Sub-nanometer finishing of polycrystalline tin by inductively coupled plasma-assisted cutting

doi:10.1007/s11465-023-0751-5

Front. Mech. Eng.

2023, Vol. 18

Issue (3) : 35 https://doi.org/10.1007/s11465-023-0751-5

RESEARCH ARTICLE

Sub-nanometer finishing of polycrystalline tin by inductively coupled plasma-assisted cutting

Peng LYU¹, Min LAI¹(

), Yifei SONG¹, Zhifu XUE¹, Fengzhou FANG^1,²(

)

¹. State Key Laboratory of Precision Measuring Technology & Instruments, Laboratory of MicroNano Manufacturing Technology—MNMT, Tianjin University, Tianjin 300072, China
². Centre of MicroNano Manufacturing Technology—MNMT-Dublin, School of Mechanical & Materials Engineering, University College Dublin, Dublin 4, Ireland

Download: PDF(4228 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Polycrystalline tin is an ideal excitation material for extreme ultraviolet light sources. However, the existence of grain boundary (GB) limits the surface roughness of polycrystalline tin after single-point diamond turning (SPDT). In this work, a novel method termed inductively coupled plasma (ICP)-assisted cutting was developed for the sub-nanometer finishing of polycrystalline tin. The relationship between ICP power, processing time, and modification depth was established by thermodynamic simulation, and the fitted heat transfer coefficient of polycrystalline tin was 540 W/(m²·K). The effects of large-thermal-gradient ICP treatment on the microstructure of polycrystalline tin were studied. After 0.9 kW ICP processing for 3.0 s, corresponding to the temperature gradient of 0.30 K/µm, the grain size of polycrystalline tin was expanded from a size of approximately 20–80 μm to a millimeter scale. The Taguchi method was used to investigate the effects of rotational speed, depth of cut, and feed rate on SPDT. Experiments conducted based on the ICP system indicated that the plasma-assisted cutting method promoted the reduction of the influence of GB steps on the finishing of polycrystalline tin, thereby achieving a surface finish from 8.53 to 0.80 nm in Sa. The results of residual stress release demonstrated that the residual stress of plasma-assisted turning processing after 504 h stress release was 10.7 MPa, while that of the turning process without the ICP treatment was 41.6 MPa.

Keywords plasma-assisted cutting polycrystalline tin single-point diamond turning surface roughness

Corresponding Author(s): Min LAI,Fengzhou FANG

Just Accepted Date: 29 March 2023 Issue Date: 03 August 2023

Cite this article:

Peng LYU,Min LAI,Yifei SONG, et al. Sub-nanometer finishing of polycrystalline tin by inductively coupled plasma-assisted cutting[J]. Front. Mech. Eng., 2023, 18(3): 35.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-023-0751-5
https://academic.hep.com.cn/fme/EN/Y2023/V18/I3/35

Fig.1 Principle of plasma-assisted cutting treatment for polycrystalline tin. Ar: argon.

Tab.1 ICP simulation parameters

Fig.2 Schematic of the simulation: (a) atmospheric inductively coupled plasma torch geometry model and (b) heat transfer from inductively coupled plasma to tin.

Fig.3 (a) Schematic and (b) photograph of the inductively coupled plasma setup. Ar: argon, RF: radio frequency.

Tab.2 Orthogonal parameters and corresponding values

Fig.4 Temperature simulation of the inductively coupled plasma torch: (a) 0.4 kW, (b) 0.6 kW, (c) 0.7 kW, (d) 0.8 kW, (e) 0.9 kW, and (f) 1.0 kW.

Tab.3 Fitted coefficients and thermal gradient in each group

Fig.5 (a) Section temperature distribution at 900 W and (b) error distribution after Gaussian fitting.

Fig.6 (a) Infrared thermal image of polycrystalline tin after inductively coupled plasma treatment at 0.9 kW for 3.0 s and (b) section of polycrystalline tin in the heat transfer simulation at 0.9 kW for 3.0 s.

