based on the pre-measured geometric errors and the pre-built kinematic model. Conventionally, all 21 geometric errors are considered simultaneously in the kinematic model, necessitating a careful HTM construction process to avoid unexpected errors during the derivation of the final simplified formula for compensation. For the possible 24 configurations for three-axis machine tools, this computational process has to be done individually. To solve these time-consuming and inefficient processes, we present a unified error model capable of generating error vectors applicable to all configurations. Instead of considering all geometric errors simultaneously, the proposed method analyzes the errors in subgroups and recombines the error terms at the final step. The advantage of this approach lies in clarifying the impact of geometric errors on volumetric errors, as well as expressing error and compensation vectors for various machine tools through a unified model. The proposed unified model is theoretically demonstrated by applying it to both vertical and horizontal three-axis machine tools. Furthermore, the effectiveness of the proposed method is confirmed by conducting experimental validation on a vertical three-axis testbed. [ABSTRACT FROM AUTHOR]

Ball screws are subjected to complex loads during machining processes, which affects the position accuracy of the feed shaft of machine tools. The lack of a reliable model for analyzing the deformation of the ball screw, coupled with the unclear relationship between the deformation of the ball screw and the position error of the feed shaft, makes it challenging to accurately predict and compensate for the position error. The objective of the present research is to investigate the correlation between the deformation of the ball screw and the position error of the feed shaft to improve the accuracy of the machine tool. First, this paper establishes a FEM model to explore the loading and deformation of the ball screw. Secondly, a method for predicting position errors based on the deformation of the ball screw is proposed. Finally, the ball screw-loading test device is used for the position error compensation experiment to verify the reliability of the prediction method. This paper serves as a reference for understanding the generation mechanism of position errors in machine tool feed systems. [ABSTRACT FROM AUTHOR]

Gantry five-axis machine tools have garnered acclaim for their robustness, cost-effectiveness, and accuracy in fabricating intricate workpieces. In the era of Industry 4.0, these tools necessitate heightened flexibility, precision, and cognitive capabilities to meet contemporary industrial requisites and accommodate escalating processing demands. This study delved into a comprehensive analysis of a gantry five-axis machine tool via the finite element method, encompassing static, modal, spatial accuracy analysis, and topological optimization, to grasp the structural attributes of the entire apparatus. Modal experiments were conducted to validate the numerical findings, with experimental and analytical errors falling within an acceptable range, ranging from 11 to 2.5% respectively. The analysis outcomes facilitated the identification of structural vulnerabilities and resonance frequencies, enabling the avoidance of these frequencies during machining, thereby enhancing machining precision and work piece value. Utilizing ANSYS mechanical, topology optimization of the column was executed, achieving objectives such as weight reduction, augmented rigidity, and enhanced natural frequencies in contrast to the original design. Lattice optimization was applied to the spindle, aiming to elevate the fundamental frequency and diminish mass. This approach yielded a notable frequency increase from 580 to 736 Hz and nearly halved the mass. Spatial accuracy analysis facilitated the detection of component errors at various machine positions, enabling input to the machine tool's controller for error compensation, thereby enhancing precision. This comprehensive investigation underscores the significance of advanced analytical techniques and optimization strategies in fortifying the performance and efficacy of gantry five-axis machine tools in modern industrial settings. [ABSTRACT FROM AUTHOR]

Difficult-to-machine materials are characterized by high hardness, strength, and anisotropy. During machining, tools can suffer from adhesion, abrasion, material detachment, and high cutting temperatures. These factors result in severe adhesive wear, abrasive wear, oxidative wear, and diffusion wear. Intense tool wear leads to more burrs, delamination, chipping, and dimensional errors in the parts. It significantly increases production costs and reduces machining efficiency. Therefore, further research into tool wear is of great importance. This paper reviews the characterization and monitoring of tool wear, the morphology and mechanism of wear, and measures for improving wear, with a focus on tool wear in the machining of difficult-to-machine materials. Through a comprehensive review of existing research findings, this work offers a perspective on the investigation of tool wear, providing an effective guide for future research. And it is of great significance to improve tool life and cutting quality. [ABSTRACT FROM AUTHOR]

Currently, the measurement of guide rail straightness error is basically a direct measurement, which cannot meet the requirements of online straightness error measurement in the machining process of the machine tool. Therefore, this paper proposes a straightness error evaluation model based on the recess pressure, which can realize the online measurement of the straightness error of hydrostatic guideways. To address the nonlinear relationship between the recess pressure and the linear deviation data of the guideway in an experiment, based on deep learning, the hydrostatic guideway straightness error was evaluated. First, the experimental data are analyzed and processed by feature, and a sliding window is processed using the data time sequence. Second, a long short-term memory network model is constructed based on an attention mechanism, the model parameters are obtained through orthogonal experiments, and the theoretical straightness error of the hydrostatic guideway is obtained via training. Finally, the theoretical values and experimental values of straightness error are compared and evaluated with the multilayer perceptron and recurrent neural network models. The results show that the model can effectively evaluate the straightness error of hydrostatic guideways. [ABSTRACT FROM AUTHOR]

