Tool Runout Research Articles

Low-frequency chatter suppression for robotic milling using a novel MRF absorber

Robotic milling has a unique advantage for large and complex parts. However, it is extremely prone to low-frequency chatter due to the robot structure mode. In robotic milling, low-frequency chatter has a huge impact on machining quality and efficiency. In this paper, a novel MRF (Magnetorheological fluid) absorber is used to suppress low-frequency chatter during robotic milling based on the proposed low-frequency chatter suppression strategy. First, the GPR (Gaussian process regression) model is used to predict the impulse response function based on a few measured impulse response functions. Next, a cutting force model is developed considering the tool runout and the robot structure mode. The parameters and coefficients of the cutting force model are optimized by the measured cutting forces. Then, the low-frequency chatter frequency is predicted considering the modal coupling effect based on the predicted impulse response function and the simulated cutting force. Finally, an MRF absorber is designed based on the chatter frequency range. The controlled storage modulus of the MRF in the pre-yield region enables a wider frequency shift characteristic of the designed MRF absorber. The MRF absorber controls the input current based on the predicted low-frequency chatter frequency to achieve chatter suppression during robotic milling. Robotic milling experiments verified that the MRF absorber can effectively suppress low-frequency chatter. The experimental results show that the Energy Ratio Chatter Indicator of the industrial robot with MRF absorber is less than 0.1 under different milling conditions.

Forecasting the cutting force in end milling

The object of this study is the process of end milling, taking into account the discontinuity of the process, simultaneous cutting with several flutes arranged in a spiral, tool runout, and feedback in the elastic machining system, in particular, for the depth of cutting. The subject of the study is the cutting force and identification of its empirical model. During identification, the cutting force coefficient is automatically determined when matching the theoretical and experimental oscillograms of the cutting force component. The reported results related to forecasting the cutting force at end milling are based on a mechanistic approach and involve the process modeling method for forecasting. The simulation uses an algorithm for representing the interaction of the cutter flutes workpiece engagement, based on the scan of the cutter according to the rotation angle coordinate. The algorithm makes it possible to identify empirical coefficients and exponents of the cutting force model based on experimental oscillograms of cutting force components. The built model is implemented in an application program and owing to the representation of the machining system in the form of a closed structural diagram, it allows predicting the elastic displacement, which will determine the actual cutting depth. The developed program under an interactive mode using digital files of experimental cutting force components makes it possible to perform model identification and predict cutting force components with an error of 4.6 %. The adequacy of the algorithms was confirmed by measuring the profile of the machined surface in the places where the cutting mode changed with the feed stopped. The developed simulation algorithm makes it possible to take into account the simultaneous cutting by several flutes arranged in a spiral, the runout of the tool, and the feedback in the elastic machining system, in particular, the depth of cutting

Modeling and reliability analysis of the micro-milling process considering stochastic tool wear with surrogate models

Micro-milling technology is widely employed in manufacturing micro-precision parts due to its flexible processing and high accuracy. Compared to the macro-milling process, micro-milling has a smaller machining size, which leads to tool wear, tool runout, and other factors being more sensitive to the impact of machining quality. This work proposes a reliability evaluation framework for machining systems based on the micro-milling mechanical model and its surrogate method. Establish a cutting force model under shear and plow conditions with periodic variations in instantaneous uncut thickness. Subsequently, the probability distribution of the cutting parameters is obtained based on Bayesian updates. An improved buffer failure probability method is proposed to quickly get the micro-milling system failure probability. Developing new ensemble and adaptive sampling methods and introducing stochastic configuration network (SCN), an adaptive stochastic configuration network ensemble (ASCNE) model is established to alleviate the time-consuming problem of repeated calculations of mechanical models in reliability analysis. Experimental results indicate that the mechanical model can achieve good predictive performance, and the constructed ASCNE model can achieve high-precision surrogate effects. Additionally, the reliability analysis results of the system can guide tool replacement during the machining process, ensuring safe and efficient execution of the micro-milling.

