Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm

M. R. Razfar; M. Asadnia; M. Haghshenas; M. Farahnakian

doi:10.1243/09544054JEM1809

Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm

M. R. Razfar, M. Asadnia, M. Haghshenas, M. Farahnakian

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

29 Citations (Scopus)

Abstract

This paper presents an approach to the determination of the optimal cutting parameters to create minimum surface roughness levels in the face milling of X20Cr13 stainless steel. The proposed approach is to use a particle swarm optimization (PSO)-based neural network to create a predictive model for the surface roughness level that is based on experimental data collected on X20Cr13. The optimization problem is then solved using a PSO-based neural network for optimization system (PSONNOS). A good agreement is observed between the predicted surface roughness values and those obtained in experimental measurements performed using the predicted optimal machine settings. The PSONNOS is compared to the genetic algorithm optimized neural network system (GONNS).

Original language	English
Pages (from-to)	1645-1653
Number of pages	9
Journal	Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume	224
Issue number	11
DOIs	https://doi.org/10.1243/09544054JEM1809
Publication status	Published - 1 Nov 2010
Externally published	Yes

Keywords

artificial neural network
cutting parameters
face milling
particle swarm optimization
surface roughness

Access to Document

10.1243/09544054JEM1809

Cite this

@article{922dccb6b8ce4b549387706f0ccad5ad,

title = "Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm",

abstract = "This paper presents an approach to the determination of the optimal cutting parameters to create minimum surface roughness levels in the face milling of X20Cr13 stainless steel. The proposed approach is to use a particle swarm optimization (PSO)-based neural network to create a predictive model for the surface roughness level that is based on experimental data collected on X20Cr13. The optimization problem is then solved using a PSO-based neural network for optimization system (PSONNOS). A good agreement is observed between the predicted surface roughness values and those obtained in experimental measurements performed using the predicted optimal machine settings. The PSONNOS is compared to the genetic algorithm optimized neural network system (GONNS).",

keywords = "artificial neural network, cutting parameters, face milling, particle swarm optimization, surface roughness",

author = "Razfar, {M. R.} and M. Asadnia and M. Haghshenas and M. Farahnakian",

year = "2010",

month = nov,

day = "1",

doi = "10.1243/09544054JEM1809",

language = "English",

volume = "224",

pages = "1645--1653",

journal = "Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture",

issn = "0954-4054",

publisher = "SAGE Publications",

number = "11",

}

Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm. / Razfar, M. R.; Asadnia, M.; Haghshenas, M. et al.

In: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 224, No. 11, 01.11.2010, p. 1645-1653.

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

TY - JOUR

T1 - Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm

AU - Razfar, M. R.

AU - Asadnia, M.

AU - Haghshenas, M.

AU - Farahnakian, M.

PY - 2010/11/1

Y1 - 2010/11/1

N2 - This paper presents an approach to the determination of the optimal cutting parameters to create minimum surface roughness levels in the face milling of X20Cr13 stainless steel. The proposed approach is to use a particle swarm optimization (PSO)-based neural network to create a predictive model for the surface roughness level that is based on experimental data collected on X20Cr13. The optimization problem is then solved using a PSO-based neural network for optimization system (PSONNOS). A good agreement is observed between the predicted surface roughness values and those obtained in experimental measurements performed using the predicted optimal machine settings. The PSONNOS is compared to the genetic algorithm optimized neural network system (GONNS).

AB - This paper presents an approach to the determination of the optimal cutting parameters to create minimum surface roughness levels in the face milling of X20Cr13 stainless steel. The proposed approach is to use a particle swarm optimization (PSO)-based neural network to create a predictive model for the surface roughness level that is based on experimental data collected on X20Cr13. The optimization problem is then solved using a PSO-based neural network for optimization system (PSONNOS). A good agreement is observed between the predicted surface roughness values and those obtained in experimental measurements performed using the predicted optimal machine settings. The PSONNOS is compared to the genetic algorithm optimized neural network system (GONNS).

KW - artificial neural network

KW - cutting parameters

KW - face milling

KW - particle swarm optimization

KW - surface roughness

UR - http://www.scopus.com/inward/record.url?scp=78649486645&partnerID=8YFLogxK

U2 - 10.1243/09544054JEM1809

DO - 10.1243/09544054JEM1809

M3 - Article

AN - SCOPUS:78649486645

VL - 224

SP - 1645

EP - 1653

JO - Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

JF - Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

SN - 0954-4054

IS - 11

ER -

Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this