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Multi-objective robust evolutionary optimization of the boring process of AISI 4130 steel

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Abstract

Boring is widely applied to enlarge holes. The high L/D ratio of boring bars enables self-excited vibration, deteriorating the quality of the hole. Therefore, this work aims the multi-objective evolutionary robust optimization of the boring process. Robust parameter design is employed to achieve robust models for roughness and roundness concerning tool overhang length and borehole depth, set as noise variables. These models aid the attainment of control factors’ levels, i.e., feed, cutting speed, and fixture position, which turn the responses less sensitive to noise. The robust models together with the material removal rate deterministic model are optimized through evolutionary multi-objective methods. The effects of process and noise factors are discussed considering literature. The multi-objective evolutionary optimization of the robust models helps to achieve these robust levels of process factors besides balancing the trade-off between the outcomes. The multi-objective robust evolutionary results outperform the scalarization approach considered for comparison purposes.

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Data availability

The experimental results necessary to reproduce all analyses are provided in the paper.

Abbreviations

RPD:

Robust parameter design

GRA:

Grey relational analysis

RSM:

Response surface methodology

MOEA:

Multi-objective evolutionary

NSGA:

Non-dominated sorting genetic algorithm

MOPSO:

Multi-objective particle swarm optimization

E-HHPS:

Electric–×hydraulic hybrid power steering

NBI:

Normal boundary intersection

NNC:

Normalized normal constraint

ENNC:

Enhanced normalized normal constraint

GA:

Genetic algorithm

MSE:

Mean square error

MRR:

Material removal rate

PSO:

Particle swarm optimization

MOPSO-CD:

Multi-objective particle swarm optimization with crowding distance

FCD:

Face-centered central composite design

ANOVA:

Analysis of variance

OLS:

Ordinary least squares

WLS:

Weighted least squares

RMSE:

Root mean square error

v c :

Cutting speed m/min

f :

Feed rate mm/ver

f p :

Fixture position mm

a p :

Cutting depth mm

l to :

Tool overhang length mm

l b :

Borehole depth mm

n F :

Number of factorial points –

n C :

Number of center points –

n A :

Number of axial points –

R a :

Average surface roughness μm

Ron t :

Total roundness μm

MRR:

Material removal rate mm3/min

X :

Vector of control factors

Z :

Vector of noise factors

β 0 :

Intercept

β :

Vector of linear coefficients of control factors

γ :

Vector of linear coefficients of noise factors

B :

Matrix of second-order terms of control factors

Δ :

Matrix of process × noise interaction terms

R 2 adj :

Adjusted multiple determination coefficient

T E[y] :

Target value for mean model

E[y]:

Mean function

Var[y]:

Variance function

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Funding

This research was supported by the Brazilian National Council for Scientific and Technological Development (CNPq), the Coordination of Superior Level Staff Improvement (CAPES), and the Research Support Foundation of the State of Minas Gerais (FAPEMIG).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Center for Innovation in Sustainable Manufacturing – CIMS, Federal University of São João del Rei – UFSJ, Praça Frei Orlando, 170, São João del Rei, Minas Gerais, Brazil

    Jéssica Tito Vieira, Robson Bruno Dutra Pereira, Samuel Alves Freitas, Carlos Henrique Lauro & Lincoln Cardoso Brandão

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  1. Jéssica Tito Vieira
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  2. Robson Bruno Dutra Pereira
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  3. Samuel Alves Freitas
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Contributions

Jéssica Tito Vieira: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, roles/writing - original draft, writing - review and editing.

Robson Bruno Dutra Pereira: formal analysis, methodology, software, validation, visualization, roles/writing - original draft, writing - review and editing.

Samuel Alves Freitas: data curation, investigation, validation, roles/writing - original draft, writing - review and editing.

Carlos Henrique Lauro: investigation, methodology, project administration, validation, roles/writing - original draft, writing - review and editing.

Lincoln Cardoso Brandão: conceptualization, data curation, funding acquisition, investigation, project administration, resources, supervision, validation, roles/writing - original draft, writing - review and editing.

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Correspondence to Robson Bruno Dutra Pereira.

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Vieira, J.T., Pereira, R.B.D., Freitas, S.A. et al. Multi-objective robust evolutionary optimization of the boring process of AISI 4130 steel. Int J Adv Manuf Technol 112, 1745–1765 (2021). https://doi.org/10.1007/s00170-020-06455-8

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