Energy field-assisted high-speed dry milling green machining technology for difficult-to-machine metal materials

doi:10.1007/s11465-022-0744-9

Front. Mech. Eng.

2023, Vol. 18

Issue (2) : 28 https://doi.org/10.1007/s11465-022-0744-9

REVIEW ARTICLE

Energy field-assisted high-speed dry milling green machining technology for difficult-to-machine metal materials

Jin ZHANG^1,², Xuefeng HUANG^1,², Xinzhen KANG^1,², Hao YI^1,², Qianyue WANG^1,², Huajun CAO^1,²(

)

¹. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
². State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China

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Abstract

Energy field-assisted machining technology has the potential to overcome the limitations of machining difficult-to-machine metal materials, such as poor machinability, low cutting efficiency, and high energy consumption. High-speed dry milling has emerged as a typical green processing technology due to its high processing efficiency and avoidance of cutting fluids. However, the lack of necessary cooling and lubrication in high-speed dry milling makes it difficult to meet the continuous milling requirements for difficult-to-machine metal materials. The introduction of advanced energy-field-assisted green processing technology can improve the machinability of such metallic materials and achieve efficient precision manufacturing, making it a focus of academic and industrial research. In this review, the characteristics and limitations of high-speed dry milling of difficult-to-machine metal materials, including titanium alloys, nickel-based alloys, and high-strength steel, are systematically explored. The laser energy field, ultrasonic energy field, and cryogenic minimum quantity lubrication energy fields are introduced. By analyzing the effects of changing the energy field and cutting parameters on tool wear, chip morphology, cutting force, temperature, and surface quality of the workpiece during milling, the superiority of energy-field-assisted milling of difficult-to-machine metal materials is demonstrated. Finally, the shortcomings and technical challenges of energy-field-assisted milling are summarized in detail, providing feasible ideas for realizing multi-energy field collaborative green machining of difficult-to-machine metal materials in the future.

Keywords difficult-to-machine metal material green machining high-speed dry milling laser energy field-assisted milling ultrasonic energy field-assisted milling cryogenic minimum quantity lubrication energy field-assisted milling

Corresponding Author(s): Huajun CAO

Just Accepted Date: 20 December 2022 Issue Date: 11 July 2023

Cite this article:

Jin ZHANG,Xuefeng HUANG,Xinzhen KANG, et al. Energy field-assisted high-speed dry milling green machining technology for difficult-to-machine metal materials[J]. Front. Mech. Eng., 2023, 18(2): 28.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-022-0744-9
https://academic.hep.com.cn/fme/EN/Y2023/V18/I2/28

Fig.1 Overall structure of this review. HSDM: high-speed dry milling, LAM: laser-assisted milling, UVAM: ultrasonic vibration-assisted milling, CMQLAM: cryogenic minimum quantity lubrication energy field-assisted milling.

Fig.2 Logical structure of high-speed dry milling difficult-to-machine metal materials. CVD: chemical vapor deposition, PVD: physical vapor deposition, HSDM: high-speed dry milling.

Fig.3 Influence of cutting parameters on tool wear, cutting force and temperature: (a) effect of v_c and f_z on tool wear [105], (b) cutting force under different v_c [106], and (c) cutting temperature under different v_c [106]. Reproduced with permissions from Refs. [105,106] from Elsevier and Springer Nature.

Fig.4 Influence of cutting parameters on microstructure and chip morphology: (a) workpiece microstructure and deformation zone under different v_c [107], (b) effect of v_c on chip morphology [108], and (c) major section of chips at different v_c [108]. Reproduced with permissions from Refs. [107,108] from Trans Tech Publications Ltd. and Springer Nature.

Fig.5 XRD, flank wear and tool wear images of AlTiN- and TiAlSiN-coated tools: (a) XRD diffraction patterns, (b) flank wear of cutting length under different v_c, and (c) tool flank wear images under different cutting lengths [112]. Reproduced with permission from Ref. [112] from Springer Nature.

Fig.6 Microhardness and residual stress under different cutting parameters: (a) microhardness of depth from the machined surface under different cutting parameters and (b) variation of machining surface residual stress with cutting parameters [117]. Reproduced with permission from Ref. [117] from Elsevier.

Fig.7 Tool wear under different v_c: (a) tool wear under different machining conditions [120] and (b) experiment after cutting 21.75 cm³ and flank wear of finite element method [121]. Reproduced with permissions from Refs. [120,121] from Malaysian Tribology Society and Springer Nature.

