Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel

460 Ogledov
276 Prenosov
Izvoz citacije: ABNT
VAN, An-Le ;NGUYEN, Trung-Thanh .
Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 68, n.3, p. 155-165, march 2022. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/investigation-and-optimization-of-mql-system-parameters-in-the-roller-burnishing-process-of-hardened-steel/>. Date accessed: 04 feb. 2023. 
doi:http://dx.doi.org/10.5545/sv-jme.2021.7473.
Van, A., & Nguyen, T.
(2022).
Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel.
Strojniški vestnik - Journal of Mechanical Engineering, 68(3), 155-165.
doi:http://dx.doi.org/10.5545/sv-jme.2021.7473
@article{sv-jmesv-jme.2021.7473,
	author = {An-Le  Van and Trung-Thanh  Nguyen},
	title = {Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {68},
	number = {3},
	year = {2022},
	keywords = {internal burnishing; cylindricity; circularity; roughness; ANN; VCPSO; },
	abstract = {In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.},
	issn = {0039-2480},	pages = {155-165},	doi = {10.5545/sv-jme.2021.7473},
	url = {https://www.sv-jme.eu/sl/article/investigation-and-optimization-of-mql-system-parameters-in-the-roller-burnishing-process-of-hardened-steel/}
}
Van, A.,Nguyen, T.
2022 March 68. Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 68:3
%A Van, An-Le 
%A Nguyen, Trung-Thanh 
%D 2022
%T Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel
%B 2022
%9 internal burnishing; cylindricity; circularity; roughness; ANN; VCPSO; 
%! Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel
%K internal burnishing; cylindricity; circularity; roughness; ANN; VCPSO; 
%X In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.
%U https://www.sv-jme.eu/sl/article/investigation-and-optimization-of-mql-system-parameters-in-the-roller-burnishing-process-of-hardened-steel/
%0 Journal Article
%R 10.5545/sv-jme.2021.7473
%& 155
%P 11
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 68
%N 3
%@ 0039-2480
%8 2022-03-09
%7 2022-03-09
Van, An-Le, & Trung-Thanh  Nguyen.
"Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel." Strojniški vestnik - Journal of Mechanical Engineering [Online], 68.3 (2022): 155-165. Web.  04 Feb. 2023
TY  - JOUR
AU  - Van, An-Le 
AU  - Nguyen, Trung-Thanh 
PY  - 2022
TI  - Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2021.7473
KW  - internal burnishing; cylindricity; circularity; roughness; ANN; VCPSO; 
N2  - In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.
UR  - https://www.sv-jme.eu/sl/article/investigation-and-optimization-of-mql-system-parameters-in-the-roller-burnishing-process-of-hardened-steel/
@article{{sv-jme}{sv-jme.2021.7473},
	author = {Van, A., Nguyen, T.},
	title = {Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {68},
	number = {3},
	year = {2022},
	doi = {10.5545/sv-jme.2021.7473},
	url = {https://www.sv-jme.eu/sl/article/investigation-and-optimization-of-mql-system-parameters-in-the-roller-burnishing-process-of-hardened-steel/}
}
TY  - JOUR
AU  - Van, An-Le 
AU  - Nguyen, Trung-Thanh 
PY  - 2022/03/09
TI  - Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 68, No 3 (2022): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2021.7473
KW  - internal burnishing, cylindricity, circularity, roughness, ANN, VCPSO, 
N2  - In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.
UR  - https://www.sv-jme.eu/sl/article/investigation-and-optimization-of-mql-system-parameters-in-the-roller-burnishing-process-of-hardened-steel/
Van, An-Le, AND Nguyen, Trung-Thanh.
"Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 68 Number 3 (09 March 2022)

Avtorji

Inštitucije

  • Nguyen Tat Thanh University, Faculty of Engineering and Technology, Vietnam 1
  • Le Quy Don Technical University, Faculty of Mechanical Engineering, Vietnam 2

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
© The Authors 2022. CC BY 4.0 Int.

https://doi.org/10.5545/sv-jme.2021.7473

In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to improve the hole quality in the MQL-assisted burnishing process.

internal burnishing; cylindricity; circularity; roughness; ANN; VCPSO;