Genetic algorithm for optimization design of diffractive optical elements in laser beam shaping

Ye, Jianyu; Yuan, Xiaocong; Zhou, Guangya

doi:10.1117/12.446537

Cited by 8 publications

(2 citation statements)

References 1 publication

(1 reference statement)

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“…After a training process, the neural network can become aware of similarities from new input patterns [26]. There have been many science publications about the use of AI in analyzing mechanical behaviors, including design of experiments [27], genetic algorithms [28], ant colony optimization [29], fuzzy logic [30], and finite elements analysis [31]. Mohsen and Mazahery [32] employed a standard feed-forward network with a hidden layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

New Numerical Results from Simulations of Beams and Space Frame Systems with a Tuned Mass Damper

Tho

Thom

2019

Materials

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In working processes, mechanical systems are often affected by both internal and external forces, which are the cause of the forced vibrations of the structures. They can be destroyed if the amplitude of vibration reaches a high enough value. One of the most popular ways to reduce these forced vibrations is to attach tuned mass damper (TMD) devices, which are commonly added at the maximum displacement point of the structures. This paper presents the computed results of the free vibration and the vibration response of the space frame system under an external random load, which is described as a stationary process with white noise. Static and dynamic equations are formed through the finite element method. In addition, this work also establishes artificial neural networks (ANNs) in order to predict the vibration response of the first frequencies of the structure. Numerical studies show that the data set of the TMD device strongly affects the first frequencies of the mechanical system, and the proposed artificial intelligence (AI) model can predict exactly the vibration response of the first frequencies of the structure. For the forced vibration problem, we can find optimal parameters of the TMD device and thus obtain minimum displacements of the structure. The results of this work can be used as a reference when applying this type of structure to TMD devices.

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Section: Artificial Neural Networkmentioning

confidence: 99%

New Numerical Results from Simulations of Beams and Space Frame Systems with a Tuned Mass Damper

Tho

Thom

2019

Materials

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“…Several statistical and numerical approaches have been utilised to predict and optimise various laser manufacturing processes including Artificial Neural Networks (ANN) [14]; genetic algorithms [15], design of experiments [3], finite elements analysis [16], ant colony optimisation [17], and fuzzy logic [18]. Due to their nonlinear, adaptive and learning ability using collected data, ANN models have been successfully applied to a large number of problems in several domain applications.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions

Karazi

Issa

Brabazon

2009

Optics and Lasers in Engineering

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This paper presents four models developed for the prediction of the width and depth dimensions of CO 2 laser formed micro-channels in glass. A 3 3 statistical design of experiments (DoE) model was built and conducted with the power (P), pulse repetition frequency (PRF), and traverse speed (U) of the laser machine as the selected parameters for investigation. Three feed-forward, back-propagation Artificial Neural Networks (ANNs) models were also generated. These ANN models were varied to investigate the influence of variations in the number and the selection of training data. Model A was constructed with 24 data randomly selected from the experimental results, leaving three data points for model testing; Model B was constructed with the eight corner points of the experimental data space, and seven other randomly selected data, leaving 12 data points for testing; and Model C was constructed with 15 randomly selected data leaving 12 data points for testing.These models were developed separately for both micro-channel width and depth prediction. These ANN models were constructed in LabVIEW coding. The performance of these ANN models and the DoE model were compared. When compared with the actual results two of the ANN models showed greater average percentage error than the DoE model. The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model.

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Nonlocal free vibration of functionally graded porous nanoplates using higher-order isogeometric analysis and ANN prediction

Pham

Tran

Nguyen

2023

Alexandria Engineering Journal

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Genetic algorithm for optimization design of diffractive optical elements in laser beam shaping

Cited by 8 publications

References 1 publication

New Numerical Results from Simulations of Beams and Space Frame Systems with a Tuned Mass Damper

New Numerical Results from Simulations of Beams and Space Frame Systems with a Tuned Mass Damper

Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions

Nonlocal free vibration of functionally graded porous nanoplates using higher-order isogeometric analysis and ANN prediction

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