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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Abstract

During the actual high-speed machining process, it is necessary to reduce the energy consumption and improve the machined surface quality. However, the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments. Herein, a novel intelligent system is proposed for prediction and optimization. A novel adaptive neuro-fuzzy inference system (NANFIS) is proposed to predict the energy consumption and surface quality. In the NANFIS model, the membership functions of the inputs are expanded into: membership superior and membership inferior. The membership functions are varied based on the machining theory. The inputs of the NANFIS model are cutting parameters, and the outputs are the machining performances. For optimization, the optimal cutting parameters are obtained using the improved particle swarm optimization (IPSO) algorithm and NANFIS models. Additionally, the IPSO algorithm as a learning algorithm is used to train the NANFIS models. The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron. The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2% and 93.4%, respectively. The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models. Based on the IPSO algorithm and NANFIS models, the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency. It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes, thereby enabling sustainable and intelligent manufacturing.

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Abbreviations

a p :

Depth of cut

V :

Cutting speed

f r :

Feed per tooth

d :

Diameter of the cutting plate

F t :

Tangential force

P c :

Cutting power

R 2 :

Coefficient of determination

a r :

Radial depth of cut

f c :

Cutting power model

f s :

Surface roughness model

NANFIS:

Novel adaptive neural fuzzy inference system

SANFIS:

Standard adaptive neural fuzzy inference system

IPSO:

Improved particle swarm optimization

CANFIS:

Complex adaptive neural fuzzy inference system

CGI:

Compacted graphite iron

MRR:

Material removal rate

RMSE:

Root mean square error

ANFIS:

Adaptive neural fuzzy inference system

f M :

Material removal rate model

ANOVA:

Analysis of variance

a d :

Axial depth of cut

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Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (Grant No. 51675312).

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Authors and Affiliations

  1. Center for Advanced Jet Engineering Technologies, Key Laboratory of High-efficiency and Clean Mechanical Manufacture (Ministry of Education), National Experimental Teaching Demonstration Center for Mechanical Engineering (Shandong University), School of Mechanical Engineering, Shandong University, Jinan, People’s Republic of China

    Long-Hua Xu, Chuan-Zhen Huang, Jia-Hui Niu & Han-Lian Liu

  2. School of Mechanical and Manufacturing Engineering, The University of New South Wales (UNSW), Sydney, NSW, 2052, Australia

    Jun Wang

  3. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People’s Republic of China

    Xiao-Dan Wang

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  1. Long-Hua Xu
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  2. Chuan-Zhen Huang
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  3. Jia-Hui Niu
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  4. Jun Wang
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Correspondence to Chuan-Zhen Huang.

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Xu, LH., Huang, CZ., Niu, JH. et al. Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process. Adv. Manuf. 9, 388–402 (2021). https://doi.org/10.1007/s40436-020-00339-6

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  • DOI: https://doi.org/10.1007/s40436-020-00339-6

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