Fig.7 Characterization of polycrystalline tin cross section: (a) laser confocal microscope image of the unmodified sample, (b) scanning electron microscopy (SEM) image of the 500 μm × 500 μm area in (a), (c) electron backscatter diffraction (EBSD) inverse pole figure map of the same surface in (b), (d) EBSD grain boundary map of the same surface in (b), (e) laser confocal microscope image of the modified sample, (f) SEM image of the 500 μm × 500 μm area in (e), (g) EBSD inverse pole figure of the same surface in (f), (h) EBSD grain boundary map of the same surface in (f), (i) SEM image of the 1500 μm × 1500 μm area in (e); (j) EBSD inverse pole figure of the same surface in (i), and (k) EBSD grain boundary map of the same surface in (i).

Fig.8 Section of polycrystalline tin in the heat transfer simulation at different powers. (a) P = 0.4 kW, t = 10.50 s; (b) P = 0.6 kW, t = 4.75 s; (c) P = 0.7 kW, t = 3.90 s; (d) P = 0.8 kW, t = 3.36 s; (e) P = 1.0 kW, t = 2.70 s.

Fig.9 Surface roughness in Sa of the polycrystalline tin samples modified using different powers.

Fig.10 Laser confocal microscope images of the polycrystalline tin samples modified using different radio frequency powers: (a) 0 kW, (b) 0.4 kW, (c) 0.7 kW, and (d) 0.9 kW.

Fig.11 Characterization of the polycrystalline tin surface: (a) scanning electron microscopy image of the 500 μm × 500 μm area in the unmodified sample, (b) electron backscatter diffraction inverse pole figure of the same surface in (a), (c) grain boundary inverse pole figure of the same surface in (a), (d) scanning electron microscopy image of the 500 μm × 500 μm area in the modified sample, (e) electron backscatter diffraction inverse pole figure of the same surface in (d), and (f) grain boundary inverse pole figure of the same surface in (d).

Tab.4 ANOVA results from the mean surface roughness

Tab.5 S/N response table for the surface roughness

Tab.6 Average surface roughness in Sa

Fig.12 Surface roughness under different parameters: (a) rotational speed of 3000 r/min, feed rate of 1.8 μm/r, and cut depth of 0.6 μm and (b) rotational speed of 2000 r/min, feed rate of 0.9 μm/r, and cut depth of 2 μm.

Fig.13 Electron backscatter diffraction (EBSD) result of the sample in Fig. 14(b). (a) scanning electron microscopy image of the 500 μm × 500 μm area, (b) EBSD inverse pole figure of the same surface in (a), and (c) EBSD grain boundary image of the same surface in (a).

Fig.14 Surface roughness in Sa under optimal orthogonal parameters: (a) without plasma treatment, (b) after 0.9 kW plasma treatment at the junction of two grains, and (c) after 0.9 kW plasma treatment in one grain.

Fig.15 Photograph of polycrystalline tin after processing with the optimal parameters of plasma-assister cutting.

Fig.16 Surface roughness in Sa under optimal parameters: (a) after 0.9 kW plasma treatment in one of the grains during 504 h standing, (b) after 0.9 kW plasma treatment at the junction of two grains during 504 h standing, (c) without plasma treatment during 504 h standing, (d) after 0.9 kW plasma treatment in one of the grains after 504 h standing, (e) after 0.9 kW plasma treatment at the junction of two grains after 504 h standing, and (f) without plasma treatment after 504 h standing.

Fig.17 Residual stress comparison of workpieces with and without PaC processing after standing for 72 and 504 h. PaC: plasma-assisted cutting.