The clearance flow in dry-running vacuum pumps is the main loss mechanism. To reduce the clearance mass flow rate in rarefied gas flows, sawtooth structures transversing to the direction of flow can be utilized. However, due to the sawtooth's structure size, micro-machining is necessary, whereby burr formation is a central challenge. First, the effectiveness of non-idealized sawtooth structures is investigated, demonstrating a high sensibility of the performance regarding the geometry of the tip. Therefore, burr formation in the cutting process must be minimized. For this reason, a 3D finite element (FE) chip formation model capable of predicting the burr formation is developed. An analysis of the burr formation zone showed positive triaxialities; thus, the triaxiality-dependent Johnson–Cook damage model is utilized. To minimize the mesh-induced error, a convergence analysis is conducted, showing no convergence of the maximum burr height. This is caused by the pathological mesh size dependence of local continuum damage models. A comparison of the cutting experiments and simulations revealed a reasonable prediction of cutting forces. In contrast, the passive force is predicted poorly, which is attributed to the underestimation of the ploughing force for non-elastic simulations. The prediction quality regarding the maximum burr height differs for the investigated cutting speeds, which can be explained by a built-up edge and a change in the machine tool compliance. Thereby, an analysis of the burr formation revealed that the burr height is captured by a non-physical remeshing algorithm and that the burr volume might be a more appropriate characteristic. [ABSTRACT FROM AUTHOR]

As the "mother machine" of manufacturing, machine tools have been widely employed, but with high energy consumption, low energy efficiency, and serious carbon emissions. How to reveal the dynamic characteristics of carbon emissions is of great significance for machine tools to achieve green and sustainable development. Existing research neglected the dynamic characteristics of carbon emissions and focused on a single phase for machine tool. To this end, this study carried out the digital twin-driven modeling and application of carbon emission for machine tool considering both the design and usage stages. Utilizing real-time data acquisition and model refinement of digital twin system, this research proposed an adaptive algorithm for the dynamic prediction and optimization of processing parameters. Related management services in usage stage based on digital twin was developed and a digital twin-driven carbon emission prototype system was established. And a milling experiment was performed to validate the practicality and feasibility of the proposed algorithm. Besides, combining with operation data recorded by digital twin model, working conditions of machine tool were simulated in the digital space. Optimization of tool part selection under simulated conditions can be realized during design stage, which makes the closed-loop design for machine tool. Considering both design and usage stages, this research offers a reference solution for the multidimensional, intelligent, and collaborative management of carbon emissions for machine tool. [ABSTRACT FROM AUTHOR]

As an intermittent cutting process, fly-cutting machining often results in unwanted and non-negligible vibrations, which adversely affect the surface quality of the machined workpiece. This work presents a dynamic simulation and active vibration control approach for the ultra-precision fly-cutting machine tool based on the transfer matrix method for multibody systems (MSTMM) and the independent modal space control method. The dynamic model of the ultra-precision fly-cutting machine tool system, incorporating rigid body elements, beam elements, flexible body elements, and hinge elements is established. The dynamic equations of the system are derived by directly combining the body dynamic equations of all body elements and decoupling them using augmented eigenvectors. Further, the state-space representation of the system is obtained and expressed within each independent modal space. The structure of the active vibration control system, including the modal filter and controller, has been meticulously designed. The controlled modes are selected based on H 2 norm of each mode, while the optimization of actuator positions is performed using the controllability Gramian. Finally, numerical simulations are conducted to verify the efficacy of the proposed modal space control approach in effectively reducing the tool-tip vibration and improving the machining accuracy. [ABSTRACT FROM AUTHOR]

The increasing demand for machined components of superior quality at competitive costs has driven machine and cutting tools manufacturers to innovate in machine tool technologies and cutting tools materials. Consequently, optimizing the machining process becomes an important subject of research. Particularly, optimizing machining parameters like cutting speed, depth of cut and feed rate stands out. Literature review highlights that optimization considers various "objective functions" such as minimum production cost, maximum production rate, and minimum energy consumption. However, one aspect not objectively considered is the customer's demand within a specific timeframe, known as takt time. The relevance, originality and contribution of this work is the development of a method that, considering the current machining parameters and the takt time for a given machining stage, defines the optimized cutting speed for each cutting tool to meet the takt time at the lowest production cost. An algorithm based on classical equations for calculating machining times and costs for machining processes with multiple cutting tools was developed. A software in Excel receives process data, "x and K" coefficients from Taylor's life equation and calculates the optimized cutting speeds. The present method was applied in a heavy machining industrial environment using actual production data and machining parameters. Taylor coefficients x and K were obtained through cutting tool life tests carried out on the part under analysis. Results showed potential machining cost savings of up to 6.2% compared to traditional methods for obtaining takt time on shop floor. [ABSTRACT FROM AUTHOR]