Prediction of surface topography for the five-axis bull-nose end milling of directional plexiglass considering tool runout and dynamic displacement

Prediction of surface topography for the five-axis bull-nose end milling of directional plexiglass considering tool runout and dynamic displacement

Time-varying dynamic modeling of micro-milling considering tool wear and the process parameter identification

Micro-milling technology is widely used in the manufacturing of micro three-dimensional complex parts in aerospace, mechanical equipment, biomedical devices, electronic components, and other fields due to its advantages of high machining accuracy, small size, and complex surface. Consequently, understanding its machining process is essential. This study proposes a time-varying dynamic micro-milling process model with the effects of tool runout, tool wear, and cutting status and explores the stable cutting domain of real-time machining. Considering the time-varying characteristics of the cutting process, combined with measured data and particle filtering method, the cutting force coefficients and tool wear parameters in the dynamic model at different times are identified. The dynamic equation of micro-milling is derived using a discrete method, and the stability of the cutting process is evaluated. Multiple cutting experiments are conducted on the Al6061, and the test data are compared with simulated values. The comparison results show that the accuracy of the proposed model in predicting the cutting state can reach 100%, and the average error of cutting force prediction is 7.72%. The research results can provide theoretical guidance for the safety evaluation of micro-milling and the selection of machining conditions.

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis.

Micro-machining is a widespread finishing process for fabricating accurate parts as biomedical devices. The continuous effort in reducing the gap between the micro- and macro-domains is connected to the transition from conventional to micro-scale machining. This process generates several undesired issues, which complicate the process's optimization, and tool run-out is one of the most difficult phenomena to experimentally investigate. This work focuses on its analytical description; in particular, a new method to calibrate the model parameters based on cutting force signal elaboration is described. Today, run-out prevision requires time-consuming geometrical measurements, and the main aim of our innovative model is to make the analysis completely free from dimensional measurements. The procedure was tested on data extrapolated from the micro-machining of additively manufactured AlSi10Mg specimens. The strategy appears promising because it is built on a strong mathematical basis, and it may be developed in further studies.

Dynamic Modeling for Chatter Analysis in Micro-Milling by Integrating Effects of Centrifugal Force, Gyroscopic Moment, and Tool Runout.

During micro-milling, regenerative chatter will decrease the machining accuracy, destabilize the micro-milling process, shorten the life of the micro-mill, and increase machining failures. Establishing a mathematical model of chatter vibration is essential to suppressing the adverse impact of chatter. The mathematical model must include the dynamic motions of the cutting system with the spindle-holder-tool assembly and tool runout. In this study, an integrated model was developed by considering the centrifugal force induced by rotational speeds, the gyroscopic effect introduced by high speeds, and the tool runout caused by uncertain factors. The tool-tip frequency-response functions (FRFs) obtained by theoretical calculations and the results predicted by simulation experiments were compared to verify the developed model. And stability lobe diagrams (SLDs) and time-domain responses are depicted and analyzed. Furthermore, experiments on tool-tip FRFs and micro-milling were conducted. The results validate the effectiveness of the integrated model, which can calculate the tool-tip FRFs, SLDs, and time responses to analyze chatter stability by considering the centrifugal force, gyroscopic effect, and tool runout.

Physics-guided intelligent system for cutting force estimation in ultrasonic atomization-assisted micro-milling of porous titanium

Porous titanium is a new biomechanical implant material due to the good mechanical properties and biocompatibility. In order to meet the manufacturing needs of anisotropic porous titanium with complex micro-structures, the ultrasonic atomization with excellent cooling and lubrication effect is integrated with the micro-milling operation. Accurate prediction of cutting forces in the machining process is a principal factor as it exerts indispensable influence on the process efficiency and surface integrity. The prediction power of the traditional physics-based mechanistic cutting force model has been limited by the explicit relationship between force components and process variables based on the cutting mechanism and process geometries. In this paper, a comprehensive physics-guided intelligent system for stochastic cutting forces estimation is developed in the ultrasonic atomization-assisted micro-milling operation of porous titanium. The physics-based mechanistic cutting force model is proposed based on the determination of in-process random porosity of machined porous titanium with tool-workpiece contact. The influence of process uncertainty related to size effect and tool run-out is considered in the proposed physics-based mechanistic model. To overcome the poor nonlinear fitting and indeterminate cutting constants with numerous experiments, the machine learning algorithm has been merged into the physics-based mechanistic model to establish the physics-guided intelligent system of stochastic cutting force prediction. The predicted cutting force values of the proposed comprehensive physics-guided machine learning system are validated by the measured results with a wide variety of ultrasonic atomization-assisted micro-milling tests.

