Restricted accessResearch articleFirst published online 2025
Prediction of white layer thickness with machining time in milling of nickel-based superalloy Inconel 718 considering time-varying tool wear and tool temperature effect
White layer has a significant impact on the surface quality of workpieces, so the accurate prediction of white layer thickness (WLT) is crucial. However, the present prediction models ignore the situation that WLT changes with machining time due to time-varying tool wear and tool temperature effect. Hence, a WLT prediction method during the milling process of Inconel 718 considering tool wear and tool temperature changes with machining time was presented in this paper. Firstly, a method was put forward to predict tool wear and tool temperature with machining time. Then, a finite element simulation model with obtained tool wear and tool temperature was set up to calculate WLT on the basis of dynamic recrystallization (DRX) mechanism. Finally, the prediction results were validated by the results of milling experiments. The average error of the proposed method was 12.36%. The results show the effectiveness of the proposed WLT prediction approach for Inconel 718, providing theoretical guidance for optimal machining time of each tool and cutting-parameter optimization.
YinQLiuZWangB, et al. Recent progress of machinability and surface integrity for mechanical machining Inconel 718: a review. Int J Adv Manuf Technol2020; 109: 215–245.
2.
Wusatowska-SarnekAMDubielBCzyrska-FilemonowiczA, et al. Microstructural characterization of the white etching layer in nickel-based superalloy. Metall Mater Trans A2011; 42A: 3813–3825.
3.
YangYYFangHSHuangWG.A study on wear resistance of the white layer. Tribol Int1996; 29: 425–428.
4.
ZhangSLiuZWangB, et al. Phase transition and dynamic recrystallization mechanisms of white layer formation during turning superalloy Inconel 718. J Mater Res Technol2021; 15: 5288–5296.
5.
ZhangBFShenWCLiuYJ, et al. Microstructures of surface white layer and internal white adiabatic shear band. Wear1997; 211: 164–168.
6.
UmbrelloD.Analysis of the white layers formed during machining of hardened AISI 52100 steel under dry and cryogenic cooling conditions. Int J Adv Manuf Technol2013; 64: 633–642.
7.
RanganathSGuoCHegdeP.A finite element modeling approach to predicting white layer formation in nickel superalloys. CIRP Ann Manuf Technol2009; 58: 77–80.
8.
PoulachonGAlbertASchluraffM, et al. An experimental investigation of work material microstructure effects on white layer formation in PCBN hard turning. Int J Mach Tools Manuf2005; 45: 211–218.
9.
HosseiniSBKlementUYaoY, et al. Formation mechanisms of white layers induced by hard turning of AISI 52100 steel. Acta Mater2015; 89: 258–267.
10.
RameshAMelkoteSN.Modeling of white layer formation under thermally dominant conditions in orthogonal machining of hardened AISI 52100 steel. Int J Mach Tools Manuf2008; 48: 402–414.
11.
DuanCKongWHaoQ, et al. Modeling of white layer thickness in high speed machining of hardened steel based on phase transformation mechanism. Int J Adv Manuf Technol2013; 69: 59–70.
12.
DuanCZhangFQinS, et al. Modeling of dynamic recrystallization in white layer in dry hard cutting by finite element–cellular automaton method. J Mech Sci Technol2018; 32: 4299–4312.
13.
ArfaouiSZemzemiFTourkiZ.Relationship between cutting process parameters and white layer thickness in orthogonal cutting. Mater Manuf Process2018; 33: 661–669.
14.
NayakRKBartaryaGSahooMR.Numerical analysis of the effect of tool wear on surface integrity during hard turning. J Mech Sci Technol2021; 35: 1215–1222.
15.
TekkayaBMeurerMDoelzM, et al. Modeling of microstructural workpiece rim zone modifications during hard machining. J Mater Process Technol2023; 311: 117815.
16.
ChouYKSongH.Thermal modeling for white layer predictions in finish hard turning. Int J Mach Tools Manuf2005; 45: 481–495.
17.
YanLYangWJinH, et al. Analytical modelling of microstructure changes in the machining of 304 stainless steel. Int J Adv Manuf Technol2012; 58: 45–55.
18.
ZengHYanRHuT, et al. Analytical modeling of white layer formation in orthogonal cutting of AerMet100 steel based on phase transformation mechanism. J Manuf Sci Eng Trans ASME2019; 141.
19.
ZemzemiFKhochtaliHBen SalemW, et al. Analytical multi-physics model of microstructure changes in hard turning of AISI 52100 steel: prediction of thicknesses of white and dark layers. Int J Adv Manuf Technol2021; 112: 2755–2771.
20.
ShiJWangJ-YLiuCR.Modelling white layer thickness based on the cutting parameters of hard machining. Proc Inst Mech Eng Part B: J Eng Manuf2006; 220: 119–128.
21.
LiLLaiDJiQ, et al. Influence of tool characteristics on white layer produced by cutting hardened steel and prediction of white layer thickness. Int J Adv Manuf Technol2021; 113: 1215–1228.
22.
XuDLiuYDingL, et al. Experimental and numerical investigation of Inconel 718 machining with worn tools. J Manuf Process2022; 77: 163–173.
23.
