Micro-milling Force Model Research Articles

Minimum cutting thickness prediction model for micro-milling machining and experimental Study of FeCoNiCrMn high-entropy alloy machining

FeCoNiCrMn high entropy alloy has excellent properties such as high hardness, high strength and high wear resistance, which has a broad application prospect in aerospace, military and other fields. As an emerging difficult-to-machine material, the minimum cutting thickness determines the highest machining accuracy of micro-milling. In order to improve the micro-milling quality of FeCoNiCrMn high-entropy alloy, a minimum cutting thickness prediction model based on the effective front angle of the tool is established according to the analysis of micro-milling force model. Through the FeCoNiCrMn high-entropy alloy micro-milling processing experiments, the milling force parameters are calculated by substituting them into the prediction model, and the minimum cutting thickness value of FeCoNiCrMn high-entropy alloy is 1.367 μm when the flank radius of the milling cutter is 5 μm.When the feed rate of each tooth is in the range of 1 μm–1.5 μm, the severe size effect and the ploughing effect lead to the specific cutting energy nonlinearly increase, and the cutting force and surface roughness increase sharply with large fluctuations. From this, the range of the minimum cutting thickness of the high-entropy alloy was judged to be between 1 μm and 1.5 μm, and the accuracy of this prediction model was verified.

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.

Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear

Aluminum alloy LF 21 has a strong ability to reflect electromagnetic waves. LF 21 waveguide slit array structure is widely used in waveguide radar antenna. The stiffness of the slit array structure is relatively weak. So, the structure is prone to deformation under the cutting force in the conventional milling process. Micro-milling technology can realize high-precision machining of mesoscale parts/structures and is a potential effective machining technology for the waveguide slit array structure. However, the diameter of the micro-milling cutter is small, and the feed per tooth is comparable to the arc radius of the cutting edge, so the micro-milling cutter is prone to wear. In addition, the effects of elastic recovery of material, the minimum cutting thickness and friction of cutting dead zone on micro-milling force cannot be ignored. A simulation model of micro-milling aluminum alloy LF 21 processes based on DEFORM 3D is built by combining the theory of cutting and the technology of process simulation. Prediction of tool wear is achieved. The quantitative relationship between the arc radius of the cutting edge and tool wear is clarified for the first time. The authors built an improved cutting force model in micro-milling LF 21 considering tool wear and cutter runout with the minimum cutting thickness as the boundary. The validity of the built micro-milling force model is verified by experiments.

An improved cutting force model in micro-milling considering the comprehensive effect of tool runout, size effect, and tool wear

Tool runout, cutting edge radius-size effect, and tool wear have significant impacts on the cutting force of micro-milling. In order to predict the micro-milling force and the related cutting performance, it is necessary to establish a cutting force model including tool runout, cutting edge radius, and tool wear. In this study, an instantaneous uncut chip thickness (IUCT) model considering tool runout, a nonlinear shear/ploughing coefficient model including cutting-edge radius, and a friction force coefficient model embedded with flank wear width are respectively constructed. By integrating the IUCT, the nonlinear shear/ploughing coefficient and the friction force coefficient, a comprehensive micro-milling force model including tool runout, cutting edge radius, and tool wear is derived. Experiment results show that the proposed comprehensive model is efficient to predict the nonlinear cutting force of micro-milling with variable tool runout, cutting edge radius, and tool wear.

Prediction of Nonlinear Micro-Milling Force with a Novel Minimum Uncut Chip Thickness Model.

