Open Access

Table 1

Key scientific studies and their findings.

Chip thickness modeling
Authors Type of model Material Main findings
Son et al. [4] Analytical, experimental Aluminum, Brass, OFHC The minimum cutting thickness was determined by the tool edge radius and the friction coefficient of a workpiece-tool.
Bao and Tansel [7,8] Analytical Aluminum, Copper, Steel Model considers effect of milling direction on chip thickness. The chip thickness of down-milling is always bigger than that of up-milling.
Kang and Zheng [11] Analytical, Fourier series Aluminum 7075 The chip thickness is estimated by summing the thicknesses of the conventional chip component and the additional chip component that considers effects of feed per tooth, number of cutter teeth and cutter radius on the chip thickness.
Vogler et al. [5] FEM Pearlite and Ferrite The minimum chip thickness during micro milling differs between 20% and 35% of tool edge radius depending on material ductility.
Li et al. [9] Numerical, Tylor's series Theoretical study The undeformed chip thickness can be found through finding the intersection point of the path curve left by preceding tooth and line passing through current tooth tip and cutter axis.
Force modeling
Authors Type of model Material Main findings
Fang et al. [19] Analytical Theoretical study The size effect, which means the specific cutting force increases with a decrease in undeformed chip thickness, highly depends on the material constitutive behavior in machining.
Jun et al. [18] Analytical Pearlite and Ferrite Only elastic deformation occurs in the workpiece material when the uncut chip thickness is smaller than critical value. As uncut chip thickness increases beyond critical value, the deformation becomes mixed elastic-plastic.
Zhang et al. [24] Analytical, experimental Copper The actual uncut chip thickness might be increasing or decreasing depending on tool run-out, cutting direction (up/down-milling) and depth of cut.
Park and Malekian [15] Mechanistic Aluminum 7075 The cutting forces due to ploughing regime, when the chip thickness is lower than the critical value, are formulated as a function of ploughed material volume.
Malekian et al. [17] Mechanistic Aluminum 6061 The receptance coupling of the spindle and the micro-tools is employed to extract the dynamics at the tool tip. The frequency analysis of the forces shows that the effects of run-out and tool imperfections are more significant at low feed rates.
Jin and Altintas [13] FEM Brass 260 The feed force, which is mainly contributed by the integration of friction stress along the tool–chip contact area, can be underestimated in FE simulations due to the underestimation of the friction stress along the tool-rake face.
Jing et al. [21] Hybrid model: FEM and Mechanistic AISI 1045 The increase in the cutting speed can lead to an increase in effect of the runout and when the feed per tooth is smaller than the runout, there is only one cutting edge engaged in the machining operation within one cutter revolution.
Jing et al. [23] Hybrid model: FEM and Mechanistic Ti6Al4V The cutter runout, tool-workpiece vibration and material elastic recovery have a significant effect on the cutting forces, especially at lower feed per tooth.
Tool deflection and surface roughness modeling
Authors Type of model Material Main findings
Mamedov et al. [25] Analytical Ti6Al4V The instantaneous deflections of micro end mill is calculated by dividing cutting force matrix, which is applied on the tool as a distributed load along the cutting edge, with stiffness matrix of the cutter modeled as Timoshenko beam element.
Sun et al. [26] Analytical Aluminum 6061 The variation of surface generation mechanisms can induce periodic cutting force oscillations and highly non-uniform surfaces characterized as low surface roughness in the center indicating shearing mechanism and high surface roughness on the sides suggesting ploughing effect.
Zhang et al. [35] Analytical Aluminum 6061 The trajectory-based surface generation model is developed by considering the comprehensive effects of tool run-out, stochastic tool wear, size effect and the concept of the minimum chip thickness.
Matsumara and Ono [37,38] Analytical Glass Due to absence of elastic deformation in glass, the edge roughness of cutting tool is transferred directly onto the surface without elastic recovery.
Leo Kumar [27] Experimental C360 Copper alloy Genetic Algorithm based on the principle of natural genetics and natural selection was used to minimize surface roughness and machining time. As the result author concluded that combination of low spindle speed and feed rate yields good surface finish.
Beruvides et al. [29] Experimental, ANFIS Tungsten–copper alloy (W78Cu22) Multiple Linear Regression technique was used to predict surface roughness as a function of the z-axis vibration captured during the cutting process.
Vipindas and Mathew [30] Experimental Ti6Al4V The influence of the tool wear on surface roughness was investigated and enlargement of tool edge radius was found to be one of the wear modes in micro machining as it affects the machining mechanism.
Chen et al. [31,34] FEM, experimental Aluminum 6061 Since the machining system is not a rigid body system, the cutting force generated during milling could induce the relative displacement between tool and workpiece, combine word displacement.