Abbreviations
ACS	Atomic and close-to-atomic scale
Al	Aluminium
ANOVA	Analysis of variance
Ar	Argon
Cu	Copper
EBSD	Electron backscatter diffraction
EBSM	Electron beam selective melting
GB	Grain boundary
ICP	Inductively coupled plasma
PaC	Plasma-assisted cutting
RF	Radio frequency
SEM	Scanning electron microscopy
S/N	Signal-to-noise
SPDT	Single-point diamond turning
UCT	Undeformed chip thickness
WL	White light
Variables
A	Height of Gaussian function
B	Offset of the Gaussian function along the y-axis
C_P	Plasma’s specific heat capacity
C_p1	Tin’s specific thermal capacity
F	Lorentz force
GranT	Initial temperature gradient
h	Coefficient of heat transfer
I	Matrix of identity
k	Thermal conductivity
k₁	Thermal conductivity of tin
n	Normal vector
p	Pressure
q₀	Internal heat flow
q	Plasma’s heat flux conductivity vector
q₁	Vector of heat flux conductivity
Q	Source of heat
Q₁	Heat source of tin
Q_p	Work of pressure
Q_ted	Thermoelastic damping
Q_vd	Work of viscous dissipation
T	Plasma temperature
T₁	Temperature of tin
T_bottom	Bottom temperature of polycrystalline tin
T_ext	ICP action section temperature
T_ext0	Central temperature of the heat source
u	Plasma’s velocity vector
u₁	Tin’s velocity vector
α	Central angle of each point on the Debye ring
$ρ$	Plasma’s fluid density
$ρ 1$	Density of tin
$μ$	Dynamic viscosity
σ	Standard deviation of the Gaussian function