The minimum uncut chip thickness (MUCT), dividing the cutting zone into the shear region and the ploughing region, has a strong nonlinear effect on the cutting force of micro-milling. Determining the MUCT value is fundamental in order to predict the micro-milling force. In this study, based on the assumption that the normal shear force and the normal ploughing force are equivalent at the MUCT point, a novel analytical MUCT model considering the comprehensive effect of shear stress, friction angle, ploughing coefficient and cutting-edge radius is constructed to determine the MUCT. Nonlinear piecewise cutting force coefficient functions with the novel MUCT as the break point are constructed to represent the distribution of the shear/ploughing force under the effect of the minimum uncut chip thickness. By integrating the cutting force coefficient function, the nonlinear micro-milling force is predicted. Theoretical analysis shows that the nonlinear cutting force coefficient function embedded with the novel MUCT is absolutely integrable, making the micro-milling force model more stable and accurate than the conventional models. Moreover, by considering different factors in the MUCT model, the proposed micro-milling force model is more flexible than the traditional models. Micro-milling experiments under different cutting conditions have verified the efficiency and improvement of the proposed micro-milling force model.

Establishment of the optimal predictive model of die steel SKD 11 micromilling via nanofluid (graphene)/ultrasonic atomizing minimum quantity lubrication

This study is mainly utilized nanofluid (graphene) and ultrasonic atomization system to process SKD11 mold steel in micromilling to improve the quality of processed products, find the optimal quality characteristics, and construct an accurate prediction model. In this study, Taguchi’s robust design is adopted. The L18(21×37) orthogonal table is used to find the optimal parameter combinations for each quality characteristic, and the micromilling force and micromilling temperature are used as characteristic indicators. The control variables are the average thickness of nanographene, weight percent concentration, ultrasonic atomization amount, spindle speed, feed rate, air pressure, nozzle angle, nozzle distance. Also, a back propagation neural network (BPNN) is used to predict the micromilling processing mode. Taguchi method is used to optimize the hyper parameters in the neural network to improve the accuracy of the prediction model, reduce the input of the number of training samples, and then build a neural network prediction model that can accurately predict the quality characteristics of micromilling force and micromilling temperature. The prediction results show that the milling force parameter value error between the micromilling force model prediction and the single target optimization is 0.55%. The error between the micromilling temperature model prediction and the single-target optimization of the micromilling temperature value is 5.90%.

A Comprehensive Micro-Milling Force Model for a Low-Stiffness Machining System

Abstract Micro-milling is widely used in various crucial fields with the ability of machining micro- and meso-scaled functional structures on various materials efficiently. However, the micro-milling force model is not comprehensively developed yet when tool feature sizes continually decrease to under 200 µm in a low-stiffness system. This paper proposes an analytical force model considering the influence of tool radius, size effect, tool runout, tool deflection, and the actual trochoidal trajectories and the interaction of historical tool teeth trajectories (IHTTT). Different micro-milling status are recognized by analyzing the cutting process of different tool teeth. Conditions of single-tooth cutting status are determined by a proposed numerical algorithm, and entry angle and exit angle are analyzed under various cutting conditions for the low-stiffness system. Three micro-milling status, including single-tooth cutting status, are distinguished based on the instantaneous undeformed chip thickness resulting in three types of material removal mechanisms in predicting micro-milling force components. Discontinuous change rates of undeformed chip thickness are found in the low-stiffness micro-milling system. The proposed micro-milling force model is then verified through experiments of micro slot milling Elgiloy alloy with a 150-μm-diametrical two-teeth micro-end mill. The experimental results show a root-mean-square error (RSME) of 0.092 N in the predicted resultant force, accounting for approximately 5.12% of the measured force, by which the proposed theoretical model is verified to be of good prediction accuracy.

3D chatter stability of high-speed micromilling by considering nonlinear cutting coefficients, and process damping

High-speed micromilling can be used to fabricate 3D complex miniature components. The regenerative chatter, which is closely related to machining geometric accuracy, surface quality and tool wear, is a very important topic in micromachining research. In order to predict the high-speed micromilling stability more comprehensively, a micromilling force model was developed, which took into account the nonlinear change of cutting coefficients caused by feed rate, and the process damping effect in the shearing-dominant regimes. The relationship between micromilling force coefficients and feed rate were researched by using the least square and regression analysis method which are suitable to fit the relationship between related variables through experimental data, and ignore the complex internal relationships. The dynamic parameters of tool-machine system were obtained by performing FEM simulation on the entire spindle-holder-tool system. The continuous change of feed rate was first considered into micromilling 3D stability model by analyzing the influence of feed rate on milling force coefficients. Finally, micromilling tests were performed on AL7075 to verify the accuracy of the stability prediction model.