Fig.8 Cutting temperature and tool wear under different cutting conditions: (a) temperature distribution under different v_c conditions and (b) tool wear with different cutting volumes [121]. Reproduced with permission from Ref. [121] from Springer Nature.

Fig.9 Surface roughness corresponding to different coated tools: (a) SiAlON tool and (b) physical vapor deposition tool [124]. PVD: physical vapor deposition. Reproduced with permission from Ref. [124] from Springer Nature.

Fig.10 Summary of high-speed dry milling machinability.

Fig.11 Laser-assisted milling (LAM) principle and logic: (a) LAM schematic diagram [128] and (b) logical structure of LAM difficult-to-machine metal materials. FEM: finite element method; B&F: back and forth. Reproduced with permission from Ref. [128] from Springer Nature.

Fig.12 Cutting forces and grain deformation distribution: (a) tool wear under different cutting conditions [134] and (b) surface roughness at different v_c [135]. CM: conventional milling; LAM: laser-assisted milling; H.F: high-feed milling; L.F: low-feed milling. Reproduced with permissions from Refs. [134,135] from Elsevier.

Fig.13 Force prediction and cutting morphology: (a) laser-assisted milling (LAM) force prediction methodology [136] and (b) comparison of chip morphology [137]. CM: conventional milling. Reproduced with permissions from Refs. [136,137] from ASME and Elsevier.

Fig.14 Effect of different laser parameters on heat-affected zone: (a) P_L, (b) d_L, (c) v_f, and (d) laser incident angle [138]. HAZ: heat-affected zone. Reproduced with permission from Ref. [138] from Springer Nature.

Fig.15 Conventional milling and laser-assisted milling (LAM) machined surface: (a) surface roughness and (b) microstructure [139]. CM: conventional milling. Reproduced with permission from Ref. [139] from MDPI.

Fig.16 Rotation angle and round angle of laser-assisted milling: (a) temperature distribution of rotation angle [140] and (b) round-angle laser-assisted milling path [141]. Reproduced with permission from Refs. [140,141] from Springer Nature.

Fig.17 Thermal analysis of laser-assisted milling [142]. Reproduced with permission from Ref. [142] from Springer Nature.

Fig.18 Surface quality, unit cost and annual cost: (a) microstructure, (b) residual stress, (c) cost of the unit under different v_c [144], and (d) annual costs of produced parts [145]. CM: conventional milling; LAM: laser-assisted milling. Reproduced with permissions from Refs. [144,145] from Springer Nature and Elsevier.

Fig.19 Flank face wear at different milling conditions: (a) scanning electron microscope of coated tool at 10s with laser-assisted milling, (b) scanning electron microscope of coated tool at 16.6 min with laser-assisted milling [148], and (c) scanning electron microscope of the coated tool at 20 mm with laser-assisted milling under different P_L [149]. Reproduced with permissions from Refs. [148,149] from Springer Nature and ASME.

Fig.20 The influence of workpiece inclination angles on cutting force and tool damage: (a) workpiece inclination and cutting force and (b) workpiece inclination and tool damage [151]. CM: conventional milling; LAM: laser-assisted milling. Reproduced with permission from Ref. [151] from Springer Nature.

Fig.21 Temperature feedback and cutting temperature: (a) effect of P_L on cutting temperature, (b) effect of V_L on cutting temperature [154], and (c) schematic diagram of the temperature feedback system [153]. Reproduced with permission from Refs. [153,154] from Springer Nature.

Fig.22 Cutting forces and grain deformation distribution: (a) cutting forces of path-optimized laser-assisted milling (LAM) [155] and (b) grain deformation distribution in different machining processes [156]. CM: conventional milling; LS: single laser scanning. Reproduced with permissions from Refs. [155,156] from Elsevier.

Fig.23 Summary of laser-assisted milling machinability. HAZ: heat-affected zone.

Fig.24 Ultrasonic vibration-assisted milling (UVAM) principle and logic: (a) UVAM principle and device [159,160] and (b) logical structure of laser-assisted milling difficult-to-machine metal materials. CM: conventional milling. Reproduced with permissions from Refs. [159,160] from Elsevier.

