In the present work, micro arc oxidation (MAO) coatings were synthesized on magnesium substrate employing 11 different electrolyte compositions containing systematically varied concentrations of sodium silicate (Na 2 SiO 3 ), potassium hydroxide (KOH), and sodium aluminate (NaAlO 2 ). The resultant coatings were subjected to coating thickness measurement, energy dispersive spectroscopy (EDS), scanning electron microscopy (SEM), image analysis, and three-dimensional (3-D) optical profilometry. The corrosion performance of the coatings was evaluated by conducting potentiodynamic polarization tests in 3.5 wt pct NaCl solution. The inter-relationships between the electrolyte chemistry and the resulting chemistry and porosity of the coating, on one hand, and with the aqueous corrosion behavior of the coating, on the other, were studied. The changes in pore morphology and pore distribution in the coatings were found to be significantly influenced by the electrolyte composition. The coatings can have either through-thickness pores or pores in the near surface region alone depending on the electrolyte composition. The deleterious role of KOH especially when its concentration is >20 pct of total electrolyte constituents promoting the formation of large and deep pores in the coating was demonstrated. A reasonable correlation indicating the increasing pore volume implying the increased corrosion was noticed.
The powder forging process of die forging, sintering, and upsetting is a convenient way of reducing or eliminating the porosity from traditional powder metallurgy products. Forging of metal powder enhances the demanding high-tensile, impact, and fatigue strength of powder metallurgy products. In this research, a demonstration system has been developed that employs a neural network for advising aluminium—iron composite compositions and optimum process settings with desired properties at an early stage in the design of the component. The input comprises the desired mechanical properties, such as formability index, and the system employs these data as inputs in order to recommend suitable metal powder compositions and process settings such as the particle size, percentage of iron content, preform density, aspect ratio, and compact load. The training data were collected by the experimental set-up in the laboratory for the sintered aluminium—iron composite preforms. Comparison of predicted and experimental data has confirmed the accuracy of the neural network approach; therefore, a new way for recommending suitable metal powder compositions and process settings is explored.
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