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Milling optimization of GH4169 nickel–based superalloy under minimal quantity lubrication condition based on multi-objective particle swarm optimization algorithm

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Abstract

The surface quality of machining will greatly affect the service life of components. In this paper, to reduce the surface roughness and cutting force of nickel-based superalloy during machining, the milling parameters of GH4169 nickel-base superalloy under micro-lubrication conditions were optimized. Based on the response surface method, the influence of milling parameters (spindle speed, feed per tooth, micro-lubricating oil, and micro-lubricating pressure) on the surface roughness and cutting force was analysed; through the analysis of variance, the significant interaction effect and the regularity between each index were obtained, and the optimization models of surface roughness, redial, and axial cutting force were established; the multi-objective particle swarm optimization algorithm was used to optimize the model based on crowding distance sorting; the optimal combination of process parameters was determined to reduce surface roughness and achieve less cutting force. The research results show that the feed per tooth and the air pressure have the most obvious influence on the surface roughness; the interaction effect of the feed per tooth and the oil volume has the most significant influence on the radial and axial cutting forces. The radial and axial cutting forces show a trend of increasing with the increase of the feed per tooth, and decreasing with the increase of the oil amount; the optimal parameter combination was used to carry out the machining experiment, and the surface roughness and diameter. The relative error between the measured value of tangential cutting force and the predicted value of the model is 8.14%, the surface of the specimen is good, and the lowest surface roughness and cutting force are obtained, which has certain guiding significance for the successful research on the side milling of GH4169 nickel-based superalloy.

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Abbreviations

\(Sa\) :

Surface roughness (\(\mathrm{\mu m}\))

\(Fx\) :

Radial cutting force (\(\mathrm{N}\))

\(Fy\) :

Tangential cutting force (\(\mathrm{N}\))

\(n\) :

Spindle speed (\(\mathrm{r}/\mathrm{min}\))

\(fz\) :

Feed per tooth (μm/z)

\(Q\) :

Micro-lubricating oil quantity (ml/min)

\(P\) :

Micro-lubricating air pressure (bar)

\({a}_{e}\) :

Radial cutting depth (\(\mathrm{mm}\))

\({a}_{p}\) :

Axial cutting depth (\(\mathrm{mm}\))

\(q\) :

Heat flux per unit area (\(\mathrm{W}/{\mathrm{m}}^{2}\))

\(h\) :

Coefficient of convective heat transfer (\(\mathrm{W}/{(\mathrm{m}}^{2}\mathrm{ K})\))

\(\Delta T\) :

Temperature difference (\(K\))

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. U1908222, 52174116, 52104087), Natural Science Foundation of Liaoning Province (Grant Nos. 20180550167), Key Projects of Education Department of Liaoning Province (Grant Nos. LJ2019ZL005, Nos. LJ2017ZL001), and Oversea Training Project of High Level Innovation Team of Liaoning Province (Grant Nos. 2018LNGXGJWPY-ZD001).

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Authors and Affiliations

  1. School of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China

    Chenguang Guo, Xiaodong Chen, Qiang Li, Guangshuo Ding, Haitao Yue & Jianzhuo Zhang

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  1. Chenguang Guo
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Contributions

Chenguang Guo: conceptualization, methodology, supervision, writing — review and editing. Xiaodong Chen: investigation, resources, writing — original draft. Qiang Li: formal analysis, software, investigation, writing — original draft. Guangshuo Ding: supervision, data curation. Haitao Yue and Jianzhuo Zhang: validation, resource.

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Correspondence to Chenguang Guo.

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Guo, C., Chen, X., Li, Q. et al. Milling optimization of GH4169 nickel–based superalloy under minimal quantity lubrication condition based on multi-objective particle swarm optimization algorithm. Int J Adv Manuf Technol 123, 3983–3994 (2022). https://doi.org/10.1007/s00170-022-10461-3

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