Prediction of Cutting Forces and Processing Parameter Optimization in C60 Minimum Quantity Lubrication Milling of GH4169

doi:10.3969/j.issn.1004-132X.2026.07.004

Abstract

Abstract:

Nickel-based superalloys were widely used in aerospace and other fields. However， significant challenges were in machining， including severe tool wear， poor surface integrity， and low processing efficiency， which could not be effectively mitigated by conventional lubrication techniques. To address these issues， C60 nanofluid cutting fluid was introduced to enhance lubrication at the tool-workpiece interfaces， thereby reducing friction and suppressing heat accumulation. A novel cutting force modeling approach was developed， incorporating the tribological properties and cooling effects of nanofluids. The model integrated oblique cutting theory， mirror heat source method， and Johnson-Cook constitutive equation to calculate cutting forces. Experimental results demonstrate that the proposed model accurately quantifies the synergistic effects of friction reduction and cooling enhancement under nanofluid lubrication， achieving an average prediction error of 6.73% for cutting forces. Notably， the cutting force peak is reduced by 19.6% under nanofluid lubrication compared to conventional cutting fluids. Furthermore， a multi-objective optimization strategy was proposed based on Pareto optimality and PSO. A comprehensive evaluation system was established considering machining efficiency and cutting forces. Optimization results show that the cutting force decreases by 5.7%， while machining efficiency increases by 36.33% after parameter optimization.

Key words: C60, nanofluid, cutting force prediction, particle swarm optimization（PSO）

摘要：

镍基高温合金在航空航天等领域应用广泛，但加工时刀具磨损严重、切削力大且效率低，传统润滑技术难以改善切削性能。针对上述问题，引入C60纳米流体切削液改善切削区润滑条件，降低刀-工界面摩擦并抑制热积累。构建考虑纳米流体摩擦特性和冷却效果的切削力建模方法，结合斜角切削理论、镜像热源法与Johnson-Cook本构方程计算切削力。铣削实验结果显示，该模型能量化纳米流体润滑下摩擦与冷却的协同作用，预测切削力平均误差为6.73%，纳米流体润滑下切削合力峰值较常规切削液降低19.6%。进一步提出基于Pareto最优解的粒子群优化算法多目标优化策略，构建加工效率与切削力的多目标评价体系，优化工艺参数后，切削合力减小了5.7%，加工效率提高了36.33%。

关键词: C60, 纳米流体, 切削力预测, 粒子群优化

CLC Number:

TG54

PAN Zhirong, SUN Hao, YAO Bin, CAI Zhiqin, LAN Qixin, ZHANG Jinhui. Prediction of Cutting Forces and Processing Parameter Optimization in C60 Minimum Quantity Lubrication Milling of GH4169[J]. China Mechanical Engineering, 2026, 37(7): 1562-1571.

潘志榕, 孙浩, 姚斌, 蔡志钦, 蓝启鑫, 张金辉. C60微量润滑铣削GH4169切削力预测及工艺参数优化[J]. 中国机械工程, 2026, 37(7): 1562-1571.

Figures/Tables 18

Fig.1 Schematic diagram of overall end milling cutter

Fig.2 Schematic diagram of undeformed chip thickness

Fig.3 Schematic diagram of oblique cutting

Fig.4 Schematic diagram of shear heat source

Fig.5 Schematic diagram of friction heat source in the second deformation zone

Fig.6 Schematic diagram of heat dissipation source

Fig.7 Milling test platform

Tab.1 Experimental parameters for milling nickel based high-temperature alloys

铣削加工参数	数值
切削速度 $v c$ /（m·min^-1）	40，60，80，100
每齿进给量 $f z$ /mm	0.02，0.04，0.06，0.08
轴向切深 $a p$ /mm	0.1，0.2，0.3，0.4
径向切深 $a e$ /mm	1，2，3，4

Tab.1 Experimental parameters for milling nickel based high-temperature alloys

铣削加工参数	数值
切削速度 $v c$ /（m·min^-1）	40，60，80，100
每齿进给量 $f z$ /mm	0.02，0.04，0.06，0.08
轴向切深 $a p$ /mm	0.1，0.2，0.3，0.4
径向切深 $a e$ /mm	1，2，3，4

Tab.2 Physical parameters of nickel based high-temperature alloy GH4169［26］

参数	数值
A/MPa	1290
B/MPa	895
C	0.016
m	1.55
n	0.526
密度/（g·cm^-3）	8.24
热导率/（W·m^-1·K^-1）	11.4

Fig.8 Friction coefficient curve

Fig.9 Cutting forces under different cutting parameters

Fig.10 Cutting temperature fields under different lubrication conditions

Tab.3 Errors between measured and predicted cutting temperatures

切削工况	实测切削温度/℃	预测切削温度/℃	误差/%
干切削	301.6	286.8	4.9
基础切削液	210.4	192.3	8.6
纳米流体切削液	165.8	152.5	8.0
平均值			7.2

Fig. 11 Cutting forces under different lubrication conditions

Tab.4 Errors between measured and predicted cutting force values

	切削力	实测值/N	预测值/N	误差/%
干切削	$F x$	407.56	390.59	4.17
	$F y$	161.73	155.20	4.04
	$F z$	-145.71	-126.91	12.89
基础切削液	$F x$	313.90	299.30	4.65
	$F y$	146.82	138.23	5.39
	$F z$	-87.62	-80.85	7.72
纳米流体切削液	$F x$	255.26	241.42	5.42
	$F y$	131.01	122.71	6.34
	$F z$	-68.67	-61.83	9.96
平均值				6.73

Tab.4 Errors between measured and predicted cutting force values

	切削力	实测值/N	预测值/N	误差/%
干切削	$F x$	407.56	390.59	4.17
	$F y$	161.73	155.20	4.04
	$F z$	-145.71	-126.91	12.89
基础切削液	$F x$	313.90	299.30	4.65
	$F y$	146.82	138.23	5.39
	$F z$	-87.62	-80.85	7.72
纳米流体切削液	$F x$	255.26	241.42	5.42
	$F y$	131.01	122.71	6.34
	$F z$	-68.67	-61.83	9.96
平均值				6.73

Fig.12 The Pareto frontier of PSO algorithm

Tab.5 The selected Pareto optimal solution

序号	切削速度/（m·min^-1）	每齿进给量/mm	轴向切深/mm	径向切深/mm	切削合力/N	R_MRR/ （mm³·min^-1）
1	100	0.071	0.15	3.43	260.65	581.4
2	100	0.058	0.23	2.84	273.65	602.98
3	100	0.065	0.19	3.15	280.64	622.03
4	96.06	0.070	0.22	2.78	288.52	654.55
5	96.63	0.071	0.28	2.18	290.63	666.5

Tab.6 Optimization results of cutting parameters

	切削合力/N	R_MRR/（mm³·min^-1）
优化前	295.02	458.38
优化后	278.2	624.92

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