LiangXLiuZWangB.State-of-the-art of surface integrity induced by tool wear effects in machining process of titanium and nickel alloys: a review. Measurement2019; 132: 150–181.
24.
la MonacaAAxinteDALiaoZ, et al. Temperature-dependent shear localisation and microstructural evolution in machining of nickel-base superalloys. Mater Des2022; 219.
25.
KaraguzelU.Transient multi-domain thermal modeling of interrupted cutting with coated tools. Int J Adv Manuf Technol2021; 116: 345–361.
26.
ZhangZLiuZRenX, et al. Prediction of tool wear rate and tool wear during dry orthogonal cutting of Inconel 718. Metals2023; 13.
27.
OsmondLCookICurtisD, et al. Tool life and wear mechanisms of CVD coated and uncoated SiAlON ceramic milling inserts when machining aged Inconel 718. Proc Inst Mech Eng Part B: J Eng Manuf2024; 238: 1069–1083.
28.
LuoXChengKHoltR, et al. Modeling flank wear of carbide tool insert in metal cutting. Wear2005; 259: 1235-1240.
29.
FengYHsuF-CLuY-T, et al. Tool wear rate prediction in ultrasonic vibration-assisted milling. Mach Sci Technol2020; 24: 758–780.
30.
YueCLiXLiuX, et al. Wear behavior of tool flank in the side milling of Ti6Al4V: an analytical model and experimental validation. Proc Inst Mech Eng Part C: J Mech Eng Sci2022; 236: 1631–1644.
31.
KomanduriRHouZB.Thermal modeling of the metal cutting process – Part I – Temperature rise distribution due to shear plane heat source. Int J Mech Sci2000; 42: 1715–1752.
32.
CuiDZhangDWuB, et al. An investigation of tool temperature in end milling considering the flank wear effect. Int J Mech Sci2017; 131: 613–624.
33.
LinBWangLGuoY, et al. Modeling of cutting forces in end milling based on oblique cutting analysis. Int J Adv Manuf Technol2016; 84: 727–736.
34.
DikshitMK.Determination of force coefficient based on instantaneous forces and linear mechanistic model in ball end milling. Proc Inst Mech Eng Part B: J Eng Manuf2023; 237: 1704–1715.
35.
FuZYangWWangX, et al. Analytical modelling of milling forces for helical end milling based on a predictive machining theory. In: 15th CIRP Conference on Modelling of Machining Operations (CMMO), Karlsruhe, Germany, 2015 Jun 11–12, pp. 258–263.
36.
ChenDZhangXXieY, et al. A unified analytical cutting force model for variable helix end mills. Int J Adv Manuf Technol2017; 92: 3167–3185.
37.
HouYZhangDWuB, et al. Milling force modeling of worn tool and tool flank wear recognition in end milling. IEEE/ASME Trans Mechatron2015; 20: 1024–1035.
38.
WangSQLiJGHeCL, et al. An analytical model of residual stress in orthogonal cutting based on the radial return method. J Mater Process Technol2019; 273.
39.
MaoJZhaoMWeiX, et al. A material constitutive model-based prediction method for flank milling force considering the deformation of workpiece. J Manuf Process2022; 84: 403–413.
40.
ShawMCCooksonJ.Metal cutting principles. Oxford University Press, 2005.
41.
UllahIZhangSZhangQ, et al. Numerical investigation on serrated chip formation during high-speed milling of Ti-6Al-4V alloy. J Manuf Process2021; 71: 589–603.
42.
JiangFLiuZWanY, et al. Experimental investigation of cutting tool temperature during slot milling of AerMet 100 steel. Proc Inst Mech Eng Part B: J Eng Manuf2016; 230: 838–847.
43.
YanSZhuDZhuangK, et al. Modeling and analysis of coated tool temperature variation in dry milling of Inconel 718 turbine blade considering flank wear effect. J Mater Process Technol2014; 214: 2985–3001.
44.
LiXWangYGuoK, et al. Simulation analysis of cutting process for Inconel 718 nickel-based superalloy. Int J Adv Manuf Technol2021; 114: 2721–2738.
45.
ChenX-MLinYCWenD-X, et al. Dynamic recrystallization behavior of a typical nickel-based superalloy during hot deformation. Mater Des2014; 57: 568–577.
46.
HuangDWuWLambertD, et al. Computer simulation of microstructure evolution during hot forging of Waspaloy and nickel alloy 718. SFTC Paper2001; 368.
47.
LiangJGaoHLiD, et al. Study on milling tool wear morphology and mechanism during machining superalloy GH4169 with PVD-TiAlN coated carbide tool. Tribol Int2023; 182.
48.
LiJGaoDLiaoZ, et al. The influence of material anisotropy on the machinability of laser additive manufactured stainless steel 316L. J Manuf Process2025; 145: 581–599.
49.
ZhangSLiJFWangYW.Tool life and cutting forces in end milling Inconel 718 under dry and minimum quantity cooling lubrication cutting conditions. J Clean Prod2012; 32: 81–87.
50.
LiJGaoDLuY, et al. Temperature effect on adiabatic shear phenomenon and microstructure evolution of directional energy deposition 316L stainless steel. Mater Charact2025; 224: 115079.
