Modelling of Fatigue Failure for Plasma Coated Members Using Artificial Intelligence Technique

Coating materials in form of powder such as Magnesium Zirconate, Aluminum Bronze and Molybdenum were mixed in different portions and sprayed on steel specimen to find the fatigue properties of steel using plasma technique. The effect of coating mixture on the number of cycles needed for failure under different loads was done experimentally. A cyclic loading was applied to it repeatedly until failure occurs. The results were compared with those for the same specimen without coating. The results were then modelled using Artificial Intelligence Technique then optimized for maximum cycles of coated substance failure. The results showed significant improvement to the specimen’s resistance to failure with coating. Further, models were developed out of the experimental data and tested for accuracy and gave satisfactory results. However, the time consumed by the GA method was greater than that consumed by the same software for the ANN model development.Also, sensitivity analysis showed that the key effect for the variables studied was for the load while the least effect was for the Molybdenum mixture. On the other hand, using GA method, the importance of variables was maximum for the load and minimum for Magnesium oxide and Zirconate oxide mixture Further, using the correlation method, there was strong negative (i.e. inverse relationship) correlation between the number of cycles and load and weak with Magnesium oxide and Zirconate oxide mixture   while strong positive correlation was shown with Molybdenum and least positive for  Aluminum Copper Balance. Keywords: Artificial neural network, modeling, Plasma coating, fatigue failure

A Study of the Electrolyte Composition Influence on the Structure and Properties of MAO Coatings Formed on AMg6 Alloy

The influence of electrolysis conditions at different electrolyte compositions on the phase formation and properties of coatings obtained by microarc oxidation (MAO) on an aluminum alloy AMg6 was studied. For electrolysis, three types of electrolytes were used: alkaline electrolyte ((KOH) solution in distilled water), silicate electrolyte (with different percentages of Na2SiO3 component) and complex alkaline silicate electrolyte with liquid glass (1÷12 g/l Na2SiO3) and potassium hydroxide (1÷6 g/l KOH). An analysis of the results showed that the choice of electrolyte type and conditions of the microarc oxidation process allows a wide variation in the phase-structural state, thickness and properties of the AMg6 aluminum alloy. The criterion for the expected phase-structural state of the coatings as a result of microarc oxidation is the completeness of the γ–Al2O3→α–Al2O3 transformation process during coating formation. The use of an alkaline electrolyte does not allow achieving a high hardness of the coating due to the formation of the γ-Al2O3 phase and the absence of thermodynamic conditions for the γ–Al2O3→α–Al2O3 transition. When using a silicate electrolyte, it is possible to significantly increase the growth rate of the coating, but at the same time, the presence of a large specific Si concentration stimulates the formation of mullite and an amorphous-like phase. The use of a combined alkaline silicate electrolyte (with different percentages of KOH+Na2SiO3) with a low content (6 g/l) of Na2SiO3 in solution stimulates the formation of mullite. This is manifested to the greatest extent with the lowest content (1 g/l) of the KOH component. At a higher content (2 g/l) of the KOH component, the processes characteristic of an alkaline electrolyte become dominant. This leads to an incomplete transformation reaction and the formation of only the γ-Al2O3 phase. The achievement of the thermodynamic conditions of the γ–Al2O3→α–Al2O3 conversion became possible with an increase in the specific Na2SiO3 content in the electrolyte solution to 12 g/l. In this case, MAO coatings were formed on the AMg6 alloy with the highest hardness of 1500 kg/mm2 and high electric strength of 12 V/μ