Surface grinding is critical for producing precise workpiece dimensions and surface finishes, especially for materials like cast iron and steel. Optimal grinding materials must reliably cling to magnetic chucks and resist melting or fouling the cutting wheel. Our investigation seeks to optimize the static and dynamic performance of surface grinder machines by evaluating their structural integrity and dynamics using finite element analysis (FEA). Modal and static studies are done under various tool post and column configurations to simulate real-world machining circumstances. Modal analysis reveals key resonant modes, with lowest frequencies 55, 61, and 85 Hz, respectively, seen when the column and tool post are centrally positioned. Modal analysis elucidates the natural frequencies affecting the machine's structure, aiding in resonance abatement during machining, and increasing machining precision. Static obtains a −500 N force along the Z-axis direction at the tooltip, alongside a gravity force of 9.8 m/s affecting all components. Investigation indicated substantial distortion at the junction of the grinding stone and primary tool, with a maximum deformation of 18.5 μm. Harmonic analysis, done within a frequency range of 55–235 Hz, corroborated the mode shape analysis results by exerting a −500 N force along the Z-axis. Spatial analysis was done to investigate the movement of the machine column and tool post during milling. Position C displayed the highest modal frequency 61, 67, and 95, respectively, due to the tool post's proximity to the working surface. This emphasizes the necessity of spatial optimization in machining. Experimental validation utilizing the predictive diagnostic performance system (PDPS) validates the differentiation between healthy and unhealthy machine signals during grinding, facilitating real-time issue identification. Predictive maintenance predicts machine issues, cuts downtime, boosts quality, and saves costs. This study attempts to find maximum deformation in machine tool structures under different configurations, ultimately increasing the structural properties and stability of surface grinding machines. [ABSTRACT FROM AUTHOR]

High-speed dry milling process for high-strength steel is widely used in the aerospace field, which can achieve good machining quality and accuracy while reducing the use of cutting fluid. However, unsuitable milling parameters can cause excessive vibration of the machine tool and even chattering of the milling system, which in turn affects the machining process. Thus, it is necessary to find the milling parameters boundaries of high-speed dry milling for high-strength steel. In this paper, based on the nonlinear milling force model, the regenerative effect and modal coupling are taken into account to establish a three-dimensional dynamic model for high-speed dry milling of high-strength steel, and the stability lobe diagram (SLD) is obtained by using the compound Simpson integration method (CoSIM), which is proved to be accurate and effective by numerical and experimental verification. The method fits using additional known time points and their responses, resulting in more accurate SLDs. Numerical validation results demonstrate that the method has higher prediction accuracy and convergence speed while maintaining higher computational rates. The study conducts experimental verification through multiple sets of high-speed dry milling experiments, demonstrating that the anticipated SLD findings are largely congruent with experimental outcomes. Consequently, the model and solution approach demonstrate significant potential in the selection of milling parameters. [ABSTRACT FROM AUTHOR]

Geometric error is a crucial factor influencing the spatial accuracy of CNC machine tools. A novel methodology for modeling, measuring, and identifying geometric errors in multi-axis machine tools is proposed in this paper. Firstly, a synthetic volumetric error model is established by utilizing dual quaternions for multi-axis CNC machine tools. To characterize the relative position relationship between the tool and the workpiece, the kinematics model automatically incorporates the complex coupling between position and orientation motion in an implicit way, enabling a concise and compact representation of the kinematics. Secondly, measure path planning includes candidate measurement positions which are screened to obtain the optimal position group by using observation indices and the modified Detmax method. Thirdly, a parametric modeling method based on exponential cosine fitting is proposed for representing both angular and linear errors, and an improved sparrow search algorithm and nested parameter uncertainty optimization are established to process curve fitting–based optimization of the geometric error term. The fitting of the exponential cosine model is quantified with model uncertainty, and the nested uncertainty optimization method is employed to improve geometric error terms with a poor fitting effect. Finally, the effectiveness is demonstrated through experimental comparisons. The geometric error of a single axis has an average decreased of 69.7%, and the compensation rate of roundness driven by two-axis synchronization is an average of 68.7%. This method offers the advantage of quantifying the minimum optimal number of measurements and positions, improving the efficiency of parametric modeling. [ABSTRACT FROM AUTHOR]

Machining efficiency and accuracy are subjected to higher requirements due to the increasingly widespread application of sculptured surface parts in the fields of aerospace, transportation, and energy. However, the influence of geometric error, tool deflection, and other factors decreases the quality and reliability of CNC machined workpiece. Error compensation is a popular and effective way to improve the machining accuracy of the workpiece. A novel multi-stage error compensation methodology that combines model-based and data-driven approaches is proposed, which realizes the closed-loop quality control process of the sculptured surface by five-axis flank milling. The global process consists of two special stages. First, the compensated machining of stage I is completed by a flexible mirror compensation method after establishing the tool deflection prediction model, which is the major factor causing the geometric error of the machined part. Subsequently, based on the on-machine measurement (OMM) data, the surface is automatically reconstructed by mirror points in 3D software and the toolpath for stage II compensated machining is generated. Finally, the verification experiment on the five-axis machine tool demonstrates that the methodology introduced in this research can effectively enhance the machining accuracy of the workpiece. [ABSTRACT FROM AUTHOR]

