Statistical and Numerical Approaches for Modeling and Optimizing Laser Micromachining Process-Review

Shadi M. Karazi; Mahmoud Moradi; Khaled Y. Benyounis

doi:10.1016/b978-0-12-803581-8.11650-9

Statistical and Numerical Approaches for Modeling and Optimizing Laser Micromachining Process-Review

Shadi M. Karazi, Mahmoud Moradi, Khaled Y. Benyounis

Research output: Contribution to Book/Report › Chapter › peer-review

Abstract

This article presents the modeling and optimization techniques commonly used in engineering applications especially in Laser Micromachining process. Design of Experiment DOE (Response Surface Method and Taguchi), Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particle swarm optimization (PSO) and mixed techniques are explained briefly. Furthermore, a review of laser micromachining processes parameters optimization was studied. Recent researches which used different approaches for modeling and optimization was presented.

Original language	English
Title of host publication	Reference Module in Materials Science and Materials Engineering
Publisher	Elsevier
ISBN (Print)	978-0-12-803581-8
DOIs	https://doi.org/10.1016/b978-0-12-803581-8.11650-9
Publication status	Published - 29 Apr 2019
Externally published	Yes

Keywords

Laser micromachining
modeling
optimization

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10.1016/b978-0-12-803581-8.11650-9

https://doras.dcu.ie/23411/2/CH0000_UN-%d6%a3_Karazi_v1.pdfLicence: CC BY-NC-ND

Cite this

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abstract = "This article presents the modeling and optimization techniques commonly used in engineering applications especially in Laser Micromachining process. Design of Experiment DOE (Response Surface Method and Taguchi), Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particle swarm optimization (PSO) and mixed techniques are explained briefly. Furthermore, a review of laser micromachining processes parameters optimization was studied. Recent researches which used different approaches for modeling and optimization was presented.",

keywords = "Laser micromachining, modeling, optimization",

author = "Karazi, {Shadi M.} and Mahmoud Moradi and Benyounis, {Khaled Y.}",

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AU - Moradi, Mahmoud

AU - Benyounis, Khaled Y.

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Y1 - 2019/4/29

N2 - This article presents the modeling and optimization techniques commonly used in engineering applications especially in Laser Micromachining process. Design of Experiment DOE (Response Surface Method and Taguchi), Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particle swarm optimization (PSO) and mixed techniques are explained briefly. Furthermore, a review of laser micromachining processes parameters optimization was studied. Recent researches which used different approaches for modeling and optimization was presented.

AB - This article presents the modeling and optimization techniques commonly used in engineering applications especially in Laser Micromachining process. Design of Experiment DOE (Response Surface Method and Taguchi), Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particle swarm optimization (PSO) and mixed techniques are explained briefly. Furthermore, a review of laser micromachining processes parameters optimization was studied. Recent researches which used different approaches for modeling and optimization was presented.

KW - Laser micromachining

KW - modeling

KW - optimization

UR - https://pureportal.coventry.ac.uk/en/publications/statistical-and-numerical-approaches-for-modeling-and-optimizing-laser-micromachining-processreview(8fc7c0e9-9be9-4632-adc0-d1f403f80362).html

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BT - Reference Module in Materials Science and Materials Engineering

PB - Elsevier

ER -

Statistical and Numerical Approaches for Modeling and Optimizing Laser Micromachining Process-Review

Abstract

Keywords

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