1	A T H Beaucamp , K Nagai , T Hirayama , M Okada , H Suzuki , Y Namba . Elucidation of material removal mechanism in float polishing. Precision Engineering, 2022, 73: 423–434 https://doi.org/10.1016/j.precisioneng.2021.10.004
2	E White , G O’Sullivan , P Dunne . Soft X-ray spectra of cerium laser-produced plasmas. Journal of Physics B: Atomic, Molecular, and Optical Physics, 2021, 54(23): 235701 https://doi.org/10.1088/1361-6455/ac42da
3	Z Bouza , J Byers , J Scheers , R Schupp , Y Mostafa , L Behnke , Z Mazzotta , J Sheil , W Ubachs , R Hoekstra , M Bayraktar , O O Versolato . The spectrum of a 1-μm-wavelength-driven tin microdroplet laser-produced plasma source in the 5.5–265.5 nm wavelength range. AIP Advances, 2021, 11(12): 125003 https://doi.org/10.1063/5.0073839
4	S Y Grigoryev , S A Dyachkov , A N Parshikov , V V Zhakhovsky . Limited and unlimited spike growth from grooved free surface of shocked solid. Journal of Applied Physics, 2022, 131(6): 065104 https://doi.org/10.1063/5.0078138
5	R M Flanagan , M A Meyers , S J Fensin . The role of pre-existing defects in shock-generated ejecta in copper. Journal of Applied Physics, 2021, 130(7): 075101 https://doi.org/10.1063/5.0056581
6	Y Z Wang , Y Q Geng , G Li , J Q Wang , Z Fang , Y D Yan . Study of machining indentations over the entire surface of a target ball using the force modulation approach. International Journal of Extreme Manufacturing, 2021, 3(3): 035102 https://doi.org/10.1088/2631-7990/abff19
7	F Z Fang . Atomic and close-to-atomic scale manufacturing: perspectives and measures. International Journal of Extreme Manufacturing, 2020, 2(3): 030201 https://doi.org/10.1088/2631-7990/aba495
8	F Z Fang . The three paradigms of manufacturing advancement. Journal of Manufacturing Systems, 2022, 63: 504–505 https://doi.org/10.1016/j.jmsy.2022.05.007
9	C Z Zeng , J Shen , C F Ma . Effect of diamond microparticles on the thermal behavior of low melting point metal: an experimental and numerical study. International Journal of Thermal Sciences, 2022, 178: 107613 https://doi.org/10.1016/j.ijthermalsci.2022.107613
10	J Wang , J D Xing , D Y Wang , H X Jiang . Research on the processing and performance of single crystal tin strips using a heated mould. Rare Metal Materials and Engineering, 2008, 9(37): 1160–1163
11	Z F Xue , M Lai , F F Xu , F Z Fang . Molecular dynamics study on surface formation and phase transformation in nanometric cutting of β-Sn. Advances in Manufacturing, 2022, 10(3): 356–367 https://doi.org/10.1007/s40436-022-00399-w
12	E Brinksmeier , R Gläbe , J Osmer . Ultra-precision diamond cutting of steel molds. CIRP Annals, 2006, 55(1): 551–554 https://doi.org/10.1016/S0007-8506(07)60480-6
13	E Brinksmeier , W Preuss , O Riemer , R Rentsch . Cutting forces, tool wear and surface ﬁnish in high speed diamond machining. Precision Engineering, 2017, 49: 293–304 https://doi.org/10.1016/j.precisioneng.2017.02.018
14	Z F Wang , J J Zhang , G Li , Z W Xu , H J Zhang , J G Zhang , A Hartmaier , F Z Fang , Y D Yan , T Sun . Anisotropy-related machining characteristics in ultra-precision diamond cutting of crystalline copper. Nanomanufacturing and Metrology, 2020, 3(2): 123–132 https://doi.org/10.1007/s41871-020-00060-9
15	S B Liang , C B Ke , C Wei , M B Zhou , X P Zhang . Phase field modeling of grain boundary migration and preferential grain growth driven by electric current stressing. Journal of Applied Physics, 2018, 124(17): 175109 https://doi.org/10.1063/1.5045637
16	M R Tonks , Y F Zhang , X M Bai , P C Millett . Demonstrating the temperature gradient impact on grain growth in UO2 using the phase field method. Materials Research Letters, 2014, 2(1): 23–28 https://doi.org/10.1080/21663831.2013.849300
17	T Omori , T Kusama , S Kawata , I Ohnuma , Y Sutou , Y Araki , K Ishida , R Kainuma . Abnormal grain growth induced by cyclic heat treatment. Science, 2013, 341(6153): 1500–1502 https://doi.org/10.1126/science.1238017
18	X Z Xu , Z H Zhang , J C Dong , D Yi , J J Niu , M H Wu , L Lin , R K Yin , M Q Li , J Y Zhou , S X Wang , J L Sun , X J Duan , P Gao , Y Jiang , X S Wu , H L Peng , R S Ruoff , Z F Liu , D P Yu , E G Wang , F Ding , K H Liu . Ultrafast epitaxial growth of metre-sized single-crystal graphene on industrial Cu foil. Science Bulletin, 2017, 62(15): 1074–1080 https://doi.org/10.1016/j.scib.2017.07.005
19	G H Zhang , X F Lu , J Q Li , J Chen , X Lin , M Wang , H Tan , W D Huang . In-situ grain structure control in directed energy deposition of Ti6Al4V. Additive Manufacturing, 2022, 55: 102865 https://doi.org/10.1016/j.addma.2022.102865
20	P Fernandez-Zelaia , M M Kirka , A M Rossy , Y Lee , S N Dryepondt . Nickel-based superalloy single crystals fabricated via electron beam melting. Acta Materialia, 2021, 216: 117133 https://doi.org/10.1016/j.actamat.2021.117133
21	X C Zhang , W Li , F Liou . Additive manufacturing of cobalt-based alloy on tool steel by directed energy deposition. Optics & Laser Technology, 2022, 148: 107738 https://doi.org/10.1016/j.optlastec.2021.107738
22	Y Li , Y F Yu , Z B Wang , X Y Liang , W B Kan , F Lin . Additive manufacturing of nickel-based superalloy single crystals with IN-738 alloy. Acta Metallurgica Sinica, 2022, 35(3): 369–374 https://doi.org/10.1007/s40195-021-01320-3
23	H Helmer , A Bauereiß , R F Singer , C Körner . Grain structure evolution in Inconel 718 during selective electron beam melting. Materials Science and Engineering: A, 2016, 668: 180–187 https://doi.org/10.1016/j.msea.2016.05.046
24	A Rai , H Helmer , C Körner . Simulation of grain structure evolution during powder bed based additive manufacturing. Additive Manufacturing, 2017, 13: 124–134 https://doi.org/10.1016/j.addma.2016.10.007