Micro-milling force modeling with tool wear and runout effect by spatial analytic geometry

One of the major limitations of micro-milling applications in industries is its fast tool wear, which leads to low machining precision and efficiency. An accurate force model is fundamental for optimization micro-milling processes and minimize the tool wear. However, a generic model with tool runout and wear effect has not yet been established, which limits its practical application under varied working conditions. In this paper, a new idea is introduced by applying the spatial analytic geometry (SAG) method, under this framework the micro-milling force model is established based on the analysis of the geometrical relationship among the cutting edge positions, pre-processed workpiece morphology, and cutting force directions considering tool runout and wear effect. In this model, the tool runout is identified exclusively by only one parameter, namely the distance away from the center that perpendicular to the feed direction, so that it could be calibrated conveniently by calculating the ratio of resultant forces corresponding to different cutting edges. The tool wear–induced force is then modeled as increment of force coefficients to the original model. Therefore, the new force model with considering tool wear has the same form as the fresh tool. Finally, the accuracy and efficiency of the model are validated by experiments under varied working conditions.

Improved dynamic cutting force modelling in micro milling of metal matrix composites Part I: Theoretical model and simulations

Cutting force modelling for micro machining is of great importance for better understanding of the cutting mechanics in the process. As precision machining of metal matrix composites (MMCs) is much more complex than machining homogeneous materials, the accurate prediction of cutting force is critical to control the cutting performance and tooling life. This paper presents an improved theoretical dynamic cutting force model in MMCs micro milling process by using straight flute PCD end mills. The theoretical model is modified and improved based on the conventional milling force modelling while taking account of various factors including tooling geometries, the material microstructure, size effect and chip formation in the cutting process. An innovative expression on dynamic cutting force in micromachining MMCs is presented through the multiscale modelling and analysis. The cutting force coefficients are further defined through instantaneous cutting force signals analysis. Simulation results under varied cutting process variables are presented against a series of MMCs micro milling trials, which indicate that the improved dynamic cutting force model can predict the cutting force accurately and reveal more details on the cutting force variations in the process due to the material inhomogeneous nature. In addition, the optimal process variables can be readily explored through the dynamic force modelling and simulations so as to further improve the cutting performance.

Improved dynamic cutting force modelling in micro milling of metal matrix composites part II: Experimental validation and prediction

The effects of cutting dynamics and the particles' size and density cannot be ignored in micro milling of metal matrix composites. This article presents the improved dynamic cutting force modelling for micro milling of metal matrix composites based on the previous analytical model. This comprehensive improved cutting force model, taking the influence of the tool run-out, actual chip thickness and resultant tool tip trajectory into account, is evaluated and validated through well-designed machining trials. A series of side milling experiments using straight flutes polycrystalline diamond end mills are carried out on the metal matrix composite workpiece under various cutting conditions. Subsequently, the measured cutting forces are compensated by a Kalman filter to achieve the accurate cutting forces. These are further compared with the predicted cutting forces to validate the proposed dynamic cutting force model. The experimental results indicate that the predicted and measured cutting forces in micro milling of metal matrix composites are in good agreement.