Liu et al. [39] Experimental Aluminum 6061 In the sidewall surface topography generated from the relative motion between the major cutting edge and the workpiece material. In the floor surface topography is effected by the minimum chip thickness, the elastic recovery, and the transverse vibrations.
Groß et al. [40] Experimental Brass (CuZn39Pb3) The kinematic substructures formed on generated surface are significantly influenced by the feed per tooth and effects of material separation are strongly influenced by the selected spindle speed.
Thermal modeling
Authors Type of model Material Main findings
Lin et al. [46] Analytical 300M steel The model considers heat generated at the flank surface and stated that with progress of flank wear the proportion of temperature rise contributed by flank wear-land heat source occupied about 25–35% of total heat generated during machining.
Mamedov and Lazoglu [49] Hybrid model: FEM and Analytical Ti6Al4V The model calculates main heat inputs, generated in the primary and secondary deformation zones, as instantaneous heat sources in the shear plane and on the chip-tool contact surface. Calculated shearing and frictional heat values are employed as the heat input sources to the shearing and frictional zones in the finite element thermal simulations of the micro milling.
Lazoglu and Altintas [44] Finite difference method AISI 4140, Aluminum alloys The model estimates temperature for continuous and interrupted machining operations by considering shear energy formed in the primary zone, friction energy created at the chip-tool interface and the heat balance between chip and cutting tool.
Lazoglu and Islam [45] Finite difference method AISI 4140 An elliptic structural grid generation, that was used to solve temperature field during oblique machining operations, allowed to model different cutter geometries.
Yang et al. [47] FEM Aluminum 2024 The model estimates the effect of tool edge radius on cutting force, effective stress and mean temperature. It showed that with an increase of tool edge radius, the cutting force increases, while the effective stress and mean cutting temperature of the micro-cutter decreases.
Ozel et al. [50] FEM Ti6Al4V The model revealed advantages of cBN coated tool, which due to a lower friction coefficient and higher effective thermal conductivity has lowest temperature rise during machining.
System modeling
Authors Type of model Material Main findings
Park and Rahnama [52] Analytical Aluminum 7075 To find the stability lobes for micro-milling operations, the algorithm sweeps the depths of cut and chatter frequencies at each spindle speed; and, it checks the stability through the proposed automated zero exclusion method. The first set of unstable conditions is recorded as the border between the stable and unstable regions.
Graham et al. [54] Analytical Brass A comparative study to evaluate two different methods for prediction of chatter stability in micro milling, namely Edge theorem and Linear Matrix Inequality (LMI) method based on Lyapunov stability theory.
Mokhtari et al. [56] Analytical AISI 1045 Neglecting size effects, gyroscopic moments, and rotary inertia in the tool model causes significant errors in prediction of the stability boundary of the micro milling process.
Singh and Singh [55] Hybrid model: FEM and Experimental Used the component mode synthesis approach to couple two substructures (machine tool with a portion of shank and the micro-end mill) and determine the tool-tip dynamics.
Machining induced distortion and residual stress modeling
Authors Type of model Material Main findings
Denkena et al. [61] Experimental Aluminum 7449 The effect of cutting parameters on residual stresses and subsurface material changes was investigated and results showed that the increase of the cutting speed may reduce the compressive residual stresses on the surface. The residual stress at the surface tends to be less compressive or to a zero value with the increase of the feedrate, while the maximum compressive residual stress significantly increases.
Arrazola et al. [62] FEM Inconel 718 The model is able to simulate elastic–viscoplastic material flow around the cutting tool tip by combining coupled deformation and heat transfer using implicit integration method.
Schulze et al. [64] FEM Aluminum 7075 The model analyzed an influence of different initial stress states, introduced by 4 point bending operation, and different machining parameters on the amount of distortion via FEM simulations and experimental results. Results showed that amount of initial stress has a significant effect on distortion potential.
Lazoglu and Mamedov [65] FEM Ti6Al4V The distortion of the thin wall structure was analyzed by loading it with moving heat source and mechanical load at each axial depth of cut for a time period equal to the machining time. Later, thermal and mechanical loads were removed and distortion was measured as total plastic deformation after elastic recovery of the workpiece.
Jia et al. [66] FEM Inconel 718 The predicted value of milling force output from the process simulation model is used as the load in the deformation prediction model that uses Johnson-Cook failure model at high speed and high strain rate to describe the failure criteria of the thin-walled part.

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