25	A B Murphy , D Uhrlandt . Foundations of high-pressure thermal plasmas. Plasma Sources Science & Technology, 2018, 27(6): 063001 https://doi.org/10.1088/1361-6595/aabdce
26	R Indhu , V Vivek , L Sarathkumar , A Bharatish , S Soundarapandian . Overview of laser absorptivity measurement techniques for material processing. Lasers in Manufacturing and Materials Processing, 2018, 5(4): 458–481 https://doi.org/10.1007/s40516-018-0075-1
27	J Paparao , S Murugan . Oxy-hydrogen gas as an alternative fuel for heat and power generation applications—a review. International Journal of Hydrogen Energy, 2021, 46(76): 37705–37735 https://doi.org/10.1016/j.ijhydene.2021.09.069
28	R L Li , Y G Li , H Deng . Plasma-induced atom migration manufacturing of fused silica. Precision Engineering, 2022, 76: 305–313 https://doi.org/10.1016/j.precisioneng.2022.04.005
29	Y Shibata , Y Sakairi , K Shimada , M Mizutani , T Kuriyagawa . Effects of topography and modified layer by plasma-shot treatment on high-speed steel. Nanomanufacturing and Metrology, 2020, 3(2): 133–141 https://doi.org/10.1007/s41871-020-00065-4
30	A Bennett , N Yu , M Castelli , G D Chen , A Balleri , T Urayama , F Z Fang . Characterisation of a microwave induced plasma torch for glass surface modification. Frontiers of Mechanical Engineering, 2021, 16(1): 122–132 https://doi.org/10.1007/s11465-020-0603-5
31	Y Zhang , L F Zhang , K Y Chen , D Z Liu , D Lu , H Deng . Rapid subsurface damage detection of SiC using inductivity coupled plasma. International Journal of Extreme Manufacturing, 2021, 3(3): 035202 https://doi.org/10.1088/2631-7990/abff34
32	P Ji , H L Jin , D Li , X Su , K Liu , B Wang . Thermal modelling for conformal polishing in inductively coupled atmospheric pressure plasma processing. Optik, 2019, 182: 415–423 https://doi.org/10.1016/j.ijleo.2019.01.049
33	E A Ayyıldız , M Ayyıldız , F Kara . Optimization of surface roughness in drilling medium-density fiberboard with a parallel robot. Advances in Materials Science and Engineering, 2021, 2021: 6658968 https://doi.org/10.1155/2021/6658968
34	P Lyu , M Lai , Z Liu , F Z Fang . Ultra-smooth finishing of single-crystal lutetium oxide by plasma-assisted etching. Precision Engineering, 2021, 67: 77–88 https://doi.org/10.1016/j.precisioneng.2020.09.013
35	R E Napolitano , R J Schaefer . The convergence-fault mechanism for low-angle boundary formation in single-crystal castings. Journal of Materials Science, 2000, 35(7): 1641–1659 https://doi.org/10.1023/A:1004747612160
36	N Stanford , A Djakovic , B A Shollock , M Mclean , N D Souza , P A Jennings . Seeding of single crystal superalloys—role of seed melt-back on casting defects. Scripta Materialia, 2004, 50(1): 159–163 https://doi.org/10.1016/j.scriptamat.2003.08.029
37	H K Jo , H J Park , H J Lee , G Bae , D S Song , K K Kim , W Song , C Jeon , K S An , Y K Kwon , C Y Park . Oxygen-mediated selection of Cu crystallographic orientation for growth of single-crystalline graphene. Applied Surface Science, 2022, 584: 152585 https://doi.org/10.1016/j.apsusc.2022.152585
38	L Z Sun , B H Chen , W D Wang , Y L Z Li , X Z Zeng , H Y Liu , Y Liang , Z Y Zhao , A L Cai , R Zhang , Y S Zhu , Y C Wang , Y Q Song , Q J Ding , X Gao , H L Peng , Z Y Li , L Lin , Z F Liu . Toward epitaxial growth of misorientation-free graphene on Cu (111) foils. ACS Nano, 2022, 16(1): 285–294 https://doi.org/10.1021/acsnano.1c06285
39	E Nas, F Kara. Optimization of EDM machinability of hastelloy C22 super alloys. Machines, 2022, 10(12): 1131 https://doi.org/10.3390/machines10121131
40	B Zhang , J F Yin . The skin effect of subsurface damage distribution in materials subjected to high-speed machining. International Journal of Extreme Manufacturing, 2019, 1(1): 012007 https://doi.org/10.1088/2631-7990/ab103b
41	Z F Wang , J J Zhang , J G Zhang , G Li , H J Zhang , H ul Hassan , A Hartmaier , Y D Yan , T Sun . Towards an understanding of grain boundary step in diamond cutting of polycrystalline copper. Journal of Materials Processing Technology, 2020, 276: 116400 https://doi.org/10.1016/j.jmatprotec.2019.116400
42	G Xie , S H Zhang , W Zheng , G Zhang , J Shen , Y Z Lu , H Q Hao , L Wang , L H Lou , J Zhang . Formation and evolution of low angle grain boundary in large-scale single crystal superalloy blade. Acta Metallurgica Sinica, 2019, 55(12): 1527–1536 https://doi.org/10.11900/0412.1961.2019.00090
43	K Y You , F Z Fang , G P Yan . Surface generation of tungsten carbide in laser-assisted diamond turning. International Journal of Machine Tools and Manufacture, 2021, 168: 103770 https://doi.org/10.1016/j.ijmachtools.2021.103770
44	S Mitsui , T Sasaki , Y Arai , T Miyoshi , R Nishimura . Development of Debye-ring measurement system using SOI pixel detector. Nuclear Instruments & Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2019, 924: 441–447 https://doi.org/10.1016/j.nima.2018.09.147
45	X Wu , L Li , N He , C J Yao , M Zhao . Influence of the cutting edge radius and the material grain size on the cutting force in micro cutting. Precision Engineering, 2016, 45: 359–364 https://doi.org/10.1016/j.precisioneng.2016.03.012
46	F Nazari , M Honarpisheh , H Y Zhao . The effect of microstructure parameters on the residual stresses in the ultrafine-grained sheets. Micron, 2020, 132: 102843 https://doi.org/10.1016/j.micron.2020.102843
47	R J Ji , Q Zheng , Y H Liu , H Jin , F Zhang , S G Liu , B K Wang , S C Lu , B P Cai , X P Li . Effect of grain refinement on cutting force of difficult-to-cut metals in ultra-precision machining. Chinese Journal of Aeronautics, 2022, 35(3): 484–493 https://doi.org/10.1016/j.cja.2021.08.032