As a machining tool with great potential, abrasive water jet (AWJ) has been used extensively in cutting many types of difficult-to-cut materials. Actually, using AWJ to trim workpieces is another important application. Unlike other traditional machining tools, which do not make much difference in machining surface through cutting or trimming, the AWJ, a soft knife, would lead to a different shape profile on the machining side in the trimming process other than cutting. To explore the trimming process of AWJ, a specific method of modeling the shape profile of the machining side trimmed by AWJ is proposed in this paper. Firstly, a criterion has been built to classify whether it is a cutting task or an edge trimming task. After that, a series of experiments have been carried out to obtain how the AWJ cutting parameters affect the trimming process. Based on the experimental results, the key parameters affecting the trimming process have been found. Further, a mathematical model for trimming process has been built and its effectiveness has been verified through actual trimming. With this model, the shape error of the machining side trimmed by AWJ can be further reduced by adjusting the compensation parameters. [ABSTRACT FROM AUTHOR]

The present research focuses on comprehensively evaluating energy consumption and other vital machining characteristics during the turning process, aiming to optimize efficiency while minimizing environmental impact. The experimental data is collected through a series of machining tests on EA1N railway axle steel under dry, minimum quantity lubrication (MQL), and cryogenic cooling conditions. Under these cutting conditions, the machinability criteria (energy consumption, tool wear, surface quality, chip morphology) of train wheel axle steel were tried to be improved. As a result, cryogenic cooling at constant cutting speed gave 40% and 53% better results in terms of energy consumption than MQL and dry environment, respectively. When the same situation was examined in terms of tool wear and surface quality, 10–18% and 8–14% gave better results, respectively. In other words, it is worthy to mention that the research findings not only benefit the manufacturing industry by optimizing resource utilization but also align with global efforts to promote environmentally conscious practices in the engineering and transportation sectors. [ABSTRACT FROM AUTHOR]

Despite numerous studies regarding tool life modeling, it is usually difficult to apply conventional models to industrial fields because of factors such as impractical data acquisition systems, the complexity of model construction, and low adaptability to varying cutting conditions and volumes. This paper introduces a novel tool life model based on the power consumption of a machine tool with respect to varying process parameters; it was hypothesized that the proportion of the power consumed in tool wear (i.e., dissipating the cutting edge) to total power remains constant, regardless of the cutting conditions. Cutting power consumption was measured and integrated with the cutting length in the milling of titanium alloy. The experimental results confirmed the validity of the hypothesis; tool life was successfully predicted based on the accumulated cutting power consumption values, and the effects of variations in cutting volumes were compensated for based on cutting power consumption. The developed model was validated in a case with various feeds and cutting volumes mimicking a field case. Furthermore, the model was able to cope with unexpected incidents, such as a non-continuous coolant supply. Thus, the results demonstrate the applicability and practicality of using the integrated cutting power consumption value to predict tool life. Even in new machining environments such as with new tools and work materials, the developed model can easily be constructed with a small number of experiments to justify the model coefficients. Power consumption is expected to be more widely utilized for process monitoring in industry and as a health indicator based on its excellent performance and practicality. [ABSTRACT FROM AUTHOR]

The amplitude of tooth passing frequency in frequency spectrum is an important indicator to judge machining quality and tool wear. In comparison to the amplitude of tooth passing frequency in acceleration spectrum, broaching force more directly reflects the tool wear and machining quality, but the broaching force measurement equipment is expensive and can change the dynamic structure of the machine. Therefore, this paper establishes a frequency spectrum prediction model of the broaching process using theoretical cutting force time history. This model combines the advantages of easy acquisition of vibration data, low cost, and no change in machine structure by the acceleration sensor, with the benefits of directly reflecting tool wear and machining quality through broaching force measurement equipment. In this paper, the theoretical cutting force model is developed based on the Johnson–Cook material law. Compared to the traditional method of directly applying FFT to obtain the broaching force spectrum, this paper innovatively divides the broaching force into square waves of equal width but varying amplitudes in the time domain. The frequency domain waveform of broaching force can be regarded as the vector superposition between the harmonic series corresponding to these time-domain square waves. Through the waveform image of numerical simulation, it can be concluded that the amplitude of tooth passing frequency used for tool wear diagnosis depends on the shape of tooth passing area in cutting force time history, which is affected by factors like tool wear and lubrication conditions. Any force changes caused by tool wear and broaching quality can be reflected in the broaching force spectrum. According to the characteristics of the dynamic equation, the frequency spectrum of acceleration can be obtained by substituting the broaching force in frequency domain into the dynamic equation. Through experimental analysis, it is shown that the numerical acceleration frequency spectrum obtained by the present model is consistent with the experimental acceleration frequency spectrum. [ABSTRACT FROM AUTHOR]

