Effect of the Electrolyte Temperature and the Current Density on a Layer Microhardness Generated by the Anodic Aluminium Oxidation

Spišák, Emil; Vagaská, Alena; Kmec, Ján; Fechová, Erika; Michal, Peter; Piteľ, Ján; Kučerka, Daniel

doi:10.1155/2015/659846

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…When the anodizing time increases, pore nuclei seem in the site of boundary nodes among the surface crystals. The surface crystals have higher chemical potential and crystal structure defects content [33]. The pore contents are crucial variable quantity in the wettability of a solid [34].…”

Section: Resultsmentioning

confidence: 99%

Experimental analysis of anodic coating process parameters of Al 5754 alloy by response surface method

Celik,

Erdemir,

Coruhlu

2023

Surf. Topogr.: Metrol. Prop.

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Aluminum 5754 alloy appears as a candidate material for many engineering applications in terms of its lightness and strength values. The surface properties of this alloy need to be improved for applications where there is surface damage such as friction and wear. In this study, Central composite design was applied to investigate the influences of anodic coating process parameters (voltage, coating time and grit size) on the response (i.e. surface roughness and coating thickness). The competence of the mathematical models recognized, and the importance of the regression coefficients were studied by ANOVA. The initial surfaces of the samples were sanded with 400, 800 and 1200 grit size and surfaces with 3 different roughnesses were obtained. Al 5754 specimens were anodic coated at 8V, 12V and 16V voltages and combinations of time parameters of 10, 20 and 30 minutes. The ANOVA results show that the designed models by RSM for average coating thickness and surface roughness are statistically important at the confidence level of 95%, and 80%, respectively. Maximum anodic coated layer of 29 µm was obtained at surface prepared with 1200 grit size, at 16V of voltage and used for 30 min of anodizing time. The lowest roughness value of 0.676 µm was obtained at the surface was prepared with 1200 grit size, 8V of voltage and anodizing time of 10 minutes.

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Section: Resultsmentioning

confidence: 99%

Experimental analysis of anodic coating process parameters of Al 5754 alloy by response surface method

Celik,

Erdemir,

Coruhlu

2023

Surf. Topogr.: Metrol. Prop.

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“…Moreover, our interest is supported by the 25 years of practical experience of one of our co-authors in the surface treatment field. Noticeable results are presented in [33][34][35].…”

Section: Introductionmentioning

confidence: 88%

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

2022

Self Cite

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Engineering optimization is the subject of interest for many scientific research teams on a global scale; it is a part of today’s mathematical modelling and control of processes and systems. The attention in this article is focused on optimization modelling of technological processes of surface treatment. To date, a multitude of articles are devoted to the applications of mathematical optimization methods to control technological processes, but the situation is different for surface treatment processes, especially for anodizing. We perceive their lack more, so this state has stimulated our interest, and the article contributes to filling the gap in scientific research in this area. The article deals with the application of non-linear programming (NLP) methods to optimise the process of anodic oxidation of aluminium using MATLAB toolboxes. The implementation of optimization methods is illustrated by solving a specific problem from engineering practice. The novelty of this article lies in the selection of effective approaches to the statement of optimal process conditions for anodizing. To solve this complex problem, a solving strategy based on the design of experiments approach (for five factors), exploratory data analysis, confirmatory analysis, and optimization modelling is proposed. The original results have been obtained through the experiment (performed by using the DOE approach), statistical analysis, and optimization procedure. The main contribution of this study is the developed mathematical-statistical computational (MSC) model predicting the thickness of the resulting aluminium anodic oxide layer (AOL). Based on the MSC model, the main goal has been achieved—the statement of optimal values of factors acting during the anodizing process to achieve the thickness of the protective layer required by clients, namely, for 5, 7, 10, and 15 [μm].

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“…It is an industry that consumes a lot of energy, 95% of which comes from electricity [1][2][3]. In order to improve the energy efficiency of the aluminium electrolysis production process, core parameters such as alumina concentration [4][5][6], anode effect [7][8][9], cell resistance [10,11], and electrolyte temperature [12,13] have been extensively and deeply studied using mechanism, soft sensor, and digital simulation methods.…”

Section: Introductionmentioning

confidence: 99%

Online Prediction Method of Molten Aluminium Height in Electrolytic Cell Based on Extreme Learning Machine with Kernel Function

Cao

Cui

et al. 2021

Mathematical Problems in Engineering

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An online prediction method of molten aluminium height is proposed based on extreme learning machine with kernel function (K-ELM). Firstly, relevant variables that can be measured online related to aluminium liquid fluctuations were obtained by analyzing the mechanism model of aluminium liquid fluctuations. Then, the online prediction method of molten aluminium height is proposed based on kernel function and ELM, which just use the anode-cathode voltage and the anode rod current data. Finally, the data collection and experiment of 3 sets of anode rods in the 200 kA series aluminium electrolytic cells are carried out on-site. The results show that the maximum absolute error is only 0.25 cm and relative error is less than 1.4%, which satisfied the production site requirements. Compared with existing methods, it has certain advantages in real-time and prediction accuracy and meets the real-time and accuracy requirements of the actual production process on-site.

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Effect of the Electrolyte Temperature and the Current Density on a Layer Microhardness Generated by the Anodic Aluminium Oxidation

Cited by 6 publications

References 19 publications

Experimental analysis of anodic coating process parameters of Al 5754 alloy by response surface method

Experimental analysis of anodic coating process parameters of Al 5754 alloy by response surface method

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

Online Prediction Method of Molten Aluminium Height in Electrolytic Cell Based on Extreme Learning Machine with Kernel Function

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