Fig.25 Changes in surface morphology, burr size and surface roughness under different f_z: (a) bottom surface morphology and (b) burr width and surface roughness [164]. UVAM: ultrasonic vibration-assisted milling; CM: conventional milling. Reproduced with permission from Ref. [164] from Springer Nature.

Fig.26 Microstructure and surface roughness of conventional milling (CM) and ultrasonic vibration-assisted milling (UVAM): (a) microstructure of processed surface [166] and (b) three-dimensional surface roughness [167]. Reproduced with permissions from Refs. [166,167] from Springer Nature and Elsevier.

Fig.27 Test parameter flow and data comparison: (a) ultrasonic vibration-assisted milling overall test parameter flow and (b) A impact on measurement data [170]. Reproduced with permission from Ref. [170] from Elsevier.

Fig.28 Theoretical model and results: (a) theoretical model of the machined surface and (b) different A values of the machined surface [172]. Reproduced with permission from Ref. [172] from Journal of Vibroengineering.

Fig.29 Optimization of resonant block and analysis of tool tip trajectory: (a) finite element method (FEM) structure optimization of ultrasonic vibration-assisted milling (UVAM) system and resonant block and (b) tool-tip motion trajectory in UVAM and conventional milling (CM) process [174]. Unit: mm. Reproduced with permission from Ref. [174] from Elsevier.

Fig.30 Principle and test results: (a) influence principle of different helical angle tools on cutting edge length and (b) effect of A and helical angle on tool wear value of different cutting lengths [176]. Reproduced with permission from Ref. [176] from Elsevier.

Fig.31 Three-dimensional morphology of ultrasonic vibration-assisted milling (UVAM) and conventional milling (CM) machined surfaces: (a) CM at v_c = 125.6 m/min, (b) UVAM at v_c = 125.6 m/min, (c) CM at v_c = 157 m/min, and (d) UVAM at v_c = 157 m/min [178]. Reproduced with permission from Ref. [178] from Springer Nature.

Fig.32 Ultrasonic vibration-assisted milling (UVAM) and conventional milling (CM) analysis model of cutting force [179]. Reproduced with permission from Ref. [179] from Taylor & Francis.

Fig.33 Ultrasonic vibration-assisted milling (UVAM) and conventional milling (CM) surface quality: (a) influence of f and helix angle on the processed surface [180] and (b) effect of f_z and f on surface roughness and residual stress [181]. Reproduced with permissions from Refs. [180,181] from MDPI and Elsevier.

Fig.34 Ultrasonic vibration-assisted milling (UVAM) and conventional milling (CM) chip morphology and tool wear: (a) chip morphology under different v_c [184] and (b) tool wear [185]. Reproduced with permissions from Refs. [184,185] from ASME and Elsevier.

Fig.35 Summary of ultrasonic vibration-assisted milling (UVAM) machinability. FEM: finite element method.

Fig.36 Cryogenic minimum quantity lubrication energy field-assisted milling system (CMQLAM), lubrication mechanism and logic: (a) CMQLAM system, (b) workpiece surface boundary lubrication [189], and (c) logical structure of laser-assisted milling difficult-to-machine metal materials. CA: cold air; MQL: minimum quantity lubrication. Reproduced with permission from Ref. [189] from Mechanical Science and Technology for Aerospace Engineering.

Fig.37 Different nozzle diameters, cutting temperatures, and specific cutting energies: (a) experimental results of different nozzle diameter outlets [190], and (b) effects of green lubrication and cutting parameters on cutting temperature and specific cutting energy [191]. MQL: minimum quantity lubrication. Reproduced with permissions from Refs. [190,191] from Springer Nature and Elsevier.

Fig.38 Grain size distribution and scanning electron microscope: (a) effect of green lubrication on grain size distribution [191] and (b) scanning electron microscope images of different green lubricants [193]. CM: conventional milling; MQL: minimum quantity lubrication. Reproduced with permissions from Refs. [191,193] from Elsevier.

Fig.39 Different green cooling lubrication and tool machining surfaces: (a) uncoated tool liquid nitrogen, (b) uncoated tool minimum quantity lubrication (MQL), (c) uncoated tool cryogenic MQL (CMQL), (d) low-temperature treatment tool LN₂, (e) low-temperature treatment tool MQL, (f) low-temperature treatment tool CMQL, (g) TiAlN-coated tool LN₂, (h) TiAlN-coated tool MQL, and (i) TiAlN-coated tool CMQL [197]. Reproduced with permission from Ref. [197] from Elsevier.