Cemented carbide has been widely applied in the manufacturing industry due to the excellent wear resistance and corrosion resistance. As a typical difficult-to-machine material, it faces many challenges such as surface damage and tool wear in traditional machining. Therefore, many non-traditional machining methods based on energy fields have been gradually developed to machine cemented carbide and attract the increasing attentions. The purpose of this review is to offer the latest technological developments in various non-traditional machining processes and the hybrid machining methods of cemented carbide. According to the existing researches, the non-traditional machining methods based on thermal or chemical process exhibit some disadvantages in high efficient and precision machining of cemented carbide; the multi-energy field hybrid machining methods are developed to make up its disadvantages. However, in the multi-energy field hybrid machining, the coupling mechanisms of multiple energies and high-quality machining under multiple energy matching have not formed a unified and complete theoretical system. The development of these methods should be focused on interaction mechanism, energy transmission, and new machining tools. [ABSTRACT FROM AUTHOR]

The ball screw is a vital component in the feed drive systems of machine tools. It is susceptible to thermal errors that significantly impact its accuracy. Current thermal error modeling methods for ball screws face significant challenges in achieving full-time series prediction. Furthermore, these methods impose stringent requirements for a complex temperature data collection process, further constrained by the compact structure of machine tools. Additionally, valuable working condition data in thermal error prediction remains underutilized. This paper proposes a new hybrid-driven model that combines mechanism and data-driven approaches to achieve full-time series thermal error prediction of ball screws. The proposed model utilizes the operating rotational speed as a key input parameter, eliminating the need for temperature collection during the modeling stage and the compensation process. The temperature model is proposed as a mechanism-driven model based on heat transfer theory to calculate the temperature of the thermal sensitive points by utilizing operating rotational speed. The accuracy of the model is validated through thermal characteristic experiments of ball screws under four different working conditions. The data-driven models based on different traditional neural networks are established to predict thermal errors according to the time series temperature data from the temperature model. Moreover, hyperparameters of different neural networks are optimized by the Beetle Antennae Search (BAS). Comparative analysis among different neural network-based hybrid-driven models reveals that the convolutional neural network (CNN) model optimized by BAS consistently exhibits lower absolute errors predominantly below 10 μm, as well as lower root mean squared error (RMSE) and mean absolute error (MAE) values in each working condition. The BAS-CNN model, within the hybrid-driven model framework, is better suited for the full-time series prediction of thermal errors in ball screws. The BAS-CNN model is a foundation for thermal error compensation by utilizing working condition data. [ABSTRACT FROM AUTHOR]

Free-form surfaces generated by non-uniform rational B-splines (NURBS) are evolving to face turbomachinery component requirements, such as turbine blades to enhanced efficiency. Super abrasive machining (SAM) is presented as a potential process for high-added value components using custom-shaped tools to be adapted to any surface. The adaptability and flexibility of these tool concepts are specifically designed to fit these complex surfaces. This paper presents an innovative manufacturing approach for blade type components using a custom-shaped tool designed through an optimization process that simultaneously optimizes both the shape of the tool and its motion. The proposed method with SAM finishing using a custom-shaped tool is compared against a standard tool and traditional machining process. The result obtained on the blade test case shows that the custom-shaped tools need fewer paths, yet produce more accurate surface finish. [ABSTRACT FROM AUTHOR]

The intelligent service of the digital twin machine tool provides convenience for the human operation interaction in the machine tool, and the real-time operation interaction between the human and the machine tool has high requirements for the real-time machining model simulation algorithm. Firstly, this paper proposes a simulation method based on the combination of augmented reality (AR) and digital twin of machine tool machining virtual-real fusion. Secondly, to improve the real-time interoperability between human and machine tools in the AR virtual-real fusion machining process, this paper proposes a fast Dexel model generation method based on binary tree space segmentation. The method is based on the 3D model of the workpiece preprocessing to generate a one-way Dexel model of the binary tree storage structure, using the tool and the workpiece overlap envelope in the binary tree structure to determine the interference region, and ultimately calculating the intersection line between the one-way generation line and the tool geometry to get the model of the workpiece after cutting. By analyzing the real-time performance of the algorithm, the algorithm satisfies the simulation calculation of Dexel models of different scales. Finally, through the example of real-time interactive operation between human and machine tool AR, the results show that the average display frame rate of this algorithm in the machining process reaches 55–60 frames, and the parameter error between the model after virtual machining and the actual machining model is within 1.3%. At the same time, 100 people were randomly selected to carry out AR interaction training, and the real-time performance experience of AR virtual-real fusion machining was comprehensively evaluated, and the results showed that the system can meet the real-time demand of interaction operation of most participants. [ABSTRACT FROM AUTHOR]