Investigation of the tool flank wear influence on cutter-workpiece engagement and cutting force in micro milling processes

Cutting force modeling is essential for comprehending the dynamic mechanics of the micro-milling process. Accurate calculation of the cutter-workpiece engagement is crucial for predicting cutting force accurately. This study proposes an accurate analytical model of cutter-workpiece engagement that comprehensively incorporates tool flank wear, tool edge radius, and tool runout for predicting cutting forces in the micro-milling process. Based on the actual tool radius model, which takes into account tool flank wear and tool edge radius, a fast real-time online method for accurately calculating the entry and exit angles is presented. This method involves the trochoidal trajectories of the current cutting edge and all passing cutting edges from the previous cycle under the influence of tool runout. Then the cutter-workpiece engagement is determined. Based on this, the cutting force is predicted combined with instantaneous uncut chip thickness and cutting force coefficients considering tool flank wear and tool runout. Through micro-end milling experiments, the validity of the cutting force model is confirmed. The statistical analysis of the predicted cutting forces further demonstrates the effectiveness of accounting for the impact of tool flank wear on the cutting forces throughout the entire micro-milling process. Through case analysis, it is concluded that tool flank wear leads to increased cutter-workpiece engagement and significantly affects the cutting forces in three directions. This work could offer a theoretical foundation and practical guidance for predicting tool life and controlling surface quality during the machining process.

Investigation of the Effects of CNC Tool Runout on Machining of 1.2379 Steel and Tool Life

Investigation of the Effects of CNC Tool Runout on Machining of 1.2379 Steel and Tool Life

Tooth-wise monitoring of the asymmetrical tool wear in micro-milling based on the chip thickness reconstruction and cutting force signal

Tooth-wise monitoring of the tool wear condition is of great importance for improving the machining quality and efficiency of micro-milling. However, the uncut chip thickness, cutting force, and tool wear condition of the multi-tooth evolve asymmetrically coupled due to the tool runout, and it is difficult to monitor the tool wear of each cutting tooth solely based on the cutting force signal. The reconstruction of chip thickness is the key to overcoming this challenge. With such consideration, this study proposes a tooth-wise monitoring method for estimating the asymmetrical micro-milling tool wear based on the chip thickness reconstruction and cutting force signal. The instantaneous uncut chip thickness of each tooth is reconstructed from the floor surface topography of the machined material with the guide of the proposed surface topography model considering the asymmetrical tool wear. The wear condition of each tooth is estimated by integrating the reconstructed chip thickness and the measured cutting force signal into the proposed asymmetrical micro-milling force model. Experimental results show that the asymmetrical models outperform the traditional symmetrical models in terms of modeling flexibility and accuracy. With the consideration of the asymmetrical tool wear, the prediction accuracy of the proposed topography and cutting force models increases by 11.4% and 5.3%. The relative monitoring error within 9% demonstrates that the proposed method can accurately estimate the tool wear condition of each cutting tooth in micro-milling.

Prediction of milling force based on identified milling force coefficients and tool runout parameters in time-frequency domain

Milling force is the key variable related to machining performance. Mechanistic models are commonly used to predict milling forces. However, the identification process of milling force coefficients and tool runout parameters in this model is prone to local optimal solutions. This paper develops a time–frequency domain identification method, and then the prediction of milling force is implemented on this basis. First, a milling force model considering the influence of tool runout is established, where the milling force coefficients and tool runout parameters are identified based on the time–frequency domain identification method. The simulation and experimental data are used to compare the time–frequency domain identification method with the time-domain and frequency-domain identification methods, respectively. Then, the variation of milling force coefficients and tool runout parameters with milling conditions is studied, based on which the linear interpolation method of coefficients and parameters is used to predict the milling force. Finally, the effectiveness of the milling force prediction is verified by experiments.

