Numerical simulation and optimization in pulsed Nd: YAG laser welding of Hastelloy C-276 through Taguchi method and artificial neural network

Bagchi, Ashutosh; Saravanan, S.; Kumar, Girendra; Murugan, Gopal; Raghukandan, K.

doi:10.1016/j.ijleo.2017.08.082

Cited by 32 publications

(6 citation statements)

References 15 publications

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“…On the contrary, an increase in the pulse energy decreases the width of the joint. In regression analysis, the regression coefficient (R 2 ) is 0.96, indicating the fit of experimental data is satisfactory [11]. The P-value (0.001), in the ANOVA table, indicates that the linear and squared terms are significant at 95 % confidence level (Table 3).…”

Section: Fig1 Main Effect Plot For Attaining Minimum Weld Widthmentioning

confidence: 92%

Optimization of Process Parameters in Nd: Yag Laser Welding of Hastelloy Sheets Through Taguchi Method

Saravanan

2019

Journal of Manufacturing Engineering

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Optimization of weld width and tensile strength in pulsed Nd: YAG laser welded Hastelloy C-276 sheets, subjected to varied welding speed (350-450 mm/min), pulse energy (10-14 J) and pulse duration (6-8 ms), is attempted. Experimental conditions are designed based on Taguchi L9 orthogonal array. The parameters for attaining a minimum weld width and maximum tensile strength were determined by computing the Signal-to-Noise ratio. Further, a mathematical model is developed for determining the weld width and tensile strength of the weld, based on the regression analysis using statistical software MINITAB-16 and the level of fit are determined by analysis of variance.

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Section: Fig1 Main Effect Plot For Attaining Minimum Weld Widthmentioning

confidence: 92%

Optimization of Process Parameters in Nd: Yag Laser Welding of Hastelloy Sheets Through Taguchi Method

Saravanan

2019

Journal of Manufacturing Engineering

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“…To achieve a minimum response time of microfluidic biosensor, the influence of process parameters in terms of signal-to-noise ratio (SNR) is determined based on lower the better criteria since we want to minimize the detection time of the biosensor. The SNR for smaller the better is given by the following equation [46,47]:…”

Section: Analysis Of Variancementioning

confidence: 99%

Numerical optimization of microfluidic biosensor detection time for the SARS-CoV-2 using the Taguchi method

et al. 2023

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The performance of microfluidic biosensor of the SARS-Cov-2 was numerically analyzed through finite element method. The calculation results have been validated with comparison with experimental data reported in the literature. The novelty of this study is the use of the Taguchi method in the optimization analysis, and an L8(2 5 ) orthogonal table of five critical parameters-Reynolds number (Re), Damko ¨hler number (Da), relative adsorption capacity (r), equilibrium dissociation constant (K D ), and Schmidt number (Sc), with two levels was designed. ANOVA methods are used to obtain the significance of key parameters. The optimal combination of the key parameters is Re = 10 -2 , Da = 1000, r = 0.2, K D = 5, and Sc 10 4 to achieve the minimum response time (0.15). Among the selected key parameters, the relative adsorption capacity (r) has the highest contribution (42.17%) to the reduction of the response time, while the Schmidt number (Sc) has the lowest contribution (5.19%). The presented simulation results are useful in designing microfluidic biosensors in order to reduce their response time.

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“…The effect of optimization factor in terms of signal-to-noise ratio (S/N) to reach minimum response of microfluidic biosensor is achieved based on lower the better criteria given by this equation [40,41] (Eq. ( 10)):…”

Section: Analysis Of Variancementioning

confidence: 99%

Taguchi optimization of integrated flow microfluidic biosensor for COVID-19 detection

et al. 2022

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In this research, Taguchi's method was employed to optimize the performance of a microfluidic biosensor with an integrated flow confinement for rapid detection of the SARS-CoV-2. The finite element method was used to solve the physical model which has been first validated by comparison with experimental results. The novelty of this study is the use of the Taguchi approach in the optimization analysis. An L8 2 7 orthogonal array of seven critical parameters-Reynolds number (Re), Damköhler number (Da), relative adsorption capacity (σ ), equilibrium dissociation constant (K D ), Schmidt number (Sc), confinement coefficient (α) and dimensionless confinement position (X), with two levels was designed. Analysis of variance (ANOVA) methods are also used to calculate the contribution of each parameter. The optimal combination of these key parameters was Re 10 -2 , Da 1000, σ 0.5, K D 5, Sc 10 5 , α 2 and X 2 to achieve the lowest dimensionless response time (0.11). Among the all-optimization factors, the relative adsorption capacity (σ ) has the highest contribution (37%) to the reduction of the response time, while the Schmidt number (Sc) has the lowest contribution (7%).

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Numerical simulation and optimization in pulsed Nd: YAG laser welding of Hastelloy C-276 through Taguchi method and artificial neural network

Cited by 32 publications

References 15 publications

Optimization of Process Parameters in Nd: Yag Laser Welding of Hastelloy Sheets Through Taguchi Method

Optimization of Process Parameters in Nd: Yag Laser Welding of Hastelloy Sheets Through Taguchi Method

Numerical optimization of microfluidic biosensor detection time for the SARS-CoV-2 using the Taguchi method

Taguchi optimization of integrated flow microfluidic biosensor for COVID-19 detection

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