The assessment of production plant efficiency is crucial for optimizing the operational performance of manufacturing systems. In traditional facilities, automated data collection is limited and information primarily relies on operators declarations, which are prone to inaccuracy. There is therefore a need for readily accessible digital alternatives. This paper introduces a cost-effective method for classifying the status of machine tools using smart sensors to monitor their primary doors with minimal integration, and a streamlined algorithm for efficient data processing. The innovative algorithm was conceived using data collected in over 3 months in a manufacturing plant comprising 50 diverse machine tools engaged in batch production for the automotive industry, and is based on non-dimensional thresholds, making it suitable for generic applications requiring classification of repetitive patterns. Also, a realistic simulator was developed to provide reliable data for algorithm accuracy evaluation. The classification performance was fully tested using synthetic data, showing very good accuracy. In addition, the performance of the algorithm was compared to basic machine learning approaches further proving the validity of the proposed method. Ultimately, the classification algorithm was employed to assess the Overall Equipment Effectiveness (OEE) of the real plant machines, which were closely aligned with the estimates provided by the enterprise management. [ABSTRACT FROM AUTHOR]

Rotary axes of five-axis machine tools have highly coupled geometric errors, which are crucial factors affecting the machining accuracy. In this study, a new feature workpiece is designed, including two features of arc surface and square groove. Based on the on-machine measurement of the workpiece, five position-independent geometric errors of the two rotary axes can be identified. The cutting and measuring steps of the workpiece are described in detail, the identification principle of each error is revealed, and the uncertainty analysis is carried out. During the identification process, the machining domain and measurement domain of the same feature remain unchanged to eliminate the influence of linear axis errors on the identification results; hence, the identification accuracy is improved. In the experimental verification, the result is 90.8% consistent with the ball-bar method, which verifies the feasibility of this method. [ABSTRACT FROM AUTHOR]

Two rotating axes of the five-axis machine tools complicate the kinematics, which increases the difficulty of trajectory planning of five-axis CNC machining. In the process of machining a free-form surface with a ball-end cutter, the tool is required to follow the tool tip path of the surface and the restriction of tool orientation is only constrained within a certain range. This paper proposes a planning algorithm based on differential vector optimization for generating a smooth trajectory of each axis for five-axis machining. Firstly, the kinematic model of the five-axis CNC machine tool and the Jacobian matrix are built. Secondly, the optimization objectives combined with the smoothness optimization requirements and the limits of the tool axis vector are established. Then, the trajectory of the moving axis is generated by integrating the optimized differential vector. At last, a waveform surface machining process is simulated in the VERICUT software with the trajectory generated by the proposed optimization method. To prove the feasibility and superiority of the method, the real part machining experiment is conducted in an A-C type five-axis CNC machine. The experiments verify the smoothness and optimal of the proposed path planning method. [ABSTRACT FROM AUTHOR]

Currently, various industries are increasingly trending towards multi-axis machining and Industry 4.0. As a result, the demand for machining flexibility, speed, and precision in machine tools is also rising. Different manufacturers provide many components of machine tools, and the alignment precision, main structure, component rigidity, and dynamic performance of these components all impact the machining precision of multi-axis machine tools. Therefore, the structural design of each component is crucial. In this study, a roller cam rotary table is selected for analysis using the finite element method, considering the constraints of the machine environment. The investigation includes vibration mode shape, gravitational load, external loading, transient, spectral, and topology optimization inspection. The static analysis reveals weak points in the machine's structure. Modal analysis helps understand the natural frequencies that affect the machine's structure, resulting in mitigation of resonance during the machining process and enhancing machining precision. Finally, the modal impact test is conducted using accelerometers and an impact hammer whereas ME'scope curve fitting is employed to determine the mode shapes and natural frequencies. The results are compared to validate the accuracy of the analysis, with a deviation of 8.5%, 8.3%, and 1.4%. Topological optimization analysis is performed on machine components, aiming to reduce weight and improve natural frequencies as compared to the original design. Precision tests such as repeatability and segmentation accuracy are essential for rotary tables. These tests provide information about the errors at different angles, ensuring accuracy, reliability, and quality during the machining process. In this study, vibration signals from the rotary table are captured using a smart prediction diagnosis performance system (PDPS). An algorithm is used to analyze the normal vibration signals and establish a healthy model for continuous monitoring of the rotary table's health status. Principal Component Analysis (PCA) is applied to identify fault features and diagnose mechanical problems. It enables early detection of anomalies in the rotary table, reducing downtime and maintenance costs due to damage and improving the machine's efficiency. Amalgamation of computer-aided analysis, topology optimization, and intelligent diagnostics represents a paradigm shift in development and design technique, resulting in increased cost efficiency and reliability. This strategy reduces downtime by proactively addressing possible failures using real-time trend forecasting and maintenance scheduling, hence improving structural integrity and operating efficiency. [ABSTRACT FROM AUTHOR]

