Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm

Hou, Tung-Hsu; Su, Chi-Hung; Liu, Wanglin

doi:10.1016/j.powtec.2006.11.019

Cited by 179 publications

(78 citation statements)

References 17 publications

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“…Taguchi Method uses an orthogonal design to elicit the interaction between parameters and the performance of the optimisation process. This involves using the signal-to-noise ratio to analyse the experimental data and find the optimal parameter combinations [26]. Four main parameters of GA (i.e.…”

Section: 2taguchi-based Ga Parameter Tuningmentioning

confidence: 99%

ANN–GA smart appliance scheduling for optimised energy management in the domestic sector

Yüce

Rezgui

Mourshed

2016

Energy and Buildings

125

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Section: 2taguchi-based Ga Parameter Tuningmentioning

confidence: 99%

ANN–GA smart appliance scheduling for optimised energy management in the domestic sector

Yüce

Rezgui

Mourshed

2016

Energy and Buildings

125

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“…The response surfaces clearly reveal the optimal response point. RSM is used to find the optimal set of process parameters that produce a maximum or minimum value of the response [16]. In the present investigation the process parameters corresponding to the maximum weld penetration are considered as optimum (analyzing the contour graphs and by solving Eq.…”

Section: Optimizing Parametersmentioning

confidence: 99%

Modelling and Optimization of Gas Metal Arc Welding Using Response Surface Methodology

2016

Arimpie - 2016

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Abstract-GMAW is a complicated process with many interrelated parameters affecting the quality of the welded joint. The process variables with the maximum influence are the welding current, welding voltage, travel speed and arc efficiency. Response surface methodology was used to predict the weld penetration of gas metal arc welded mild steel. The experiments were conducted based on three factors, three-level, and central composite face centered design with full replications technique, and mathematical model was developed. The results obtained through response surface methodology were verified through a confirmation experiment.The welding parameters such as speed, welding current and voltage have been optimized for material AISI 1020 using GMAW process. To fortify the robustness of experimental design, a confirmation test was performed at selected optimal process parameter setting. Observations from this method may be useful for automotive sub-assemblies, shipbuilding and vessel fabricators and operators to obtain optimal welding conditions. Keywords-Gas metal arc welding, penetration, response surface methodology,central composite design.

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“…GA and Taguchi method on setting the parameters for P c , P m and P s were incorporated in overcoming the economic power dispatch problem (Younes and Rahli, 2006). Last but not least, the Taguchi method was collaborated with response surface method and GA in parameters optimization was applied on a nano-particle wet milling process (Hou et al, 2007). Using the Taguchi SelfAdaptive Real-Coded Genetic Algorithm (TSARCGA), economic dispatch problem with valve-point loading was elucidated (Subbaraj et al, 2011).…”

Section: Taguchi Approachmentioning

confidence: 99%

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

Lim¹,

Sulaiman²,

Mustapha³

et al. 2015

Journal of Computer Science

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This study operates within experimental design with two main tools of Taguchi method namely orthogonal array and signal to noise ratio to discover the optimal parameter settings for newly proposed generational genetic algorithms; they are Laplace Crossover-Scale Truncated Pareto Mutation (LX-STPM) and Rayleigh Crossover-Scale Truncated Pareto Mutation (RX-STPM). It concluded that GA parameter settings are algorithms and problems dependent.

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Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm

Cited by 179 publications

References 17 publications

ANN–GA smart appliance scheduling for optimised energy management in the domestic sector

ANN–GA smart appliance scheduling for optimised energy management in the domestic sector

Modelling and Optimization of Gas Metal Arc Welding Using Response Surface Methodology

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

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