[1]	Gang SHEN, Jufan ZHANG, David CULLITON, Ruslan MELENTIEV, Fengzhou FANG. Tribological study on the surface modification of metal-on-polymer bioimplants[J]. Front. Mech. Eng., 2022, 17(2): 26-.
[2]	Jiqiang WU, Liqin WANG, Zhen LI, Peng LIU, Chuanwei ZHANG. Thermal analysis of lubricated three-dimensional contact bodies considering interface roughness[J]. Front. Mech. Eng., 2022, 17(2): 16-.
[3]	Zequan YAO, Chang FAN, Zhao ZHANG, Dinghua ZHANG, Ming LUO. Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece[J]. Front. Mech. Eng., 2021, 16(4): 855-867.
[4]	Zhigang DONG, Qian ZHANG, Haijun LIU, Renke KANG, Shang GAO. Effects of taping on grinding quality of silicon wafers in backgrinding[J]. Front. Mech. Eng., 2021, 16(3): 559-569.
[5]	Arun KRISHNAN, Fengzhou FANG. Review on mechanism and process of surface polishing using lasers[J]. Front. Mech. Eng., 2019, 14(3): 299-319.
[6]	Rupesh CHALISGAONKAR, Jatinder KUMAR. Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept[J]. Front Mech Eng, 2013, 8(2): 201-214.
[7]	CHEN Hao-sheng, CHEN Da-rong, WANG Jia-dao, LI Yong-jian. Calculated journal bearing lubrication of non-Newtonian medium with surface roughness effects[J]. Front. Mech. Eng., 2006, 1(3): 270-275.

Viewed

Full text

Abstract

Cited

Shared

Discussed