Volumetric error and dynamic error are the main error sources that lead to position deviation of the cutting tool relative to the workpiece in five-axis machine tools. To comprehensively evaluate the positioning performance of the five-axis machine tools, this paper presents a comprehensive approach to identify both the volumetric and dynamic errors. A group of circular trajectories for a double ball-bar device is designed and performed in the measurement procedure. The volumetric performance is firstly evaluated by calculating the shape deviation between the real and designed circular trajectories. Since geometric errors are the main error source of volumetric inaccuracy, a position-independent geometric error (also named as location error) is identified from the measurement data in this work. A least square method–based algorithm is formulated to calculate the squareness errors between the three linear axes and the location errors of the two rotary axes. To evaluate the dynamic performance, the tangential and normal deviations between the real and designed circular trajectories are monitored. Particularly, as the magnitude of dynamic error has a strong correlation with the feedrate, the circular tests are repeated at three different feedrates. The experimental study is conducted on a five-axis machine tool to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In comparison to coordinate measuring machine (CMM), on-machine measurement (OMM) offers the benefit of avoiding re-clamping of workpieces and has become a crucial technology for adaptive machining. In OMM, the positioning errors of machine tool shift the ideal position and orientation between the probe and the workpiece. This introduces an error in the measurement result, causing workers to question OMM's precision. Therefore, the clarification of the relationship between positioning error and OMM error is helpful to make this technique more reliable. For this purpose, a five-axis OMM error model considering positioning errors is proposed. First, this paper gives the definition of feasible measurement region (FMR) about five-axis machine tool and obtains the FMR under specified measurement feature. Then, in the interest of identifying the influence of machine tool positioning error on OMM accuracy, an OMM error prediction model considering positioning error is established, which can be used to predict the error distribution in FMR. Next, the validity of the prediction model is verified by OMM experiments on the standard block. Finally, several applications of the OMM error prediction model are proposed. [ABSTRACT FROM AUTHOR]

Wire arc additive manufacturing (WAAM) is one of the most productive metal additive manufacturing methods. One of its most promising applications holds in the manufacturing of difficult-to-cut materials where production costs can be reduced with minimizing the time of machining and total tool costs. To develop a correct WAAM, technological processes for manufacturing complex-shaped components welding torch path corrections and welding power corrections have to be made especially in critical sections such as corners and sharp edges. A predictive mathematical model of the material cladding during the WAAM process has been developed for the purposes of generating an optimal toolpath of the WAAM clads. This predictive mathematical model is simplified to reflect the important physical phenomena in the weld pool but also to optimize computing time. In this paper, the principle of the mathematical model is described, and its functionality is verified by the welding experiments with five different welding power settings. For the initial calibration of the model parameters single straight-line weld clads with 5 different welding power settings (wire feeds) ranging from 5.0 to 8.6 m/min were investigated. 3D scans of these welded samples are used for the verification. With the calibrated simulation model, it was possible to predict the precise shape with a maximum deviation circa 0.20 mm. The start portions of the weld clads seem more complex having the deviation circa 0.30 mm. These are valuable results as the WAAM technology is generally considered to be reasonably rough. [ABSTRACT FROM AUTHOR]

The assembly of remanufactured machine tools is crucial for ensuring their accuracy. Poor assembly accuracy, low remanufacturing resource utilization, and inefficient assembly have become common issues in remanufacturing processes. This limitation is addressed in the study, wherein Taguchi's mass loss function (QLF) and a cost function for the remaining parts are employed. Both assembly accuracy and minimum cost are ensured through the establishment of a comprehensive matching model with closed-loop dimensional chains as constraints. The solution process of the PSO-GA model is outlined, effectively reducing uncertainty and resource waste associated with impractical matching schemes. Practical results from the CAK6163 headstock demonstrate that not only is the low success rate issue in matching remanufactured parts resolved by the proposed method, but resource utilization is also significantly enhanced, and costs are reduced. [ABSTRACT FROM AUTHOR]

The thermal error in the spindle unit is substantial and necessitates mitigation. Current models, being predominantly static in nature, have limited efficacy in error control. Integrating digital twin technology for modeling and controlling spindle unit thermal error holds promise in enhancing the machining accuracy of machine tools. Yet, the notion of a digital twin system specifically tailored for spindle unit thermal characteristics remains uncharted territory. To navigate these challenges, this study introduces a novel digital twin system tailored for spindle unit thermal characteristics. This system is poised to revolutionize thermal error modeling and compensation by harnessing the capabilities of digital twin technology. Within this digital twin framework, both the thermal error control model and the analytical thermal characteristic model are seamlessly integrated. The control model is devised as an exponential function, utilizing operational time, inherent time constants, and both initial and equilibrium thermal errors as parameters. Delving deeper, the analytical thermal characteristic model for the spindle system is rooted in a thermal resistance network approach. This leads to a closed-loop thermal characteristic modeling process, culminating in the derivation of a steady-state thermal error. Intricate heat transfer dynamics between spindle components are dissected, and a comprehensive thermal equilibrium equation set is formulated for the spindle unit. This equation set comprehensively accounts for dynamic variations in key parameters such as preload, lubricant viscosity, thermal load intensity, thermal contact resistance, and convective coefficients. To ascertain the time constant, a meticulously designed set of thermal characteristic experiments is executed. Subsequently, the digital twin system embarks on predictive modeling of thermal errors across varied operational conditions. This prediction then forms the foundation for thermal error compensation. With the integration of the present model into the digital twin system, the results are impressive: the absolute average and maximum deviations in thermal elongation, post-error control, stand at approximately 0.40 μm and 1.24 μm, respectively. [ABSTRACT FROM AUTHOR]