Surface location error prediction and stability analysis of micro-milling with variation of tool overhang length

The stability of micro-milling has an important influence on the machined surface, and choosing proper machining parameters can effectively avoid machining process instability and dynamic error. The frequency response function (FRF) of micro-milling tool point varies with tool overhang lengths, and it affects stability lobe diagram (SLD) of the machining system. During the micro-milling process, the cutting thickness was divided into the shearing zone and the plowing zone. In the shearing zone, the cutting thickness was calculated with considering cycloidal tool path and dynamic cutting thickness. After the micro-milling force model was established, the receptance coupling substructure analysis (RCSA) and experimental modal analysis (EMA) were used to obtain the FRFs at the micro-milling tool point. The 2 three-dimensional diagrams, SLD and surface location error (SLE), were developed comprehensively by means of frequency domain method, and the flank micro-milling experiments were carried out. The validity of the three-dimensional SLD containing tool overhang length and the prediction model of SLE was verified by the experimental results, and surface topography was characterized to show the actual machining state. The experimental results show that, compared with SLE, the change of tool overhang length had more significant influence on SLD, and the chatter remarks were not very obvious on the bottom of the workpiece no matter whether the machining process was stable or not. In unstable region, chatter caused by cutting tool left marks on the machined surface. Under the condition of stable milling, a machined surface with smaller SLE and better topography was obtained. The 2 three-dimensional diagrams can provide good references for the selection of optimal tool overhang length and machining parameters comprehensively.

Analytical model of work hardening and simulation of the distribution of hardening in micro-milled nickel-based superalloy

During the process of micro-milling nickel-based superalloy, the cutting surface is strengthened along with the high strain and strain rate, which leads to work hardening and affects the quality of parts and the tool’s life. At present, there are few analytical models that can predict the micro-hardness of the micro-milled surface. Therefore, the authors focus on the research of the micro-milling work hardening of nickel-based superalloy. Firstly, based on the stress-strain relationship and strain-hardening characteristics of Inconel718, the relationship between hardness and strain of Inconel718 is obtained. Then, based on the established micro-milling force model, the stress of a point in the process of micro-milling is derived. Finally, the surface hardness value of the machined surface is predicted by combining the relationship between the stress-strain and the hardness of the workpiece. In addition, a 3D simulation model of micro-milling nickel-based superalloy groove is established by Deform-3D software to research the distribution of micro-hardness of the machined surface. The research offers reference for improving the surface integrity and fatigue performance of micro-milling nickel-based superalloy and explores a feasible way for revealing the mechanism of micro-milling hardening.

Coupled thermal and mechanical analyses of micro-milling Inconel 718

Micro-milling forces, cutting temperature, and thermal–mechanical coupling are the key research topics about the mechanism of micro-milling nickel-based superalloy Inconel 718. Most current analyses of thermal–mechanical coupling in micro-milling are based on finite element or experimental methods. The simulation is not conducive to revealing the micro-milling mechanism, while the results of experiments are only valid for certain machine tool and workpiece material. Few analytical coupling models of cutting force and cutting temperature during micro-milling process have been proposed. Therefore, the authors studied coupled thermal–mechanical analyses of micro-milling Inconel 718 and presented a revised three-dimensional analytical model of micro-milling forces, which considers the effects of the cutting temperature and the ploughing force caused by the arc of cutting edge during shear-dominant cutting process. Then, an analytical cutting temperature model based on Fourier’s law is presented by regarding the contact area as a moving finite-length heat source. Coupling calculation between micro-milling force model and temperature model through an iterative process is conducted. The novelty is including cutting temperature into micro-milling force model, which simulates the interaction between cutting force and cutting temperature during micro-milling process. The established model predicts both micro-milling force and temperature. Finally, experiments are conducted to verify the accuracy of the proposed analytical method. Based on the coupled thermal–mechanical analyses and experimental results, the authors reveal the effects of cutting parameters on micro-milling forces and temperature.

