Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method

Nan Zhang; Yaoyao Shi

doi:10.1177/0954406219848473

Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method

Nan Zhang, Yaoyao Shi

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Blisk is a key component of new aero-engines. To improve machining efficiency, disc milling is used for roughing blisk tunnels. The multi-objective optimization is employed to optimize disc milling process. In this study, an integration-based approach that used grey relational analysis (GRA) coupled with radial basis function (RBF) neural network and particle swarm optimization (PSO) algorithm is applied to solve the optimization problem. To achieve smaller cutting force and greater material removal rate (MRR), the appropriate cutting speed, feed rate per tooth, and cutting height needed to be determined. A hybrid experiment scheme of three factors–five levels is carried out to generate data sample. Results of verified experiments indicate that GRA–RBF–PSO approach can improve performance better than original GRA.

Original language	English
Pages (from-to)	5556-5567
Number of pages	12
Journal	Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Volume	233
Issue number	16
DOIs	https://doi.org/10.1177/0954406219848473
State	Published - 1 Aug 2019

Keywords

blisk tunnels
Disc milling
GRA–RBF–PSO
grey relational grade
optimization
process parameters

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10.1177/0954406219848473

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title = "Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method",

abstract = "Blisk is a key component of new aero-engines. To improve machining efficiency, disc milling is used for roughing blisk tunnels. The multi-objective optimization is employed to optimize disc milling process. In this study, an integration-based approach that used grey relational analysis (GRA) coupled with radial basis function (RBF) neural network and particle swarm optimization (PSO) algorithm is applied to solve the optimization problem. To achieve smaller cutting force and greater material removal rate (MRR), the appropriate cutting speed, feed rate per tooth, and cutting height needed to be determined. A hybrid experiment scheme of three factors–five levels is carried out to generate data sample. Results of verified experiments indicate that GRA–RBF–PSO approach can improve performance better than original GRA.",

keywords = "blisk tunnels, Disc milling, GRA–RBF–PSO, grey relational grade, optimization, process parameters",

author = "Nan Zhang and Yaoyao Shi",

note = "Publisher Copyright: {\textcopyright} IMechE 2019.",

year = "2019",

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doi = "10.1177/0954406219848473",

language = "英语",

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Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method. / Zhang, Nan; Shi, Yaoyao.
In: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 233, No. 16, 01.08.2019, p. 5556-5567.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method

AU - Zhang, Nan

AU - Shi, Yaoyao

N1 - Publisher Copyright: © IMechE 2019.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Blisk is a key component of new aero-engines. To improve machining efficiency, disc milling is used for roughing blisk tunnels. The multi-objective optimization is employed to optimize disc milling process. In this study, an integration-based approach that used grey relational analysis (GRA) coupled with radial basis function (RBF) neural network and particle swarm optimization (PSO) algorithm is applied to solve the optimization problem. To achieve smaller cutting force and greater material removal rate (MRR), the appropriate cutting speed, feed rate per tooth, and cutting height needed to be determined. A hybrid experiment scheme of three factors–five levels is carried out to generate data sample. Results of verified experiments indicate that GRA–RBF–PSO approach can improve performance better than original GRA.

AB - Blisk is a key component of new aero-engines. To improve machining efficiency, disc milling is used for roughing blisk tunnels. The multi-objective optimization is employed to optimize disc milling process. In this study, an integration-based approach that used grey relational analysis (GRA) coupled with radial basis function (RBF) neural network and particle swarm optimization (PSO) algorithm is applied to solve the optimization problem. To achieve smaller cutting force and greater material removal rate (MRR), the appropriate cutting speed, feed rate per tooth, and cutting height needed to be determined. A hybrid experiment scheme of three factors–five levels is carried out to generate data sample. Results of verified experiments indicate that GRA–RBF–PSO approach can improve performance better than original GRA.

KW - blisk tunnels

KW - Disc milling

KW - GRA–RBF–PSO

KW - grey relational grade

KW - optimization

KW - process parameters

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DO - 10.1177/0954406219848473

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VL - 233

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JO - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

JF - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

IS - 16

ER -

Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method

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Keywords

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