Accurate tool wear prediction is of great significance to improve production efficiency, ensure product quality and reduce machining cost. This paper proposes a hybrid physics data-driven model-based fusion framework for tool wear prediction to improve low prediction accuracy of physical model and poor interpretation of data-driven model. In this framework, physical information and local features of sensor measurement signals are used as inputs to build a hybrid physics data-driven (HPDD) model. And data mining and physics principles are effectively integrated by using unlabeled samples for data expansion. Piecewise prediction is introduced to reduce difficulty in parameter estimation. Then, in order to manage prediction uncertainty of physical information and HPDD method, two prediction results are gradually combined based on Bayesian fusion mechanism to eliminate prediction error. Finally, the effectiveness of the proposed method is verified by experiment. Compared with existing methods, this method significantly improves prediction. The mean values of root mean square error (RMSE) and mean relative error (MARE) for tool wear prediction results are respectively 2.28 and 1.85. [ABSTRACT FROM AUTHOR]

In this paper, the geometric error modeling and compensation methods of macro–micro composite five-axis turn-milling composite machining center is studied. Firstly, the machine topological structure of the branches, intermediates, and terminal bodies of the macro–micro composite multi-body system is analyzed. Then, the tool chain transformation matrix and the end position of the workpiece chain are established to obtain the geometric error model. Based on the error compensation theory of traditional machine tool structure, the compensation mechanism of macro-level compensation and micro-level sub-micron compensation is proposed. Then, the compensation model of micro-axis error is given. Furthermore, the macro–micro composite error compensation experiment is setup; the laser interferometer is used to judge the positioning accuracy and straightness before and after compensation. The results show that the accuracy of the micro-motion platform after compensation reaches the sub-micron level, which verifies the compensation method, and the machining accuracy of the micron level is achieved through the cutting experiment. [ABSTRACT FROM AUTHOR]

The thermal error is a significant factor that influences the machining accuracy of machine tools, and error compensation is an economical and effective method for enhancing the accuracy of machine tools. However, establishing a precise thermal error prediction model is crucial for thermal error compensation. In this paper, the subtraction-average-based optimizer-based CNN-GRU neural network (SABO-CNN-GRU) is applied to integrated thermal error modeling. Through conducting a thermal characteristic experiment, temperature rise data and thermal error data were collected from the linear feed system of LXK300X helical groove CNC machine tool. The fuzzy c-means clustering and grey correlation analysis are employed to identify temperature-sensitive points in the linear feed system. By utilizing the temperature rise data from these sensitive points along with feed shaft thermal errors as data samples, and using the SABO algorithm to optimize the CNN-GRU prediction model, the thermal error prediction model of SABO-CNN-GRU is established. To validate its superiority and practicality, a comparative analysis is conducted with traditional thermal error prediction models based on CNN-GRU and SO-ELM. The results demonstrate that SABO-CNN-GRU model outperforms both models in terms of mean absolute error (MAE), root mean square error (RMSE), remaining prediction deviation (RPD), mean square error (MSE), and determination coefficient (R2) in accurately predicting results. Building upon this achievement, this paper develops a real-time thermal error compensation system which effectively reduces maximum thermal errors from 80.5 to 17.6 μm after implementing compensation measures. Effectively reducing the influence of thermal errors and improving the machining accuracy of machine tools. [ABSTRACT FROM AUTHOR]

In the machining of aero-engine gearbox ring-shaped parts, deterministic positioning is often achieved by general fixtures combined with manual alignment due to various sources of error. However, manual intervention leads to discontinuous and inefficient production. To improve this situation, workpiece alignment is centralized on alignment table equipment, and then the workpieces are transferred to the machine tools by robots for processing. In this case, the accuracy of clamping and positioning on the alignment table is extremely important. In this paper, a simulated model is constructed to predict workpiece clamping and positioning accuracy error distribution by combining manual alignment and the GD&T of process system. Firstly, an error transfer chain for the non-deterministic positioning alignment table is constructed based on assembly layers and features. Secondly, the alignment process is modeled and integrated into the established chain. Through the update of the error transfer chain, the deterministic clamping and positioning model are developed. The model shows excellent accuracy by comparing simulated results with the experimental measurements. Furthermore, an experimental scheme is designed to evaluate the influence of different types of alignment on the positioning accuracy of clamping in the assembly system. Through this experimental evaluation, the key alignment error factors affecting positioning accuracy are identified, and a quantitative metric is provided to assess the impact of workpiece and equipment alignment on the precision of workpiece fixturing and positioning. Based on these findings, the optimization suggestions for tolerance design of alignment are provided. [ABSTRACT FROM AUTHOR]