Fig.40 Tool wear under different v_c and lubrication/cooling methods: (a) flank wear of all lubrication/cooling methods and (b) final tool wear of all lubrication/cooling methods [199]. Reproduced with permission from Ref. [199] from Elsevier.

Fig.41 Effect of green lubrication and cutting parameters on (a) cutting temperature and (b) surface roughness [201]. Reproduced with permission from Ref. [201] from Springer Nature.

Fig.42 Tool life and microscopic images under different cutting conditions: (a) microscopic images of the cutting edge at the end of tool life and (b) tool life measurement and prediction results [204]. Reproduced with permission from Ref. [204] from Elsevier.

Fig.43 Nozzle structure and tool wear: (a) nozzle Coanda effect [206] and (b) influence of green lubrication and cutting volume on tool wear [207]. Reproduced with permissions from Refs. [206,207] from Springer Nature.

Fig.44 Average residual stresses corresponding to different f_z and v_c: (a) conventional milling 0° residual stresses, (b) conventional milling 90° residual stresses, (c) cryogenic minimum quantity lubrication 0° residual stresses, and (d) cryogenic minimum quantity lubrication 90° residual stresses [209]. Reproduced with permission from Ref. [209] from Elsevier.

Fig.45 Chip morphology, cutting force, and cutting temperature: (a) chip morphology under different lubrication environments [210] and (b) influence of cutting parameters on cutting force and cutting temperature [211]. CM: conventional milling; CMQL: cryogenic minimum quantity lubrication. Reproduced with permissions from Refs. [210,211] from ASTM and Springer Nature.

Fig.46 Subsurface and channel structures: (a) white layer [213] and (b) three channel structures of internal cooling milling tools [214]. Reproduced with permissions from Refs. [213,214] from SAGE and Springer Nature.

Fig.47 Summary of cryogenic minimum quantity lubrication energy field-assisted milling (CMQLAM) machinability.

Abbreviations
B&F	Back-and-forth
CA	Cold air
CCD	Central composite design
CFD	Computational fluid dynamics
CL	Conventional melting
CM	Conventional milling
CMQL	Cryogenic minimum quantity lubrication
CMQLAM	Cryogenic minimum quantity lubrication energy field-assisted milling
CVD	Chemical vapor deposition
DHC	Double helix channel
DSC	Double straight channel
FEM	Finite element method
HAZ	heat-affected zone
H.F	High feed milling
HM	Helical milling
HPDL	High-power semiconductor laser
HSDM	High-speed dry milling
LAM	Laser-assisted milling
LCO₂	Liquid carbon dioxide
L.F	Low feed milling
LMO	Local misorientation
LS	Single laser scanning
MQL	Minimum quantity lubrication
Nd:YAG	Neodymium-doped yttrium aluminum garnet
NMQL	Nanofluid minimum quantity lubrication
NURBS	Non-uniform rational B-spline
OoW	Oil-on-water
PCBN	Polycrystalline cubic boron nitride
PVD	Physical vapor deposition
SCCO₂	Supercritical carbon dioxide
SEM	Scanning electron microscope
SLM	Selective laser melting
SSC	Single straight channel
S&T	Spatial and temporal
TAM	Thermal-assisted machining
TC4	Ti–6Al–4V
UVAM	Ultrasonic vibration-assisted milling
XRD	X-ray diffraction
Variables
A	Vibration amplitude
d_L	Heat source size
f	Vibration frequency
f_z	Feed per tooth
N_z	Number of tips
P_ci	Coordinate tool point
P_li	Initial coordinate point
P_L	Laser power
P_Li	End coordinate point
r	Radius of the cutting tool
r_c	Sum of the radius of the cutting tool
R	Expected fillet radius
Sa	Average roughness
Sq	Surface root mean square roughness
t	Cutting time
v_c	Cutting speed
v_f	Feed speed
V_L	Laser scanning speed
x, y, z	Tip displacements
x_cl	Distance between the tool center and the laser heat source center
x_L	Distance between spot and tool
ω_r	Angular velocity of the spindle
α_i	Tool radius angle
α_p	Axial cutting depth
α_e	Radial cut width
β	Tool rotation angle
θ	Initial phase of the vibration signal
?_xi	Distance between the initial coordinate point of the heat source and the end coordinate point

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