Analysis and modeling of cutting force considering the tool runout effect in longitudinal-torsional ultrasonic vibration-assisted 5 axis ball end milling

The longitudinal-torsional ultrasonic vibration-assisted milling (LTUVAM) can improve the machining quality compared with conventional milling (CM), and the machining quality is closely related to cutting force which is affected by the tool runout effect. In this study, a mechanistic force model for longitudinal-torsional ultrasonic vibration-assisted 5 axis ball end milling considering the tool runout effect is proposed. First, the cutting edge motion model considering the effects of tool runout and ultrasonic vibration is established. Then, based on the analysis of the cutting edge motion relative to the workpiece, the uncut chip thickness (UCT) is computed by means of the geometrical calculation method, and the engaged cutting edge elements are determined according to the Z-map method, the ultrasonic milling force model is established after determining the cutting force coefficients and tool runout parameters through calibration tests. Moreover, the impact of different parameters on dynamic separating characteristics of LTUVAM is studied by the tool-workpiece contact rate. Finally, the verification tests are carried out, and the experimental results show the effectiveness of the ultrasonic milling force model.

Robust tool condition monitoring in Ti6Al4V milling based on specific force coefficients and growing self-organizing maps

Tool condition monitoring (TCM) is a mean to optimize production systems trying to use cutting tool life at its best. Nevertheless, nowadays available TCM algorithms typically lack robustness in order to be consistently applied in industrial scenarios. In this paper, an unsupervised artificial intelligence technique, based on Growing Self-Organizing Maps (GSOM), is presented in synergy with real-time specific force coefficients (SFC) estimation through the regression of instantaneous cutting forces. The conceived approach allows robustly mapping the SFC, exploiting process parameters and similarity to manage the variability of their estimation due to unmodelled phenomena, like machine dynamics and tool run-out. The devised approach allowed detecting the tool end-of-life in cutting tests with variable lubrication, machine tool and cutting speed, through the adoption of a self-starting control chart running on real-time clustered data. The solution was validated through the comparison of the GSOM framework with respect to the optimized self-starting control chart applied without GSOM clustering. The GSOM reached a root mean squared percentage error (RMSPE) of 13.2% with respect to 56.1% obtained with the analogous control chart in a full-set optimization scenario. When optimised on tests for a unique machine tool and tested on another machine tool, GSOM scored an RMSPE of 34.5%, whereas the optimized control chart scored 64.5%.

Dynamic stability simulation of micro-milling under the condition of multi-parameter uncertainty

A stable processing state is the continuous pursuit of manufacturing process engineering. The premise of predicting the cutting stability boundary lies in understanding the dynamic mechanism of the machining process. In addition, parameter uncertainty and measurement errors may lead to inconsistency between the simulated and the test value. In this paper, a novel dynamic micro-milling system framework is developed, which takes into account multiple parameter uncertainties, including the tool runout, the coupling effect, the size effect, the process damping, and the distribution of the cutting coefficients and model parameters. The dynamic mechanical models of the shearing and ploughing dominant regions are established, and the state transition matrix is constructed using a discrete method. The prior probability information of relevant machining parameters is obtained based on the reception coupling theory, error convergence method, and experimental data. The accuracy of the posterior parameters under reliability updates is verified by combining the experimental results, the Adaptive Bayesian Updating based on the Structural reliability analysis method, and the presented mechanical model. The proposed dynamic framework can be applied universally for the steady-state analysis of the practical machining process.

