Peter Michal

Microhardness of the Coatings Created by Anodic Oxidation of Aluminum

Microhardness is a basic controlled quality parameter according to functional coatings, which is linked with a number of physico-chemical properties, such as coating strength, abrasion resistance, resistance to deformation, life. Life of the coating from a diagnostic point of view is influenced by chemical, physical, technological and material conditions of the process of creating film. The paper deals with analysis of the microhardness coating created by anodic oxidation of aluminum with a direct link to the chemical and physical factors controlled by the Design of Experiments methodology at constant anode current density 2 A.dm-2.

Statistical Analysis of the Factors Effect on the Zinc Coating Thickness

In order to improve the corrosion resistance of the material EN 355 the thickness of the zinc coating, deposited during the acid zinc plating process at a constant current density 1 A·dm-2, was monitored. The thickness of deposited coating has been investigated as the relationship of physical and chemical factors acting during the galvanic zinc plating, i.e. the electrolyte temperature, electrolyte composition (the amount of zinc, the amount of chloride and boric acid in the electrolyte), the plating time and the size of the voltage. Based on the mathematical-statistical analysis of the obtained data the suitable predicting model was developed for determining the thickness of deposited zinc coating reflecting the technological conditions of the acid zinc plating process.

The Influence of Sodium Chloride on the Resulting AAO Film Thickness

The contribution examines the effects of the electrolyte chemical composition on the resulting AAO layer thickness, which is one of the main indicators of corrosion protection of aluminium parts and which also favourably affects mechanical properties of component surfaces. For comparison purposes, there were selected the electrolytes comprising sulphuric acid, oxalic acid, boric acid and sodium chloride. Anodizing time for all specimens was 210.00 minutes. At the same time, equal electrolyte temperature 22.00°C ± 13.64% as well as equal magnitude of the applied voltage 12.00 V ± 4.17% were determined for all specimens. The results obtained lead to the assumption that it is possible to replace conventional electrolytes by those that are more environmentally friendly, reduce the costs of their disposal and allow obtaining oxide layers of the same thickness. The admixture of sodium chloride plays here the crucial role and its effect on the thickness of the formed oxide layer has not been published yet in any study.

Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodic oxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage) and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation. The paper also deals with current densities of 1 A·dm−2 and 3 A·dm−2 for creating aluminium oxide layer.

Experimental Study and Modeling of the Zinc Coating Thickness

The effects of six factors, affecting during the acid zinc plating process as its technological conditions, on the thickness of the resulting zinc coating has been examined. In order to control the quality of the resulting zinc coating deposited on the surface alloy EN 355 at a constant current density of 5 [Adm-2], the mathematical model predicting the thickness of deposited coating was developed using Design of Experiments (DoE) method. The obtained mathematical model describes the resulting deposited layer of zinc coating in dependence on the factor-level changes and combinations with the reliability of 58.75%.

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

The paper investigates the influence of the chemical composition and temperature of electrolyte, the oxidation time, voltage, and the current density on Vickers microhardness of aluminium oxide layers, at the same time. The layers were generated in the electrolytes with different concentrations of sulphuric and oxalic acids and surface current densities 1 A·dm−2, 3 A·dm−2, and 5 A·dm−2. The electrolyte temperature varied from −1.78°C to 45.78°C. The results have showed that while increasing the electrolyte temperature at the current density of 1 A·dm−2, the increase in the layer microhardness values is approximately by 66%. While simultaneously increasing the molar concentration of H2SO4 in the electrolyte, the growth rate of the microhardness value decreases. At the current density of 3 A·dm−2, by increasing the electrolyte temperature, a reduction in the microhardness of the generated layer occurs with the anodic oxidation time less than 25 min. The electrolyte temperature is not significant with the changing values of the layer microhardness at voltages less than 10.5 V.

Mathematical Modelling and Optimization of Technological Process Using Design of Experiments Methodology

The paper deals with statistical methods application to the evaluation of the relationships between the investigation range of input factors and response in longitudinal turning process. Our research was aimed at creation of the model of real situations of cutting conditions effects on the machined surface morphology applying longitudinal turning of steel C45 with specific values. Design of experiments (DoE) have increasingly had a wider application when creation mathematical and statistical models of technological processes. So the main part of the paper is to demonstrate the procedure of statistical processing of experimentally obtained data in order to create a prediction model and compare it with the theoretical calculation formulas.

Application of neural networks to evaluate experimental data of galvanic zincing

In order to improve corrosion resistance of alloy S355 EN 1025, the relationship between the thickness of zinc coating created during the process of acidic galvanic zincing and factors that influence this process were investigated. Influence of individual factors on thickness of zinc coating for sample area with surface current density of 3 A·dm-2 was determined by planned experiment which uses central composite plan. The obtained experimental data were evaluated based on neural network theory using cubic neural unit with Levenberg-Marquardt iterative adaptive algorithm. The influence of number of training data on the reliability of the obtained computational model has been studied. Furthermore, relationship between the amount of training data and reliability of prediction for the thickness of created zinc layer was observed. The relationship between input factors and thickness of layer coating with 88.37% reliability was reached.

SIMULATION OF TECHNOLOGICAL PROCESS BY USAGE NEURAL NETWORKS AND FACTORIAL DESIGN OF EXPERIMENTS

The possibilities of simulation of technological process ofaluminium anodic oxidation using the methodology of Design of Experiments (DOE) and theory of neural networks in order to monitor the anodizing process under various operating conditions are presented in this paper. The influence of chemical and physical input factors on the resulting AAO (anodic aluminium oxide) layer thickness at applied current density of 1 A x dm-2 and 6 A x dm-2 has been investigated. Based on the evaluation of experimentally obtained data, the computational predictive model describing the effect of individual input factors and their mutual interactions on the AAO layer thickness was developed in the form of cubic function. This model indicates which factors are important and how they combine to influence the response, it will enable us to optimize operating conditions. The most significant benefit of our research work in this field is the fact that all relevant factors were varied simultaneously.

Determination of Relationship between Chemical Composition of Electrolyte and Surface Sample Quality

Paper tracks experimentally confirmed relationship between chemical composition of electrolyte and resulting surface finish quality of created oxide layer during the process of anodic oxidation of aluminium. Examined chemical factors were: concentrations of sulphuric acid, oxalic acid, boric acid and sodium chloride. Aggressive effects of electrolyte were chosen as indicator of resulting layer quality – presence and extent of etching of used substrate sample.