Investigation on Innovative Dynamic Cutting Force Modelling in Micro-milling and Its Experimental Validation

In this paper, an innovative cutting force modelling concept is presented by modelling cutting forces against micro-cutting processes such as micro-milling, ultraprecision turning and abrasive micromachining, and also taking account of micro-cutting dynamics. The modelling represents the underlying micro-cutting mechanics and physics in micro-milling in an innovative multi-scale manner, i.e. the specific cutting force at the unit length, unit area and unit volume by considering the size effect, cutting fracture energy, the material modulus, and the cutting heat and temperature partition. A novel instantaneous chip thickness algorithm is introduced to analyse the real chip thickness by taking account of the effects of the micro-tool geometry change brought up by the tool run-out and further contribute to the force model through a numerical iterative algorithm. The measured cutting forces are compensated by a Kalman filter to achieve the accurate cutting forces. This is further utilized to calibrate the model coefficients using least square method. The cutting force modelling is evaluated and validated through well-designed micro-milling trials, which can be used for optimizing the cutting process and tool cutting performance in particular.

A modified analytical cutting force prediction model under the tool flank wear effect in micro-milling nickel-based superalloy

This study attempts to develop a micro-milling force model under cutting conditions considering tool flank wear effect during micro-milling of nickel-based superalloy with coated carbide micro-milling tools based on our three-dimensional dynamic cutting force prediction model established earlier. The tool wear condition in micro-milling nickel-based superalloy was obtained by finite element method. A 3D thermal-mechanical coupled simulation model for micro-milling nickel-based superalloy was developed to obtain the tool wear conditions and the distribution of stress. For the given tool geometries and machining conditions, the cutting forces considering tool flank wear effect could be determined conveniently. In addition, experiments of micro-milling nickel-based superalloy were conducted to estimate the validity of the modified model. The results showed that the proposed modified force analytical model could predict micro-milling cutting forces more accurately.

Progress of Cutting Force Modelling in Micromilling

Progress of Cutting Force Modelling in Micromilling

Force modelling in micromilling of channels

The interest on micro cutting processes is proved by the attention of industries on this topic. This trend moves the researches on micro cutting toward different aspects. A modelling procedure for forecasting cutting forces in microcutting, considering all phenomena involved in micro scale, can be of interest for industries allowing the evaluation of process quality. This paper deals with modelling of cutting forces in micromilling operations of channels. The proposed procedure is a combination of a force model based on specific cutting pressure and instantaneous chip section, estimated considering the tool run-out contribution, an optimisation strategy (particles swarm optimisation), and data coming from experimental tests realised on a sample of titanium alloy (Ti6Al4V). The comparisons between experimental and analytical data, and the evaluation of the uncertainty of the calibrated model show the good ability of the proposed procedure for defining analytical model for force prediction in channels micromilling.

Characterization and Micromilling of Flow Induced Aligned Carbon Nanotube Nanocomposites

Carbon nanotube (CNT) based polymeric composites exhibit high strength and thermal conductivity and can be electrically conductive at a low percolation threshold. CNT nanocomposites with polystyrene (PS) thermoplastic matrix were injection-molded and high shear stress in the flow direction enabled partial alignment of the CNTs. The samples with different CNT concentrations were prepared to study the effect of CNT concentration on the cutting behavior of the samples. Characterizations of CNT polymer composites were studied to relate different characteristics of materials such as thermal conductivity and mechanical properties to micromachining. Micro-end milling was performed to understand the material removal behavior of CNT nanocomposites. It was found that CNT alignment and concentrations influenced the cutting forces. The mechanistic micromilling force model was used to predict the cutting forces. The force model has been verified with the experimental milling forces. The machinability of the CNT nanocomposites was better than that of pure polymer due to the improved thermal conductivity and mechanical characteristics.

Fuzzy cutting force modelling in micro-milling

Reliable prediction of cutting forces is essential for micromilling. In this paper, a fuzzy cutting force modelling method based on subtractive clustering method filters the noise and estimates the instantaneous cutting forces using observations acquired by sensors during cutting experiments. In the experimental case study, four data sets of micromilling cutting force are used. Each data set is used to generate a learning system which is tested by the other three data sets. It is proven that the proposed algorithm has the capability to filter and model the cutting force in spite of uncertainties in the micromilling process.