Real-time reliability analysis of micro-milling processes considering the effects of tool wear

The development of micro-milling technology has promoted the high-precision machining of complex and small-size parts in modern manufacturing. Accurate cutting force control and reasonable machining parameters selection are conducive to the sustainable machining of micro-milling. In this work, a micro-milling mechanical model considering the change of tool wear, tool runout, and chip separation status with cutting time is proposed with the improved Gated Recurrent Unit with Multi-objective Dandelion Optimizer and mathematical modeling. The modeling accuracy is verified by experiments on Al6061. Combined with the established framework and the practical physical information, the posterior distribution of process parameters is inverted using the Bayesian updating method. Finally, the reliability calculation of machining system parameters is efficiently realized utilizing the posterior information and the High-dimensional Model Representation with the Stochastic configuration network method. The influence degree of the change of cutting parameters on the extreme value of micro-milling force is evaluated. The research results can provide analytical methods and theoretical guidance to ensure safe processing.

Modelling and analysis of the micro-milling of micro-dimpled structures with copying method on glow discharge polymer (GDP)

The surface with micro-dimpled structures on the target ball is helpful for improving nuclear fusion efficiency in inertial confinement fusion. Micro ball-end milling with copying method can provide a simple and effective option in machining micro dimples with high surface finish and geometry (dimension and shape) accuracy. In this paper, based on the established geometric error model, the influence of geometric errors and structural parameters of the machine tool on the dimple dimension and shape was first studied. Next, a model expressing the trajectory of cutting edge for simulating dimple geometry generated by ball-end milling was built. The model was used to analyze the effect of the spindle attitude, tool run-out and deflection deformation on the dimple dimension and shape. Micro ball-end milling tests were carried out, and the dimension, shape and cross-section profile of simulated and milled dimples were compared. A good correspondence between the simulated and milled dimple was observed, the largest error is 6.87%. Finally, from surface morphologies and surface roughness of the milled dimples, it was found that spindle attitude, axial feed rate are the main factors affecting the surface quality of micro dimple machined using micro ball-end milling with copying method. In particular, the axial feed rate need being optimized, reducing the effect of cutting edge quality and avoiding the deterioration of dimple surface and contour caused by a high feed rate. This research is of great significance for fabricating micro-dimpled functional surfaces by surface texturing based on milling methods.

Online monitoring model of micro-milling force incorporating tool wear prediction process

In modern manufacturing, micro-milling technology plays an essential role in manufacturing high-precision and complex micro-size parts. Exploring the changing rule of time-varying cutting is of great significance for understanding the micro-milling mechanism and improving the machining efficiency. In addition, tool wear identification and updating in advance can enhance the accuracy and sustainability of micromachining. This paper presents a tool wear prediction framework for micro-milling by a temporal convolution network, bi-directional long short-term memory, and the multi-objective arithmetic optimization algorithm. Then, a new integrated model for real-time micro-milling cutting force monitoring is constructed, considering tool deformation, tool runout, time-varying cutting coefficient, chip separation state, and tool wear estimation results. Based on the micro-milling experiment with workpiece material Al6061, the accuracy of the proposed tool wear prediction and cutting force model is verified. The developed model can provide theoretical guidance for statics and dynamics analysis in the micro-milling.

Side-Milling-Force Model Considering Tool Runout and Workpiece Deformation

With the development of Industry 4.0, hard-cut materials such as titanium alloys have been widely used in the aerospace industry. However, due to the poor rigidity of titanium alloy parts, deformation and vibration easily occur during the cutting process, which affects the accuracy, surface quality and efficiency of part machining. Therefore, in this paper, tool runout and workpiece deformation are introduced into the milling process of flat-end mills. Based on the tool’s hypocycloid motion, a geometric parameter model of the milling process is established, and the undeformed cutting thickness model is obtained considering the tool runout and workpiece deformation. Finally, the milling force model for side-milling titanium alloy thin-walled parts was established. The accuracy of the force model is verified through experiments. The error of the proposed model is far less than that of the traditional basic method. The maximum error of the traditional basic method is 87.09%. However, the maximum error of the proposed model is only 66.54%. The results show that the proposed force model considering tool runout and workpiece deformation can provide more accurate milling force prediction.

Using micro-milled surface topography and force measurements to identify tool runout and mechanistic model coefficients

Using micro-milled surface topography and force measurements to identify tool runout and mechanistic model coefficients