Plasma current analysis using discrete wavelet transform during plasma electrolytic oxidation on aluminum

Sabouri, M.; Khoei, Seyed Mohammad Mousavi; Neshati, Jaber

doi:10.1016/j.jelechem.2017.03.035

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…Wavelet transforms can be divided into continuous and discrete types. Assumptions about the microplasma signal components can be made by relying on the orthogonal discrete wavelet transform (ODWT) [32]. This method is easy to implement digitally, as it only relies on a series of finite impulse response (FIR) filters.…”

Section: Wavelet Transformmentioning

confidence: 99%

Investigation of the frequency dependent spatio-temporal dynamics and controllability of microdischarges in unipolar pulsed plasma electrolytic oxidation

Hermanns¹,

Boeddeker²,

Bracht³

et al. 2020

J. Phys. D: Appl. Phys.

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The unipolar pulsed-plasma electrolytic oxidation (PEO) of aluminum has been replaced by bipolar pulsed methods that use a so-called ‘soft-sparking’mode. This method results in an effective reduction of intense microdischarges, which are detrimental to the oxide layer. In a previous publication, we developed an in-situ multivariable microdischarge control scheme using unipolar pulsing. Using this method, it is possible to restrict the mean microdischarge size to well-defined limits, while at the same time influencing the mean microdischarge energy, number density or spectral emission behaviour. This method operates well inside a frequency range of f = 1 − 20 k H z . Although this method shows highly desirable plasma control properties, the mechanisms defining this frequency-dependent controllability are unclear. The aim of this study is to visualize the spatio-temporal behavior of microdischarges in higher frequency ranges. First, a wavelet transform was performed to estimate the temporal evolution of microdischarge lifetimes. Ceramic coatings were then deposited on aluminum alloy substrates in an aqueous solution using unipolar pulsed galvanostatic PEO. The aluminum samples were coated for 30 min at frequencies of f 1 = 50 H z , f 2 = 5 k H z and f 3 = 100 k H z . High-speed imaging was carried out utilizing four synchronized intensified charge-coupled device (ICCD) cameras, each with a 500 ns exposure time. At f 2 = 5 k H z , the microdischarges were still able to follow the electrical pulses. In this regime, the process can be divided into two stages, an initial charging of the substrate surface without plasma emission and a subsequent slower evolution of microdischarges. Equivalent circuit model descriptions are given for both processes. At f 3 = 100 k H z , microdischarges were not able to follow the pulse frequency, as the lifetimes and risetimes of the microdischarge characteristics were longer than the pulse length. Reignition at the same spatial location, clustering and permanent ignition through pulse periods were observed.

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Section: Wavelet Transformmentioning

confidence: 99%

Investigation of the frequency dependent spatio-temporal dynamics and controllability of microdischarges in unipolar pulsed plasma electrolytic oxidation

Hermanns¹,

Boeddeker²,

Bracht³

et al. 2020

J. Phys. D: Appl. Phys.

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show abstract

“…[11,14,15] The experimental conditions employed in the PEO treatment allow the control of the morphology and microstructure of the oxide film, influencing other properties. [15][16][17] Additionally, the PEO condition can be obtained by different electrical stimulation, such as galvanostatic or potentiostatic (DC PEO), [18][19][20] alternate current, [21,22] pulsed potential/ current, [23,24] and hybrid [25,26] modes. The galvanostatic DC mode is handy for monitoring the stages of the oxide growth and mapping the system to comprehend its electrochemical behavior.…”

Section: Introductionmentioning

confidence: 99%

Alumina Coatings Containing Niobium Pentoxide Polymorphs Prepared by Plasma Electrolytic Oxidation of Aluminum

et al. 2023

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“…Jha and Senroy (2018) used the wavelet ridge method to analyze the dynamic characteristics of the power system. Sabouri et al (2017) adopted the orthogonal discrete wavelet transform (ODWT) to research the plasma electrolytic oxidation (PEO) of aluminum at various periods during the electrolysis process. A WNN has the potential to handle the issue of the AEMS modeling.…”

Section: Introductionmentioning

confidence: 99%

Establishing the energy consumption prediction model of aluminum electrolysis process by genetically optimizing wavelet neural network

et al. 2022

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Nowadays, it is very popular to employ genetic algorithm (GA) and its improved strategies to optimize neural networks (i.e., WNN) to solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, the traditional GA only focuses on restraining the infinite growth of the optimal species without reducing the similarity among the remaining excellent individuals when using the exclusion operator. Additionally, when performing arithmetic crossover or Cauchy mutation, a functional operator that conforms to the law of evolution is not constructed to generate proportional coefficients, which seriously restricted the exploitation of the hidden potential in genetic algorithms. To solve the above problems, this paper adopts three new methods to explore the performance enhancement of genetic algorithms (EGA). First, the mean Hamming distance (H-Mean) metric is designed to measure the spatial dispersion of individuals to alleviate selection pressure. Second, arithmetic crossover with transformation of the sigmoid-based function is developed to dynamically adjust the exchange proportion of offspring. Third, an adaptive scale coefficient is introduced into the Gauss-Cauchy mutation, which can regulate the mutation step size in real time and search accuracy for individuals in the population. Finally, the EGA solver is employed to deeply mine the optimal initial parameters of wavelet neural network (EGAWNN). Moreover, the paper provides the algorithm performance test, convergence analysis and significance test. The experimental results reveal that the EGAWNN model outperforms other relevant wavelet-based forecasting models, where the RMSE in test sets based on EGAWNN is 305.72 smaller than other seven algorithms.

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Plasma current analysis using discrete wavelet transform during plasma electrolytic oxidation on aluminum

Cited by 15 publications

References 17 publications

Investigation of the frequency dependent spatio-temporal dynamics and controllability of microdischarges in unipolar pulsed plasma electrolytic oxidation

Investigation of the frequency dependent spatio-temporal dynamics and controllability of microdischarges in unipolar pulsed plasma electrolytic oxidation

Alumina Coatings Containing Niobium Pentoxide Polymorphs Prepared by Plasma Electrolytic Oxidation of Aluminum

Establishing the energy consumption prediction model of aluminum electrolysis process by genetically optimizing wavelet neural network

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